1 Introduction
This document is intended to provide an overview of TMRC3 samples which have been sequenced. It includes some plots and analyses showing the relationships among the samples as well as some differential analyses when possible.
2 Annotation
We take the annotation data from ensembl’s biomart instance. The genome which was used to map the data was hg38 revision 91. My default when using biomart is to load the data from 1 year before the current date, which provides annotations which match hg38 91 almost perfectly.
hs_annot <- load_biomart_annotations()## The biomart annotations file already exists, loading from it.hs_annot <- hs_annot[["annotation"]]
hs_annot[["transcript"]] <- paste0(rownames(hs_annot), ".", hs_annot[["version"]])
rownames(hs_annot) <- make.names(hs_annot[["ensembl_gene_id"]], unique = TRUE)
tx_gene_map <- hs_annot[, c("transcript", "ensembl_gene_id")]hs_go <- load_biomart_go()[["go"]]## The biomart annotations file already exists, loading from it.hs_length <- hs_annot[, c("ensembl_gene_id", "cds_length")]
colnames(hs_length) <- c("ID", "length")3 Sample Estimation
I used two mapping methods for this data, hisat2 and salmon. Most analyses use hisat2, which is a more traditional map-and-count method. In contrast, salmon uses what may be thought of as a indexed voting method (so that multi-matches are discounted and the votes split among all matches). Salmon also required a pre-existing database of known transcripts (though later versions may actually use mapping from things like hisat), while hisat uses the genome and a database of known transcripts (and optionally can search for splicing junctions to find new transcripts).
3.1 Generate expressionsets
samplesheet <- "sample_sheets/tmrc3_samples_202103.xlsx"Caveat: This initial section is using salmon quantifications. A majority of analyses used hisat2.
3.1.1 Salmon expressionsets
Currently, I have these disabled.
3.1.2 Hisat2 expressionsets
The first thing to note is the large range in coverage. There are multiple samples with coverage which is too low to use. These will be removed shortly.
hs_expt <- create_expt(samplesheet,
file_column = "hg38100hisatfile",
savefile = glue::glue("hs_expt_all-v{ver}.rda"),
gene_info = hs_annot)## Reading the sample metadata.## Dropped 103 rows from the sample metadata because they were blank.## The sample definitions comprises: 131 rows(samples) and 81 columns(metadata fields).## Reading count tables.## Reading count files with read.table().## /mnt/sshfs_10186/cbcbsub00/fs/cbcb-lab/nelsayed/scratch/atb/rnaseq/lpanamensis_tmrc_2019/preprocessing/TMRC30001/outputs/hisat2_hg38_100/r1_trimmed.count_hg38_100_sno_gene_gene_id.count.xz contains 21486 rows.## preprocessing/TMRC30002/outputs/hisat2_hg38_100/r1_trimmed.count_hg38_100_sno_gene_gene_id.count.xz contains 21486 rows and merges to 21486 rows.## preprocessing/TMRC30003/outputs/hisat2_hg38_100/r1_trimmed.count_hg38_100_sno_gene_gene_id.count.xz contains 21486 rows and merges to 21486 rows.## preprocessing/TMRC30004/outputs/hisat2_hg38_100/r1_trimmed.count_hg38_100_sno_gene_gene_id.count.xz contains 21486 rows and merges to 21486 rows.## preprocessing/TMRC30005/outputs/hisat2_hg38_100/r1_trimmed.count_hg38_100_sno_gene_gene_id.count.xz contains 21486 rows and merges to 21486 rows.## preprocessing/TMRC30006/outputs/hisat2_hg38_100/r1_trimmed.count_hg38_100_sno_gene_gene_id.count.xz contains 21486 rows and merges to 21486 rows.## preprocessing/TMRC30007/outputs/hisat2_hg38_100/r1_trimmed.count_hg38_100_sno_gene_gene_id.count.xz contains 21486 rows and merges to 21486 rows.## preprocessing/TMRC30008/outputs/hisat2_hg38_100/r1_trimmed.count_hg38_100_sno_gene_gene_id.count.xz contains 21486 rows and merges to 21486 rows.## preprocessing/TMRC30009/outputs/hisat2_hg38_100/r1_trimmed.count_hg38_100_sno_gene_gene_id.count.xz contains 21486 rows and merges to 21486 rows.## preprocessing/TMRC30010/outputs/hisat2_hg38_100/r1_trimmed.count_hg38_100_sno_gene_gene_id.count.xz contains 21486 rows and merges to 21486 rows.## preprocessing/TMRC30015/outputs/hisat2_hg38_100/r1_trimmed.count_hg38_100_sno_gene_gene_id.count.xz contains 21486 rows and merges to 21486 rows.## preprocessing/TMRC30011/outputs/hisat2_hg38_100/r1_trimmed.count_hg38_100_sno_gene_gene_id.count.xz contains 21486 rows and merges to 21486 rows.## preprocessing/TMRC30012/outputs/hisat2_hg38_100/r1_trimmed.count_hg38_100_sno_gene_gene_id.count.xz contains 21486 rows and merges to 21486 rows.## preprocessing/TMRC30013/outputs/hisat2_hg38_100/r1_trimmed.count_hg38_100_sno_gene_gene_id.count.xz contains 21486 rows and merges to 21486 rows.## preprocessing/TMRC30016/outputs/hisat2_hg38_100/r1_trimmed.count_hg38_100_sno_gene_gene_id.count.xz contains 21486 rows and merges to 21486 rows.## preprocessing/TMRC30017/outputs/hisat2_hg38_100/r1_trimmed.count_hg38_100_sno_gene_gene_id.count.xz contains 21486 rows and merges to 21486 rows.## preprocessing/TMRC30050/outputs/hisat2_hg38_100/r1_trimmed.count_hg38_100_sno_gene_gene_id.count.xz contains 21486 rows and merges to 21486 rows.## preprocessing/TMRC30052/outputs/hisat2_hg38_100/r1_trimmed.count_hg38_100_sno_gene_gene_id.count.xz contains 21486 rows and merges to 21486 rows.## preprocessing/TMRC30071/outputs/hisat2_hg38_100/r1_trimmed.count_hg38_100_sno_gene_gene_id.count.xz contains 21486 rows and merges to 21486 rows.## preprocessing/TMRC30056/outputs/hisat2_hg38_100/r1_trimmed.count_hg38_100_sno_gene_gene_id.count.xz contains 21486 rows and merges to 21486 rows.## preprocessing/TMRC30058/outputs/hisat2_hg38_100/r1_trimmed.count_hg38_100_sno_gene_gene_id.count.xz contains 21486 rows and merges to 21486 rows.## preprocessing/TMRC30105/outputs/hisat2_hg38_100/r1_trimmed.count_hg38_100_sno_gene_gene_id.count.xz contains 21486 rows and merges to 21486 rows.## preprocessing/TMRC30094/outputs/hisat2_hg38_100/r1_trimmed.count_hg38_100_sno_gene_gene_id.count.xz contains 21486 rows and merges to 21486 rows.## preprocessing/TMRC30080/outputs/hisat2_hg38_100/r1_trimmed.count_hg38_100_sno_gene_gene_id.count.xz contains 21486 rows and merges to 21486 rows.## preprocessing/TMRC30103/outputs/hisat2_hg38_100/r1_trimmed.count_hg38_100_sno_gene_gene_id.count.xz contains 21486 rows and merges to 21486 rows.## preprocessing/TMRC30018/outputs/hisat2_hg38_100/r1_trimmed.count_hg38_100_sno_gene_gene_id.count.xz contains 21486 rows and merges to 21486 rows.## preprocessing/TMRC30107/outputs/hisat2_hg38_100/r1_trimmed.count_hg38_100_sno_gene_gene_id.count.xz contains 21486 rows and merges to 21486 rows.## preprocessing/TMRC30083/outputs/hisat2_hg38_100/r1_trimmed.count_hg38_100_sno_gene_gene_id.count.xz contains 21486 rows and merges to 21486 rows.## preprocessing/TMRC30019/outputs/hisat2_hg38_100/r1_trimmed.count_hg38_100_sno_gene_gene_id.count.xz contains 21486 rows and merges to 21486 rows.## preprocessing/TMRC30082/outputs/hisat2_hg38_100/r1_trimmed.count_hg38_100_sno_gene_gene_id.count.xz contains 21486 rows and merges to 21486 rows.## preprocessing/TMRC30093/outputs/hisat2_hg38_100/r1_trimmed.count_hg38_100_sno_gene_gene_id.count.xz contains 21486 rows and merges to 21486 rows.## preprocessing/TMRC30096/outputs/hisat2_hg38_100/r1_trimmed.count_hg38_100_sno_gene_gene_id.count.xz contains 21486 rows and merges to 21486 rows.## preprocessing/TMRC30014/outputs/hisat2_hg38_100/r1_trimmed.count_hg38_100_sno_gene_gene_id.count.xz contains 21486 rows and merges to 21486 rows.## preprocessing/TMRC30021/outputs/hisat2_hg38_100/r1_trimmed.count_hg38_100_sno_gene_gene_id.count.xz contains 21486 rows and merges to 21486 rows.## preprocessing/TMRC30029/outputs/hisat2_hg38_100/r1_trimmed.count_hg38_100_sno_gene_gene_id.count.xz contains 21486 rows and merges to 21486 rows.## preprocessing/TMRC30020/outputs/hisat2_hg38_100/r1_trimmed.count_hg38_100_sno_gene_gene_id.count.xz contains 21486 rows and merges to 21486 rows.## preprocessing/TMRC30038/outputs/hisat2_hg38_100/r1_trimmed.count_hg38_100_sno_gene_gene_id.count.xz contains 21486 rows and merges to 21486 rows.## preprocessing/TMRC30039/outputs/hisat2_hg38_100/r1_trimmed.count_hg38_100_sno_gene_gene_id.count.xz contains 21486 rows and merges to 21486 rows.## preprocessing/TMRC30023/outputs/hisat2_hg38_100/r1_trimmed.count_hg38_100_sno_gene_gene_id.count.xz contains 21486 rows and merges to 21486 rows.## preprocessing/TMRC30025/outputs/hisat2_hg38_100/r1_trimmed.count_hg38_100_sno_gene_gene_id.count.xz contains 21486 rows and merges to 21486 rows.## preprocessing/TMRC30022/outputs/hisat2_hg38_100/r1_trimmed.count_hg38_100_sno_gene_gene_id.count.xz contains 21486 rows and merges to 21486 rows.## preprocessing/TMRC30046/outputs/hisat2_hg38_100/r1_trimmed.count_hg38_100_sno_gene_gene_id.count.xz contains 21486 rows and merges to 21486 rows.## preprocessing/TMRC30047/outputs/hisat2_hg38_100/r1_trimmed.count_hg38_100_sno_gene_gene_id.count.xz contains 21486 rows and merges to 21486 rows.## preprocessing/TMRC30048/outputs/hisat2_hg38_100/r1_trimmed.count_hg38_100_sno_gene_gene_id.count.xz contains 21486 rows and merges to 21486 rows.## preprocessing/TMRC30026/outputs/hisat2_hg38_100/r1_trimmed.count_hg38_100_sno_gene_gene_id.count.xz contains 21486 rows and merges to 21486 rows.## preprocessing/TMRC30030/outputs/hisat2_hg38_100/r1_trimmed.count_hg38_100_sno_gene_gene_id.count.xz contains 21486 rows and merges to 21486 rows.## preprocessing/TMRC30031/outputs/hisat2_hg38_100/r1_trimmed.count_hg38_100_sno_gene_gene_id.count.xz contains 21486 rows and merges to 21486 rows.## preprocessing/TMRC30032/outputs/hisat2_hg38_100/r1_trimmed.count_hg38_100_sno_gene_gene_id.count.xz contains 21486 rows and merges to 21486 rows.## preprocessing/TMRC30024/outputs/hisat2_hg38_100/r1_trimmed.count_hg38_100_sno_gene_gene_id.count.xz contains 21486 rows and merges to 21486 rows.## preprocessing/TMRC30040/outputs/hisat2_hg38_100/r1_trimmed.count_hg38_100_sno_gene_gene_id.count.xz contains 21486 rows and merges to 21486 rows.## preprocessing/TMRC30033/outputs/hisat2_hg38_100/r1_trimmed.count_hg38_100_sno_gene_gene_id.count.xz contains 21486 rows and merges to 21486 rows.## preprocessing/TMRC30049/outputs/hisat2_hg38_100/r1_trimmed.count_hg38_100_sno_gene_gene_id.count.xz contains 21486 rows and merges to 21486 rows.## preprocessing/TMRC30053/outputs/hisat2_hg38_100/r1_trimmed.count_hg38_100_sno_gene_gene_id.count.xz contains 21486 rows and merges to 21486 rows.## preprocessing/TMRC30054/outputs/hisat2_hg38_100/r1_trimmed.count_hg38_100_sno_gene_gene_id.count.xz contains 21486 rows and merges to 21486 rows.## preprocessing/TMRC30037/outputs/hisat2_hg38_100/r1_trimmed.count_hg38_100_sno_gene_gene_id.count.xz contains 21486 rows and merges to 21486 rows.## preprocessing/TMRC30027/outputs/hisat2_hg38_100/r1_trimmed.count_hg38_100_sno_gene_gene_id.count.xz contains 21486 rows and merges to 21486 rows.## preprocessing/TMRC30028/outputs/hisat2_hg38_100/r1_trimmed.count_hg38_100_sno_gene_gene_id.count.xz contains 21486 rows and merges to 21486 rows.## preprocessing/TMRC30034/outputs/hisat2_hg38_100/r1_trimmed.count_hg38_100_sno_gene_gene_id.count.xz contains 21486 rows and merges to 21486 rows.## preprocessing/TMRC30035/outputs/hisat2_hg38_100/r1_trimmed.count_hg38_100_sno_gene_gene_id.count.xz contains 21486 rows and merges to 21486 rows.## preprocessing/TMRC30036/outputs/hisat2_hg38_100/r1_trimmed.count_hg38_100_sno_gene_gene_id.count.xz contains 21486 rows and merges to 21486 rows.## preprocessing/TMRC30044/outputs/hisat2_hg38_100/r1_trimmed.count_hg38_100_sno_gene_gene_id.count.xz contains 21486 rows and merges to 21486 rows.## preprocessing/TMRC30055/outputs/hisat2_hg38_100/r1_trimmed.count_hg38_100_sno_gene_gene_id.count.xz contains 21486 rows and merges to 21486 rows.## preprocessing/TMRC30068/outputs/hisat2_hg38_100/r1_trimmed.count_hg38_100_sno_gene_gene_id.count.xz contains 21486 rows and merges to 21486 rows.## preprocessing/TMRC30070/outputs/hisat2_hg38_100/r1_trimmed.count_hg38_100_sno_gene_gene_id.count.xz contains 21486 rows and merges to 21486 rows.## preprocessing/TMRC30041/outputs/hisat2_hg38_100/r1_trimmed.count_hg38_100_sno_gene_gene_id.count.xz contains 21486 rows and merges to 21486 rows.## preprocessing/TMRC30042/outputs/hisat2_hg38_100/r1_trimmed.count_hg38_100_sno_gene_gene_id.count.xz contains 21486 rows and merges to 21486 rows.## preprocessing/TMRC30043/outputs/hisat2_hg38_100/r1_trimmed.count_hg38_100_sno_gene_gene_id.count.xz contains 21486 rows and merges to 21486 rows.## preprocessing/TMRC30045/outputs/hisat2_hg38_100/r1_trimmed.count_hg38_100_sno_gene_gene_id.count.xz contains 21486 rows and merges to 21486 rows.## preprocessing/TMRC30059/outputs/hisat2_hg38_100/r1_trimmed.count_hg38_100_sno_gene_gene_id.count.xz contains 21486 rows and merges to 21486 rows.## preprocessing/TMRC30060/outputs/hisat2_hg38_100/r1_trimmed.count_hg38_100_sno_gene_gene_id.count.xz contains 21486 rows and merges to 21486 rows.## preprocessing/TMRC30061/outputs/hisat2_hg38_100/r1_trimmed.count_hg38_100_sno_gene_gene_id.count.xz contains 21486 rows and merges to 21486 rows.## preprocessing/TMRC30062/outputs/hisat2_hg38_100/r1_trimmed.count_hg38_100_sno_gene_gene_id.count.xz contains 21486 rows and merges to 21486 rows.## preprocessing/TMRC30063/outputs/hisat2_hg38_100/r1_trimmed.count_hg38_100_sno_gene_gene_id.count.xz contains 21486 rows and merges to 21486 rows.## preprocessing/TMRC30051/outputs/hisat2_hg38_100/r1_trimmed.count_hg38_100_sno_gene_gene_id.count.xz contains 21486 rows and merges to 21486 rows.## preprocessing/TMRC30064/outputs/hisat2_hg38_100/r1_trimmed.count_hg38_100_sno_gene_gene_id.count.xz contains 21486 rows and merges to 21486 rows.## preprocessing/TMRC30065/outputs/hisat2_hg38_100/r1_trimmed.count_hg38_100_sno_gene_gene_id.count.xz contains 21486 rows and merges to 21486 rows.## preprocessing/TMRC30066/outputs/hisat2_hg38_100/r1_trimmed.count_hg38_100_sno_gene_gene_id.count.xz contains 21486 rows and merges to 21486 rows.## preprocessing/TMRC30067/outputs/hisat2_hg38_100/r1_trimmed.count_hg38_100_sno_gene_gene_id.count.xz contains 21486 rows and merges to 21486 rows.## preprocessing/TMRC30057/outputs/hisat2_hg38_100/r1_trimmed.count_hg38_100_sno_gene_gene_id.count.xz contains 21486 rows and merges to 21486 rows.## preprocessing/TMRC30069/outputs/hisat2_hg38_100/r1_trimmed.count_hg38_100_sno_gene_gene_id.count.xz contains 21486 rows and merges to 21486 rows.## preprocessing/TMRC30074/outputs/hisat2_hg38_100/r1_trimmed.count_hg38_100_sno_gene_gene_id.count.xz contains 21486 rows and merges to 21486 rows.## preprocessing/TMRC30072/outputs/hisat2_hg38_100/r1_trimmed.count_hg38_100_sno_gene_gene_id.count.xz contains 21486 rows and merges to 21486 rows.## preprocessing/TMRC30076/outputs/hisat2_hg38_100/r1_trimmed.count_hg38_100_sno_gene_gene_id.count.xz contains 21486 rows and merges to 21486 rows.## preprocessing/TMRC30077/outputs/hisat2_hg38_100/r1_trimmed.count_hg38_100_sno_gene_gene_id.count.xz contains 21486 rows and merges to 21486 rows.## preprocessing/TMRC30078/outputs/hisat2_hg38_100/r1_trimmed.count_hg38_100_sno_gene_gene_id.count.xz contains 21486 rows and merges to 21486 rows.## preprocessing/TMRC30088/outputs/hisat2_hg38_100/r1_trimmed.count_hg38_100_sno_gene_gene_id.count.xz contains 21486 rows and merges to 21486 rows.## preprocessing/TMRC30079/outputs/hisat2_hg38_100/r1_trimmed.count_hg38_100_sno_gene_gene_id.count.xz contains 21486 rows and merges to 21486 rows.## preprocessing/TMRC30097/outputs/hisat2_hg38_100/r1_trimmed.count_hg38_100_sno_gene_gene_id.count.xz contains 21486 rows and merges to 21486 rows.## preprocessing/TMRC30075/outputs/hisat2_hg38_100/r1_trimmed.count_hg38_100_sno_gene_gene_id.count.xz contains 21486 rows and merges to 21486 rows.## preprocessing/TMRC30085/outputs/hisat2_hg38_100/r1_trimmed.count_hg38_100_sno_gene_gene_id.count.xz contains 21486 rows and merges to 21486 rows.## preprocessing/TMRC30086/outputs/hisat2_hg38_100/r1_trimmed.count_hg38_100_sno_gene_gene_id.count.xz contains 21486 rows and merges to 21486 rows.## preprocessing/TMRC30087/outputs/hisat2_hg38_100/r1_trimmed.count_hg38_100_sno_gene_gene_id.count.xz contains 21486 rows and merges to 21486 rows.## preprocessing/TMRC30101/outputs/hisat2_hg38_100/r1_trimmed.count_hg38_100_sno_gene_gene_id.count.xz contains 21486 rows and merges to 21486 rows.## preprocessing/TMRC30089/outputs/hisat2_hg38_100/r1_trimmed.count_hg38_100_sno_gene_gene_id.count.xz contains 21486 rows and merges to 21486 rows.## preprocessing/TMRC30090/outputs/hisat2_hg38_100/r1_trimmed.count_hg38_100_sno_gene_gene_id.count.xz contains 21486 rows and merges to 21486 rows.## preprocessing/TMRC30081/outputs/hisat2_hg38_100/r1_trimmed.count_hg38_100_sno_gene_gene_id.count.xz contains 21486 rows and merges to 21486 rows.## preprocessing/TMRC30092/outputs/hisat2_hg38_100/r1_trimmed.count_hg38_100_sno_gene_gene_id.count.xz contains 21486 rows and merges to 21486 rows.## preprocessing/TMRC30104/outputs/hisat2_hg38_100/r1_trimmed.count_hg38_100_sno_gene_gene_id.count.xz contains 21486 rows and merges to 21486 rows.## preprocessing/TMRC30106/outputs/hisat2_hg38_100/r1_trimmed.count_hg38_100_sno_gene_gene_id.count.xz contains 21486 rows and merges to 21486 rows.## preprocessing/TMRC30095/outputs/hisat2_hg38_100/r1_trimmed.count_hg38_100_sno_gene_gene_id.count.xz contains 21486 rows and merges to 21486 rows.## preprocessing/TMRC30108/outputs/hisat2_hg38_100/r1_trimmed.count_hg38_100_sno_gene_gene_id.count.xz contains 21486 rows and merges to 21486 rows.## preprocessing/TMRC30084/outputs/hisat2_hg38_100/r1_trimmed.count_hg38_100_sno_gene_gene_id.count.xz contains 21486 rows and merges to 21486 rows.## preprocessing/TMRC30098/outputs/hisat2_hg38_100/r1_trimmed.count_hg38_100_sno_gene_gene_id.count.xz contains 21486 rows and merges to 21486 rows.## preprocessing/TMRC30099/outputs/hisat2_hg38_100/r1_trimmed.count_hg38_100_sno_gene_gene_id.count.xz contains 21486 rows and merges to 21486 rows.## preprocessing/TMRC30100/outputs/hisat2_hg38_100/r1_trimmed.count_hg38_100_sno_gene_gene_id.count.xz contains 21486 rows and merges to 21486 rows.## preprocessing/TMRC30102/outputs/hisat2_hg38_100/r1_trimmed.count_hg38_100_sno_gene_gene_id.count.xz contains 21486 rows and merges to 21486 rows.## preprocessing/TMRC30091/outputs/hisat2_hg38_100/r1_trimmed.count_hg38_100_sno_gene_gene_id.count.xz contains 21486 rows and merges to 21486 rows.## preprocessing/TMRC30073/outputs/hisat2_hg38_100/r1_trimmed.count_hg38_100_sno_gene_gene_id.count.xz contains 21486 rows and merges to 21486 rows.## Finished reading count data.## Warning in create_expt(samplesheet, file_column = "hg38100hisatfile", savefile
## = glue::glue("hs_expt_all-v{ver}.rda"), : Some samples were removed when cross
## referencing the samples against the count data.## Matched 21440 annotations and counts.## Bringing together the count matrix and gene information.## Some annotations were lost in merging, setting them to 'undefined'.## The final expressionset has 21481 rows and 108 columns.hs_expt <- exclude_genes_expt(hs_expt, column = "gene_biotype",
method = "keep", pattern = "protein_coding")## Before removal, there were 21481 entries.## Now there are 19928 entries.## Percent kept: 99.600, 99.714, 99.759, 99.397, 99.540, 99.552, 99.928, 99.876, 99.938, 99.867, 79.229, 99.946, 99.860, 99.873, 92.600, 85.708, 99.880, 99.952, 99.900, 99.852, 99.961, 99.862, 99.946, 99.845, 99.954, 98.239, 99.837, 99.951, 89.738, 99.830, 99.951, 99.843, 99.855, 99.953, 99.915, 91.997, 99.882, 99.933, 99.863, 94.990, 92.874, 99.874, 99.948, 99.904, 97.076, 99.876, 99.955, 99.900, 99.881, 99.927, 99.801, 99.879, 99.953, 99.893, 99.899, 99.947, 99.908, 99.888, 99.941, 99.782, 80.330, 99.891, 99.941, 99.926, 99.905, 99.948, 99.883, 73.317, 99.812, 99.904, 99.865, 99.913, 99.888, 99.924, 99.847, 99.898, 99.904, 99.869, 99.895, 99.928, 99.811, 99.891, 99.935, 99.830, 99.894, 99.944, 99.811, 89.882, 86.953, 92.560, 93.354, 83.616, 88.383, 94.220, 91.439, 93.996, 92.838, 80.268, 95.899, 93.052, 93.651, 95.931, 90.869, 98.763, 98.397, 97.735, 99.166, 89.231## Percent removed: 0.400, 0.286, 0.241, 0.603, 0.460, 0.448, 0.072, 0.124, 0.062, 0.133, 20.771, 0.054, 0.140, 0.127, 7.400, 14.292, 0.120, 0.048, 0.100, 0.148, 0.039, 0.138, 0.054, 0.155, 0.046, 1.761, 0.163, 0.049, 10.262, 0.170, 0.049, 0.157, 0.145, 0.047, 0.085, 8.003, 0.118, 0.067, 0.137, 5.010, 7.126, 0.126, 0.052, 0.096, 2.924, 0.124, 0.045, 0.100, 0.119, 0.073, 0.199, 0.121, 0.047, 0.107, 0.101, 0.053, 0.092, 0.112, 0.059, 0.218, 19.670, 0.109, 0.059, 0.074, 0.095, 0.052, 0.117, 26.683, 0.188, 0.096, 0.135, 0.087, 0.112, 0.076, 0.153, 0.102, 0.096, 0.131, 0.105, 0.072, 0.189, 0.109, 0.065, 0.170, 0.106, 0.056, 0.189, 10.118, 13.047, 7.440, 6.646, 16.384, 11.617, 5.780, 8.561, 6.004, 7.162, 19.732, 4.101, 6.948, 6.349, 4.069, 9.131, 1.237, 1.603, 2.265, 0.834, 10.769libsizes <- plot_libsize(hs_expt)## The scale difference between the smallest and largest
## libraries is > 10. Assuming a log10 scale is better, set scale = FALSE if not.libsizes$plot
nonzero <- plot_nonzero(hs_expt)
nonzero$plot## Warning: ggrepel: 90 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
box <- plot_boxplot(hs_expt)## This data will benefit from being displayed on the log scale.## If this is not desired, set scale='raw'## Some entries are 0. We are on log scale, adding 1 to the data.## Changed 520052 zero count features.box
3.2 Minimum coverage sample filtering
I arbitrarily chose 3,000,000 counts as a minimal level of coverage. We may want this to be higher.
hs_valid <- subset_expt(hs_expt, coverage = 3000000)## Subsetting given a minimal number of counts/sample.## There were 108, now there are 104 samples.plot_libsize(hs_valid)$plot## The scale difference between the smallest and largest
## libraries is > 10. Assuming a log10 scale is better, set scale = FALSE if not.valid_write <- sm(write_expt(hs_valid, excel = glue("excel/hs_valid-v{ver}.xlsx")))4 Questions from Maria Adelaida
The following comes from an email 20190830 from Maria Adelaida.
Samples WT1010 and WT1011 PBMCs from two healthy donors processed 2h, 7h and 12h after sample procurement. This is an analysis to explore the time-effect on gene expression and define steps for data analysis for patient samples considering time-dependent effects.
- An initial PCA on the raw data would be very useful to see if there is clustering based on time or (as usual), mostly a donor-specific effect. Then I think a hierarchical clustering of genes based on time-dependent modifications to see what is mostly affected (if any) - like what you guys did for T.cruzi.
Samples from SU1017, SU1034 Samples from TMRC CL patients. m= monocyte, n= neutrophil. Samples labeled “1” are taken before treatment and those “2” mid way through treatment. This is exiting, because these will be our first neutrophil transcriptomes.
In an attempt to poke at these questions, I mapped the reads to hg38_91 using salmon and hisat2. It is very noteworthy that the salmon mappings are exhibiting some serious problems and should be looked into further. The hisat2 mappings are significantly more ‘normal’. Having said that, two samples remain basically unusable: tmrc30009 (1034n1) and (to a smaller degree) tmrc30007 (1017n1) have too few reads as shown above.
4.1 Preparation
To address these, I added to the end of the sample sheet columns named ‘condition’, ‘batch’, ‘donor’, and ‘time’. These are filled in with shorthand values according to the above.
4.2 Global view
Before addressing the questions explicitly by subsetting the data, I want to get a look at the samples as they are.
hs_valid <- set_expt_batches(hs_valid, fact = "donor")
hs_valid <- set_expt_samplenames(hs_valid, newnames = pData(hs_valid)[["samplename"]])
all_norm <- normalize_expt(hs_valid, convert = "cpm", filter = TRUE)## This function will replace the expt$expressionset slot with:## cpm(cbcb(data))## It will save copies of each step along the way
## in expt$normalized with the corresponding libsizes. Keep libsizes in mind
## when invoking limma. The appropriate libsize is non-log(cpm(normalized)).
## This is most likely kept at:
## 'new_expt$normalized$intermediate_counts$normalization$libsizes'
## A copy of this may also be found at:
## new_expt$best_libsize## Leaving the data in its current base format, keep in mind that
## some metrics are easier to see when the data is log2 transformed, but
## EdgeR/DESeq do not accept transformed data.## Leaving the data unnormalized. This is necessary for DESeq, but
## EdgeR/limma might benefit from normalization. Good choices include quantile,
## size-factor, tmm, etc.## Not correcting the count-data for batch effects. If batch is
## included in EdgerR/limma's model, then this is probably wise; but in extreme
## batch effects this is a good parameter to play with.## Step 1: performing count filter with option: cbcb## Removing 5276 low-count genes (14652 remaining).## Step 2: not normalizing the data.## Step 3: converting the data with cpm.## The method is: raw.## Step 4: not doing batch correction.## Step 4: not transforming the data.all_norm <- normalize_expt(all_norm, transform = "log2")## This function will replace the expt$expressionset slot with:## log2(data)## It will save copies of each step along the way
## in expt$normalized with the corresponding libsizes. Keep libsizes in mind
## when invoking limma. The appropriate libsize is non-log(cpm(normalized)).
## This is most likely kept at:
## 'new_expt$normalized$intermediate_counts$normalization$libsizes'
## A copy of this may also be found at:
## new_expt$best_libsize## Filter is false, this should likely be set to something, good
## choices include cbcb, kofa, pofa (anything but FALSE). If you want this to
## stay FALSE, keep in mind that if other normalizations are performed, then the
## resulting libsizes are likely to be strange (potentially negative!)## Leaving the data unconverted. It is often advisable to cpm/rpkm
## the data to normalize for sampling differences, keep in mind though that rpkm
## has some annoying biases, and voom() by default does a cpm (though hpgl_voom()
## will try to detect this).## Leaving the data unnormalized. This is necessary for DESeq, but
## EdgeR/limma might benefit from normalization. Good choices include quantile,
## size-factor, tmm, etc.## Not correcting the count-data for batch effects. If batch is
## included in EdgerR/limma's model, then this is probably wise; but in extreme
## batch effects this is a good parameter to play with.## Step 1: not doing count filtering.## Step 2: not normalizing the data.## Step 3: not converting the data.## The method is: raw.## Step 4: not doing batch correction.## Step 4: transforming the data with log2.## transform_counts: Found 89539 values equal to 0, adding 1 to the matrix.plt <- plot_pca(all_norm, plot_labels = FALSE)$plot## Potentially check over the experimental design, there appear to be missing values.## Warning in plot_pca(all_norm, plot_labels = FALSE): There are NA values in the
## component data. This can lead to weird plotting errors.## Not putting labels on the PC plot.pp(file = glue("images/tmrc3_pca_nolabels-v{ver}.pdf"), image = plt)## Writing the image to: images/tmrc3_pca_nolabels-v202103.pdf and calling dev.off().## Warning in MASS::cov.trob(data[, vars]): Probable convergence failure## Warning in MASS::cov.trob(data[, vars]): Probable convergence failure## Warning: Removed 21 rows containing missing values (geom_point).## Warning in MASS::cov.trob(data[, vars]): Probable convergence failure
## Warning in MASS::cov.trob(data[, vars]): Probable convergence failure## Warning: Removed 21 rows containing missing values (geom_point).
all_ts <- plot_tsne(all_norm)## Potentially check over the experimental design, there appear to be missing values.## Warning in plot_pca(..., pc_method = "tsne"): There are NA values in the
## component data. This can lead to weird plotting errors.## plot labels was not set and there are more than 100 samples, disabling it.## Not putting labels on the PC plot.all_ts$plot## Warning in MASS::cov.trob(data[, vars]): Probable convergence failure## Warning in MASS::cov.trob(data[, vars]): Probable convergence failure## Warning: Removed 21 rows containing missing values (geom_point).
knitr::kable(all_pca$table)## Error in knitr::kable(all_pca$table): object 'all_pca' not foundwrite.csv(all_pca$table, file = "coords/hs_donor_pca_coords.csv")## Error in is.data.frame(x): object 'all_pca' not foundplot_corheat(all_norm)$plot
plot_topn(hs_valid)$plot## `geom_smooth()` using formula 'y ~ x'
4.3 All celltypes clinical outcome
hs_clinical <- subset_expt(hs_valid, subset = "condition!='PBMC'&condition!='Macrophage'")## Using a subset expression.## There were 104, now there are 86 samples.hs_clinical <- set_expt_conditions(hs_clinical, fact = "clinicaloutcome")
hs_clinical <- set_expt_batches(hs_clinical, fact = "typeofcells")
chosen_colors <- c("#D95F02", "#7570B3", "#1B9E77", "#FF0000")
names(chosen_colors) <- c("cure", "failure", "lost", "null")
hs_clinical <- set_expt_colors(expt = hs_clinical, colors = chosen_colors)## The new colors are a character, changing according to condition.hs_clinical <- set_expt_samplenames(expt = hs_clinical, newnames = pData(hs_clinical)$samplename)
hs_clinical_norm <- normalize_expt(hs_clinical, filter = TRUE, convert = "cpm", norm = "quant")## This function will replace the expt$expressionset slot with:## cpm(quant(cbcb(data)))## It will save copies of each step along the way
## in expt$normalized with the corresponding libsizes. Keep libsizes in mind
## when invoking limma. The appropriate libsize is non-log(cpm(normalized)).
## This is most likely kept at:
## 'new_expt$normalized$intermediate_counts$normalization$libsizes'
## A copy of this may also be found at:
## new_expt$best_libsize## Leaving the data in its current base format, keep in mind that
## some metrics are easier to see when the data is log2 transformed, but
## EdgeR/DESeq do not accept transformed data.## Not correcting the count-data for batch effects. If batch is
## included in EdgerR/limma's model, then this is probably wise; but in extreme
## batch effects this is a good parameter to play with.## Step 1: performing count filter with option: cbcb## Removing 5472 low-count genes (14456 remaining).## Step 2: normalizing the data with quant.## Step 3: converting the data with cpm.## The method is: raw.## Step 4: not doing batch correction.## Step 4: not transforming the data.hs_clinical_norm <- normalize_expt(hs_clinical, transform = "log2")## This function will replace the expt$expressionset slot with:## log2(data)## It will save copies of each step along the way
## in expt$normalized with the corresponding libsizes. Keep libsizes in mind
## when invoking limma. The appropriate libsize is non-log(cpm(normalized)).
## This is most likely kept at:
## 'new_expt$normalized$intermediate_counts$normalization$libsizes'
## A copy of this may also be found at:
## new_expt$best_libsize## Filter is false, this should likely be set to something, good
## choices include cbcb, kofa, pofa (anything but FALSE). If you want this to
## stay FALSE, keep in mind that if other normalizations are performed, then the
## resulting libsizes are likely to be strange (potentially negative!)## Leaving the data unconverted. It is often advisable to cpm/rpkm
## the data to normalize for sampling differences, keep in mind though that rpkm
## has some annoying biases, and voom() by default does a cpm (though hpgl_voom()
## will try to detect this).## Leaving the data unnormalized. This is necessary for DESeq, but
## EdgeR/limma might benefit from normalization. Good choices include quantile,
## size-factor, tmm, etc.## Not correcting the count-data for batch effects. If batch is
## included in EdgerR/limma's model, then this is probably wise; but in extreme
## batch effects this is a good parameter to play with.## Step 1: not doing count filtering.## Step 2: not normalizing the data.## Step 3: not converting the data.## The method is: raw.## Step 4: not doing batch correction.## Step 4: transforming the data with log2.## transform_counts: Found 399372 values equal to 0, adding 1 to the matrix.##plt <- plot_pca(hs_clinical_norm, plot_labels = FALSE, size_column = "visitnumber")$plot
plt <- plot_pca(hs_clinical_norm, plot_labels = FALSE)$plot## Not putting labels on the PC plot.pp(file = glue("images/all_clinical_nobatch_pca-v{ver}.png"), image = plt, heigh = 8, width = 20)## Writing the image to: images/all_clinical_nobatch_pca-v202103.png and calling dev.off().
plt <- plot_pca(hs_clinical_norm, plot_labels = FALSE, size_column = "visitnumber")$plot## Not putting labels on the PC plot.pp(file = glue("images/all_clinical_nobatch_pca_sized-v{ver}.png"), image = plt, heigh = 8, width = 20)## Writing the image to: images/all_clinical_nobatch_pca_sized-v202103.png and calling dev.off().
plt <- plot_pca(hs_clinical_norm)$plot
pp(file = glue("images/all_clinical_nobatch_pca_labeled-v{ver}.png"), height = 8, width = 20, image = plt)## Writing the image to: images/all_clinical_nobatch_pca_labeled-v202103.png and calling dev.off().## Warning: ggrepel: 1 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps## Warning: ggrepel: 10 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
5 Repeat without Biopsies
hs_clinic <- subset_expt(hs_clinical, subset = "batch!='Biopsy'")## Using a subset expression.## There were 86, now there are 54 samples.hs_clinic_norm <- normalize_expt(hs_clinic, filter = TRUE, convert = "cpm", norm = "quant")## This function will replace the expt$expressionset slot with:## cpm(quant(cbcb(data)))## It will save copies of each step along the way
## in expt$normalized with the corresponding libsizes. Keep libsizes in mind
## when invoking limma. The appropriate libsize is non-log(cpm(normalized)).
## This is most likely kept at:
## 'new_expt$normalized$intermediate_counts$normalization$libsizes'
## A copy of this may also be found at:
## new_expt$best_libsize## Leaving the data in its current base format, keep in mind that
## some metrics are easier to see when the data is log2 transformed, but
## EdgeR/DESeq do not accept transformed data.## Not correcting the count-data for batch effects. If batch is
## included in EdgerR/limma's model, then this is probably wise; but in extreme
## batch effects this is a good parameter to play with.## Step 1: performing count filter with option: cbcb## Removing 8038 low-count genes (11890 remaining).## Step 2: normalizing the data with quant.## Step 3: converting the data with cpm.## The method is: raw.## Step 4: not doing batch correction.## Step 4: not transforming the data.hs_clinic_norm <- normalize_expt(hs_clinic, transform = "log2")## This function will replace the expt$expressionset slot with:## log2(data)## It will save copies of each step along the way
## in expt$normalized with the corresponding libsizes. Keep libsizes in mind
## when invoking limma. The appropriate libsize is non-log(cpm(normalized)).
## This is most likely kept at:
## 'new_expt$normalized$intermediate_counts$normalization$libsizes'
## A copy of this may also be found at:
## new_expt$best_libsize## Filter is false, this should likely be set to something, good
## choices include cbcb, kofa, pofa (anything but FALSE). If you want this to
## stay FALSE, keep in mind that if other normalizations are performed, then the
## resulting libsizes are likely to be strange (potentially negative!)## Leaving the data unconverted. It is often advisable to cpm/rpkm
## the data to normalize for sampling differences, keep in mind though that rpkm
## has some annoying biases, and voom() by default does a cpm (though hpgl_voom()
## will try to detect this).## Leaving the data unnormalized. This is necessary for DESeq, but
## EdgeR/limma might benefit from normalization. Good choices include quantile,
## size-factor, tmm, etc.## Not correcting the count-data for batch effects. If batch is
## included in EdgerR/limma's model, then this is probably wise; but in extreme
## batch effects this is a good parameter to play with.## Step 1: not doing count filtering.## Step 2: not normalizing the data.## Step 3: not converting the data.## The method is: raw.## Step 4: not doing batch correction.## Step 4: transforming the data with log2.## transform_counts: Found 282204 values equal to 0, adding 1 to the matrix.plt <- plot_pca(hs_clinic_norm, plot_labels = FALSE)$plot## Not putting labels on the PC plot.pp(file = glue("images/no_biopsy_clinical_nobatch_pca-v{ver}.png"),
height = 8, width = 20, image = plt)## Writing the image to: images/no_biopsy_clinical_nobatch_pca-v202103.png and calling dev.off().
plt <- plot_pca(hs_clinic_norm, plot_labels = FALSE, size_column = "visitnumber")$plot## Not putting labels on the PC plot.plt <- plot_pca(hs_clinic_norm)$plot
pp(file = glue("images/no_biopsy_clinical_nobatch_pca_labeled-v{ver}.png"),
height = 8, width = 20, image = plt)## Writing the image to: images/no_biopsy_clinical_nobatch_pca_labeled-v202103.png and calling dev.off().
hs_clinic_de <- all_pairwise(hs_clinic, model_batch = FALSE)## Plotting a PCA before surrogate/batch inclusion.## Not putting labels on the PC plot.## Assuming no batch in model for testing pca.## Not putting labels on the PC plot.## Finished running DE analyses, collecting outputs.## Comparing analyses.
hs_clinic_table <- combine_de_tables(hs_clinic,
excel = glue::glue("excel/cure_separate_lost_failed_table-v{ver}.xlsx"))## Not adding plots, limma had an error.## Not adding plots, deseq had an error.## Not adding plots, edger had an error.## Not adding plots, basic had an error.## Error in (function (classes, fdef, mtable) : unable to find an inherited method for function 'fData' for signature '"NULL"'hs_clinic_sig <- extract_significant_genes(hs_clinic_table,
excel = glue::glue("excel/cure_separate_lost_failed_sig-v{ver}.xlsx"))## Error in extract_significant_genes(hs_clinic_table, excel = glue::glue("excel/cure_separate_lost_failed_sig-v{ver}.xlsx")): object 'hs_clinic_table' not found6 Combine cure and lost
Najib seems interested in this, but was curious to see how they look when the cure and lost samples are combined into a single group.
cl_idx <- pData(hs_clinical)[["condition"]] == "cure" |
pData(hs_clinical)[["condition"]] == "lost"
new_factor <- pData(hs_clinical)[["condition"]]
new_factor[cl_idx] <- "cure_lost"
hs_cl <- set_expt_conditions(hs_clinical, fact = new_factor)
test <- normalize_expt(hs_cl, transform = "log2", convert = "cpm", norm = "quant", filter = TRUE)## This function will replace the expt$expressionset slot with:## log2(cpm(quant(cbcb(data))))## It will save copies of each step along the way
## in expt$normalized with the corresponding libsizes. Keep libsizes in mind
## when invoking limma. The appropriate libsize is non-log(cpm(normalized)).
## This is most likely kept at:
## 'new_expt$normalized$intermediate_counts$normalization$libsizes'
## A copy of this may also be found at:
## new_expt$best_libsize## Not correcting the count-data for batch effects. If batch is
## included in EdgerR/limma's model, then this is probably wise; but in extreme
## batch effects this is a good parameter to play with.## Step 1: performing count filter with option: cbcb## Removing 5472 low-count genes (14456 remaining).## Step 2: normalizing the data with quant.## Step 3: converting the data with cpm.## The method is: raw.## Step 4: not doing batch correction.## Step 4: transforming the data with log2.## transform_counts: Found 47 values equal to 0, adding 1 to the matrix.plot_pca(test)$plot## Warning: ggrepel: 63 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
hs_clv2 <- subset_expt(hs_cl, subset = "batch!='Biopsy'")## Using a subset expression.## There were 86, now there are 54 samples.hs_clv2_norm <- normalize_expt(hs_clv2, filter = TRUE, convert = "cpm", norm = "quant")## This function will replace the expt$expressionset slot with:## cpm(quant(cbcb(data)))## It will save copies of each step along the way
## in expt$normalized with the corresponding libsizes. Keep libsizes in mind
## when invoking limma. The appropriate libsize is non-log(cpm(normalized)).
## This is most likely kept at:
## 'new_expt$normalized$intermediate_counts$normalization$libsizes'
## A copy of this may also be found at:
## new_expt$best_libsize## Leaving the data in its current base format, keep in mind that
## some metrics are easier to see when the data is log2 transformed, but
## EdgeR/DESeq do not accept transformed data.## Not correcting the count-data for batch effects. If batch is
## included in EdgerR/limma's model, then this is probably wise; but in extreme
## batch effects this is a good parameter to play with.## Step 1: performing count filter with option: cbcb## Removing 8038 low-count genes (11890 remaining).## Step 2: normalizing the data with quant.## Step 3: converting the data with cpm.## The method is: raw.## Step 4: not doing batch correction.## Step 4: not transforming the data.hs_clv2_norm <- normalize_expt(hs_clv2, transform = "log2")## This function will replace the expt$expressionset slot with:## log2(data)## It will save copies of each step along the way
## in expt$normalized with the corresponding libsizes. Keep libsizes in mind
## when invoking limma. The appropriate libsize is non-log(cpm(normalized)).
## This is most likely kept at:
## 'new_expt$normalized$intermediate_counts$normalization$libsizes'
## A copy of this may also be found at:
## new_expt$best_libsize## Filter is false, this should likely be set to something, good
## choices include cbcb, kofa, pofa (anything but FALSE). If you want this to
## stay FALSE, keep in mind that if other normalizations are performed, then the
## resulting libsizes are likely to be strange (potentially negative!)## Leaving the data unconverted. It is often advisable to cpm/rpkm
## the data to normalize for sampling differences, keep in mind though that rpkm
## has some annoying biases, and voom() by default does a cpm (though hpgl_voom()
## will try to detect this).## Leaving the data unnormalized. This is necessary for DESeq, but
## EdgeR/limma might benefit from normalization. Good choices include quantile,
## size-factor, tmm, etc.## Not correcting the count-data for batch effects. If batch is
## included in EdgerR/limma's model, then this is probably wise; but in extreme
## batch effects this is a good parameter to play with.## Step 1: not doing count filtering.## Step 2: not normalizing the data.## Step 3: not converting the data.## The method is: raw.## Step 4: not doing batch correction.## Step 4: transforming the data with log2.## transform_counts: Found 282204 values equal to 0, adding 1 to the matrix.plt <- plot_pca(hs_clv2_norm, plot_labels = FALSE)$plot## Not putting labels on the PC plot.plt
hs_clv2_de <- all_pairwise(hs_clv2, model_batch = TRUE)## Plotting a PCA before surrogate/batch inclusion.## Not putting labels on the PC plot.## Using limma's removeBatchEffect to visualize with(out) batch inclusion.## Not putting labels on the PC plot.## Finished running DE analyses, collecting outputs.## Comparing analyses.hs_clv2_table <- combine_de_tables(hs_clv2_de,
excel = glue::glue("excel/curelost_failed_table-v{ver}.xlsx"))## Deleting the file excel/curelost_failed_table-v202103.xlsx before writing the tables.## Writing a legend of columns.## Printing a pca plot before/after surrogates/batch estimation.## Working on table 1/1: failure_vs_cure_lost## Adding venn plots for failure_vs_cure_lost.
## Limma expression coefficients for failure_vs_cure_lost; R^2: 0.995; equation: y = 1.01x - 0.0623## Deseq expression coefficients for failure_vs_cure_lost; R^2: 0.986; equation: y = 0.911x + 0.861## Edger expression coefficients for failure_vs_cure_lost; R^2: 0.994; equation: y = 0.981x + 0.18## Writing summary information, compare_plot is: TRUE.## Performing save of excel/curelost_failed_table-v202103.xlsx.
hs_clv2_sig <- extract_significant_genes(hs_clv2_table,
excel = glue::glue("excel/curelost_failed_sig-v{ver}.xlsx"))## Deleting the file excel/curelost_failed_sig-v202103.xlsx before writing the tables.## Printing significant genes to the file: excel/curelost_failed_sig-v202103.xlsx## 1/1: Creating significant table up_limma_failure_vs_cure_lost## Printing significant genes to the file: excel/curelost_failed_sig-v202103.xlsx## 1/1: Creating significant table up_edger_failure_vs_cure_lost## Printing significant genes to the file: excel/curelost_failed_sig-v202103.xlsx## 1/1: Creating significant table up_deseq_failure_vs_cure_lost## Printing significant genes to the file: excel/curelost_failed_sig-v202103.xlsx## 1/1: Creating significant table up_ebseq_failure_vs_cure_lost## Printing significant genes to the file: excel/curelost_failed_sig-v202103.xlsx## 1/1: Creating significant table up_basic_failure_vs_cure_lost## Adding significance bar plots.ups <- hs_clv2_sig[["deseq"]][["ups"]][[1]]
downs <- hs_clv2_sig[["deseq"]][["downs"]][[1]]
hs_clv2_gprofiler_ups <- simple_gprofiler(ups)## Performing gProfiler GO search of 215 genes against hsapiens.## GO search found 6 hits.## Performing gProfiler KEGG search of 215 genes against hsapiens.## KEGG search found 0 hits.## Performing gProfiler REAC search of 215 genes against hsapiens.## REAC search found 3 hits.## Performing gProfiler MI search of 215 genes against hsapiens.## MI search found 0 hits.## Performing gProfiler TF search of 215 genes against hsapiens.## TF search found 20 hits.## Performing gProfiler CORUM search of 215 genes against hsapiens.## CORUM search found 3 hits.## Performing gProfiler HP search of 215 genes against hsapiens.## HP search found 0 hits.hs_clv2_gprofiler_ups$pvalue_plots$reactome_plot_over
hs_clv2_gprofiler_downs <- simple_gprofiler(downs)## Performing gProfiler GO search of 912 genes against hsapiens.## GO search found 55 hits.## Performing gProfiler KEGG search of 912 genes against hsapiens.## KEGG search found 2 hits.## Performing gProfiler REAC search of 912 genes against hsapiens.## REAC search found 10 hits.## Performing gProfiler MI search of 912 genes against hsapiens.## MI search found 0 hits.## Performing gProfiler TF search of 912 genes against hsapiens.## TF search found 69 hits.## Performing gProfiler CORUM search of 912 genes against hsapiens.## CORUM search found 0 hits.## Performing gProfiler HP search of 912 genes against hsapiens.## HP search found 0 hits.hs_clv2_gprofiler_downs$pvalue_plots$bpp_plot_over
hs_clv2_gprofiler_downs$pvalue_plots$reactome_plot_over
hs_clv2_gsva <- simple_gsva(hs_clv2)## Converting the rownames() of the expressionset to ENTREZID.## 577 ENSEMBL ID's didn't have a matching ENTEREZ ID. Dropping them now.## Before conversion, the expressionset has 19928 entries.## After conversion, the expressionset has 19507 entries.## Warning in .filterFeatures(expr, method): 1964 genes with constant expression
## values throuhgout the samples.## Mapping identifiers between gene sets and feature names
## Setting parallel calculations through a MulticoreParam back-end
## with workers=8 and tasks=100.
## Estimating ssGSEA scores for 2987 gene sets.
##
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|======================================================================| 100%hs_clv2_gsva_sig <- get_sig_gsva_categories(hs_clv2_gsva)## Starting limma pairwise comparison.## libsize was not specified, this parameter has profound effects on limma's result.## Using the libsize from expt$libsize.## Limma step 1/6: choosing model.## Choosing the non-intercept containing model.## Assuming this data is similar to a micro array and not performign voom.## Limma step 3/6: running lmFit with method: ls.## Limma step 4/6: making and fitting contrasts with no intercept. (~ 0 + factors)## Limma step 5/6: Running eBayes with robust = FALSE and trend = FALSE.## Limma step 6/6: Writing limma outputs.## Limma step 6/6: 1/1: Creating table: failure_vs_cure_lost. Adjust = BH## Limma step 6/6: 1/2: Creating table: cure_lost. Adjust = BH## Limma step 6/6: 2/2: Creating table: failure. Adjust = BH## The factor cure_lost has 30 rows.## The factor failure has 24 rows.## Testing each factor against the others.## Scoring cure_lost against everything else.## Scoring failure against everything else.
## Deleting the file excel/gsva_subset.xlsx before writing the tables.## Warning: ggrepel: 45 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps## Warning: ggrepel: 48 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
broad_c7 <- GSEABase::getGmt("reference/msigdb/c7.all.v7.2.entrez.gmt",
collectionType = GSEABase::BroadCollection(category = "c7"),
geneIdType = GSEABase::EntrezIdentifier())
testing <- simple_gsva(hs_clv2, signatures = broad_c7,
msig_xml = "reference/msigdb_v7.2.xml", cores = 10)## Converting the rownames() of the expressionset to ENTREZID.## 577 ENSEMBL ID's didn't have a matching ENTEREZ ID. Dropping them now.## Before conversion, the expressionset has 19928 entries.## After conversion, the expressionset has 19507 entries.## Warning in .filterFeatures(expr, method): 1964 genes with constant expression
## values throuhgout the samples.## Mapping identifiers between gene sets and feature names
## Setting parallel calculations through a MulticoreParam back-end
## with workers=10 and tasks=100.
## Estimating ssGSEA scores for 4872 gene sets.
##
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|======================================================================| 100%## Adding annotations from reference/msigdb_v7.2.xml.test_sig <- get_sig_gsva_categories(testing, excel = "excel/test_gsva.xlsx")## Starting limma pairwise comparison.## libsize was not specified, this parameter has profound effects on limma's result.## Using the libsize from expt$libsize.## Limma step 1/6: choosing model.## Choosing the non-intercept containing model.## Assuming this data is similar to a micro array and not performign voom.## Limma step 3/6: running lmFit with method: ls.## Limma step 4/6: making and fitting contrasts with no intercept. (~ 0 + factors)## Limma step 5/6: Running eBayes with robust = FALSE and trend = FALSE.## Limma step 6/6: Writing limma outputs.## Limma step 6/6: 1/1: Creating table: failure_vs_cure_lost. Adjust = BH## Limma step 6/6: 1/2: Creating table: cure_lost. Adjust = BH## Limma step 6/6: 2/2: Creating table: failure. Adjust = BH## The factor cure_lost has 30 rows.## The factor failure has 24 rows.## Testing each factor against the others.## Scoring cure_lost against everything else.## Scoring failure against everything else.
## Deleting the file excel/test_gsva.xlsx before writing the tables.## Warning: ggrepel: 16 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps## Warning: ggrepel: 25 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
7 Cell types
The following blocks split the samples into a few groups by sample type and look at the distributions between them.
7.1 Implementation details
Get top/bottom n genes for each cell type, using clinical outcome as the factor of interest. For the moment, use sva for the DE analysis. Provide cpms for the top/bottom n genes.
Start with top/bottom 200. Perform default logFC and p-value as well.
8 NOTE
Sample sheet rows 11-14 had the clinical outcome set to ‘NA’ instead of cure/fail/lost In addition, some newer entries were ‘Cure/Failure’ instead of ‘cure/failure’, I changed this.
8.1 Monocytes
mono <- subset_expt(hs_valid, subset = "typeofcells=='Monocytes'")## Using a subset expression.## There were 104, now there are 20 samples.mono <- set_expt_conditions(mono, fact = "clinicaloutcome")
mono <- set_expt_batches(mono, fact = "donor")
## FIXME set_expt_colors should speak up if there are mismatches here!!!
mono <- set_expt_colors(expt = mono, colors = chosen_colors)## The new colors are a character, changing according to condition.save_result <- save(mono, file = "rda/monocyte_expt.rda")
mono_norm <- normalize_expt(mono, convert = "cpm", filter = TRUE)## This function will replace the expt$expressionset slot with:## cpm(cbcb(data))## It will save copies of each step along the way
## in expt$normalized with the corresponding libsizes. Keep libsizes in mind
## when invoking limma. The appropriate libsize is non-log(cpm(normalized)).
## This is most likely kept at:
## 'new_expt$normalized$intermediate_counts$normalization$libsizes'
## A copy of this may also be found at:
## new_expt$best_libsize## Leaving the data in its current base format, keep in mind that
## some metrics are easier to see when the data is log2 transformed, but
## EdgeR/DESeq do not accept transformed data.## Leaving the data unnormalized. This is necessary for DESeq, but
## EdgeR/limma might benefit from normalization. Good choices include quantile,
## size-factor, tmm, etc.## Not correcting the count-data for batch effects. If batch is
## included in EdgerR/limma's model, then this is probably wise; but in extreme
## batch effects this is a good parameter to play with.## Step 1: performing count filter with option: cbcb## Removing 9025 low-count genes (10903 remaining).## Step 2: not normalizing the data.## Step 3: converting the data with cpm.## The method is: raw.## Step 4: not doing batch correction.## Step 4: not transforming the data.mono_norm <- normalize_expt(mono_norm, transform = "log2")## This function will replace the expt$expressionset slot with:## log2(data)## It will save copies of each step along the way
## in expt$normalized with the corresponding libsizes. Keep libsizes in mind
## when invoking limma. The appropriate libsize is non-log(cpm(normalized)).
## This is most likely kept at:
## 'new_expt$normalized$intermediate_counts$normalization$libsizes'
## A copy of this may also be found at:
## new_expt$best_libsize## Filter is false, this should likely be set to something, good
## choices include cbcb, kofa, pofa (anything but FALSE). If you want this to
## stay FALSE, keep in mind that if other normalizations are performed, then the
## resulting libsizes are likely to be strange (potentially negative!)## Leaving the data unconverted. It is often advisable to cpm/rpkm
## the data to normalize for sampling differences, keep in mind though that rpkm
## has some annoying biases, and voom() by default does a cpm (though hpgl_voom()
## will try to detect this).## Leaving the data unnormalized. This is necessary for DESeq, but
## EdgeR/limma might benefit from normalization. Good choices include quantile,
## size-factor, tmm, etc.## Not correcting the count-data for batch effects. If batch is
## included in EdgerR/limma's model, then this is probably wise; but in extreme
## batch effects this is a good parameter to play with.## Step 1: not doing count filtering.## Step 2: not normalizing the data.## Step 3: not converting the data.## The method is: raw.## Step 4: not doing batch correction.## Step 4: transforming the data with log2.## transform_counts: Found 617 values equal to 0, adding 1 to the matrix.plt <- plot_pca(mono_norm, plot_labels = FALSE)$plot## Not putting labels on the PC plot.pp(file = glue("images/mono_pca_normalized-v{ver}.pdf"), image = plt)## Writing the image to: images/mono_pca_normalized-v202103.pdf and calling dev.off().
plt
mono_de <- sm(all_pairwise(mono, model_batch = FALSE, filter = TRUE))
mono_tables <- combine_de_tables(mono_de, keepers = keepers,
excel = glue::glue("excel/monocyte_clinical_all_tables-v{ver}.xlsx"))## Deleting the file excel/monocyte_clinical_all_tables-v202103.xlsx before writing the tables.## Writing a legend of columns.## Printing a pca plot before/after surrogates/batch estimation.## Working on 1/1: fail_vs_cure which is: failure/cure.## Found table with failure_vs_cure## Adding venn plots for fail_vs_cure.
## Limma expression coefficients for fail_vs_cure; R^2: 0.969; equation: y = 0.995x - 0.0119## Deseq expression coefficients for fail_vs_cure; R^2: 0.974; equation: y = 1.03x - 0.301## Edger expression coefficients for fail_vs_cure; R^2: 0.974; equation: y = 1.03x - 0.311## Writing summary information, compare_plot is: TRUE.## Performing save of excel/monocyte_clinical_all_tables-v202103.xlsx.
written <- write_xlsx(data = mono_tables[["data"]][[1]],
excel = glue::glue("excel/monocyte_clinical_table-v{ver}.xlsx"))## Saving to: excel/monocyte_clinical_table-v202103.xlsxmono_sig <- extract_significant_genes(mono_tables, according_to = "deseq")## Printing significant genes to the file:## 1/1: Creating significant table up_deseq_fail_vs_curewritten <- write_xlsx(data = mono_sig[["deseq"]][["ups"]][[1]],
excel = glue::glue("excel/monocyte_clinical_sigup-v{ver}.xlsx"))## Saving to: excel/monocyte_clinical_sigup-v202103.xlsxwritten <- write_xlsx(data = mono_sig[["deseq"]][["downs"]][[1]],
excel = glue::glue("excel/monocyte_clinical_sigdown-v{ver}.xlsx"))## Saving to: excel/monocyte_clinical_sigdown-v202103.xlsxmono_pct_sig <- extract_significant_genes(mono_tables, n = 200, lfc = NULL, p = NULL, according_to = "deseq")## Getting the top and bottom 200 genes.## Printing significant genes to the file:## 1/1: Creating significant table up_deseq_fail_vs_curewritten <- write_xlsx(data = mono_pct_sig[["deseq"]][["ups"]][[1]],
excel = glue::glue("excel/monocyte_clinical_sigup_pct-v{ver}.xlsx"))## Saving to: excel/monocyte_clinical_sigup_pct-v202103.xlsxwritten <- write_xlsx(data = mono_pct_sig[["deseq"]][["downs"]][[1]],
excel = glue::glue("excel/monocyte_clinical_sigdown_pct-v{ver}.xlsx"))## Saving to: excel/monocyte_clinical_sigdown_pct-v202103.xlsxmono_sig$summary_df## data frame with 0 columns and 1 rowmono_cpm <- sm(normalize_expt(mono, convert = "cpm"))
written <- write_xlsx(data = exprs(mono_cpm),
excel = glue::glue("excel/monocyte_cpm_before_batch-v{ver}.xlsx"))## Saving to: excel/monocyte_cpm_before_batch-v202103.xlsxmono_bcpm <- sm(normalize_expt(mono, filter = TRUE, convert = "cpm"))
written <- write_xlsx(data = exprs(mono_bcpm),
excel = glue::glue("excel/monocyte_cpm_after_batch-v{ver}.xlsx"))## Saving to: excel/monocyte_cpm_after_batch-v202103.xlsxups <- mono_sig[["deseq"]][["ups"]][["fail_vs_cure"]]
downs <- mono_sig[["deseq"]][["downs"]][["fail_vs_cure"]]
up_goseq <- simple_goseq(sig_genes = ups, go_db = hs_go, length_db = hs_length)## Using the row names of your table.## Found 121 genes out of 122 from the sig_genes in the go_db.## Found 122 genes out of 122 from the sig_genes in the length_db.## Using manually entered categories.## Calculating the p-values...## 'select()' returned 1:1 mapping between keys and columns## simple_goseq(): Calculating q-values## simple_goseq(): Filling godata with terms, this is slow.## Testing that go categories are defined.## Removing undefined categories.## Gathering synonyms.## Gathering category definitions.## simple_goseq(): Making pvalue plots for the ontologies.
broad_c7 <- GSEABase::getGmt("reference/msigdb/c7.all.v7.2.entrez.gmt",
collectionType = GSEABase::BroadCollection(category = "c7"),
geneIdType = GSEABase::EntrezIdentifier())
up_goseq_gsva <- goseq_msigdb(sig_genes = ups, signatures = broad_c7,
signature_category = "c7", length_db = hs_length)## Starting to coerce the msig data to the ontology format.## Finished coercing the msig data.## Error in requireNamespace(orgdb): object 'orgdb' not found8.2 Neutrophils
neut <- subset_expt(hs_valid, subset = "typeofcells=='Neutrophils'")## Using a subset expression.## There were 104, now there are 20 samples.neut <- set_expt_conditions(neut, fact = "clinicaloutcome")
neut <- set_expt_batches(neut, fact = "donor")
neut <- set_expt_colors(expt = neut, colors = chosen_colors)## The new colors are a character, changing according to condition.save_result <- save(neut, file = "rda/neutrophil_expt.rda")
neut_norm <- sm(normalize_expt(neut, convert = "cpm", filter = TRUE))
neut_norm <- normalize_expt(neut_norm, transform = "log2")## This function will replace the expt$expressionset slot with:## log2(data)## It will save copies of each step along the way
## in expt$normalized with the corresponding libsizes. Keep libsizes in mind
## when invoking limma. The appropriate libsize is non-log(cpm(normalized)).
## This is most likely kept at:
## 'new_expt$normalized$intermediate_counts$normalization$libsizes'
## A copy of this may also be found at:
## new_expt$best_libsize## Filter is false, this should likely be set to something, good
## choices include cbcb, kofa, pofa (anything but FALSE). If you want this to
## stay FALSE, keep in mind that if other normalizations are performed, then the
## resulting libsizes are likely to be strange (potentially negative!)## Leaving the data unconverted. It is often advisable to cpm/rpkm
## the data to normalize for sampling differences, keep in mind though that rpkm
## has some annoying biases, and voom() by default does a cpm (though hpgl_voom()
## will try to detect this).## Leaving the data unnormalized. This is necessary for DESeq, but
## EdgeR/limma might benefit from normalization. Good choices include quantile,
## size-factor, tmm, etc.## Not correcting the count-data for batch effects. If batch is
## included in EdgerR/limma's model, then this is probably wise; but in extreme
## batch effects this is a good parameter to play with.## Step 1: not doing count filtering.## Step 2: not normalizing the data.## Step 3: not converting the data.## The method is: raw.## Step 4: not doing batch correction.## Step 4: transforming the data with log2.## transform_counts: Found 863 values equal to 0, adding 1 to the matrix.plt <- plot_pca(neut_norm, plot_labels = FALSE)$plot## Not putting labels on the PC plot.pp(file = glue("images/neut_pca_normalized-v{ver}.pdf"), image = plt)## Writing the image to: images/neut_pca_normalized-v202103.pdf and calling dev.off().
plt
neut_de <- sm(all_pairwise(neut, model_batch = FALSE, filter = TRUE))
neut_tables <- combine_de_tables(neut_de, keepers = keepers,
excel = glue::glue("excel/neutrophil_clinical_all_tables-v{ver}.xlsx"))## Deleting the file excel/neutrophil_clinical_all_tables-v202103.xlsx before writing the tables.## Writing a legend of columns.## Printing a pca plot before/after surrogates/batch estimation.## Working on 1/1: fail_vs_cure which is: failure/cure.## Found table with failure_vs_cure## Adding venn plots for fail_vs_cure.
## Limma expression coefficients for fail_vs_cure; R^2: 0.962; equation: y = 0.982x + 0.0896## Deseq expression coefficients for fail_vs_cure; R^2: 0.964; equation: y = 0.968x + 0.287## Edger expression coefficients for fail_vs_cure; R^2: 0.964; equation: y = 0.968x + 0.23## Writing summary information, compare_plot is: TRUE.## Performing save of excel/neutrophil_clinical_all_tables-v202103.xlsx.
written <- write_xlsx(data = neut_tables[["data"]][[1]],
excel = glue::glue("excel/neutrophil_clinical_table-v{ver}.xlsx"))## Saving to: excel/neutrophil_clinical_table-v202103.xlsxneut_sig <- extract_significant_genes(neut_tables, according_to = "deseq")## Printing significant genes to the file:## 1/1: Creating significant table up_deseq_fail_vs_curewritten <- write_xlsx(data = neut_sig[["deseq"]][["ups"]][[1]],
excel = glue::glue("excel/neutrophil_clinical_sigup-v{ver}.xlsx"))## Saving to: excel/neutrophil_clinical_sigup-v202103.xlsxwritten <- write_xlsx(data = neut_sig[["deseq"]][["downs"]][[1]],
excel = glue::glue("excel/neutrophil_clinical_sigdown-v{ver}.xlsx"))## Saving to: excel/neutrophil_clinical_sigdown-v202103.xlsxneut_pct_sig <- extract_significant_genes(neut_tables, n = 200, lfc = NULL, p = NULL, according_to = "deseq")## Getting the top and bottom 200 genes.## Printing significant genes to the file:## 1/1: Creating significant table up_deseq_fail_vs_curewritten <- write_xlsx(data = neut_sig[["deseq"]][["ups"]][[1]],
excel = glue::glue("excel/neutrophil_clinical_sigup_pct-v{ver}.xlsx"))## Saving to: excel/neutrophil_clinical_sigup_pct-v202103.xlsxwritten <- write_xlsx(data = neut_sig[["deseq"]][["downs"]][[1]],
excel = glue::glue("excel/neutrophil_clinical_sigdown_pct-v{ver}.xlsx"))## Saving to: excel/neutrophil_clinical_sigdown_pct-v202103.xlsxneut_cpm <- sm(normalize_expt(neut, convert = "cpm"))
written <- write_xlsx(data = exprs(neut_cpm),
excel = glue::glue("excel/neutrophil_cpm_before_batch-v{ver}.xlsx"))## Saving to: excel/neutrophil_cpm_before_batch-v202103.xlsxneut_bcpm <- sm(normalize_expt(neut, filter = TRUE, convert = "cpm"))
written <- write_xlsx(data = exprs(neut_bcpm),
excel = glue::glue("excel/neutrophil_cpm_after_batch-v{ver}.xlsx"))## Saving to: excel/neutrophil_cpm_after_batch-v202103.xlsx8.3 Eosinophils
eo <- subset_expt(hs_valid, subset = "typeofcells=='Eosinophils'")## Using a subset expression.## There were 104, now there are 14 samples.eo <- set_expt_conditions(eo, fact = "clinicaloutcome")
eo <- set_expt_batches(eo, fact = "donor")
eo <- set_expt_colors(expt = eo, colors = chosen_colors)## The new colors are a character, changing according to condition.save_result <- save(eo, file = "rda/eosinophil_expt.rda")
eo_norm <- sm(normalize_expt(eo, convert = "cpm", filter = TRUE))
eo_norm <- normalize_expt(eo_norm, transform = "log2")## This function will replace the expt$expressionset slot with:## log2(data)## It will save copies of each step along the way
## in expt$normalized with the corresponding libsizes. Keep libsizes in mind
## when invoking limma. The appropriate libsize is non-log(cpm(normalized)).
## This is most likely kept at:
## 'new_expt$normalized$intermediate_counts$normalization$libsizes'
## A copy of this may also be found at:
## new_expt$best_libsize## Filter is false, this should likely be set to something, good
## choices include cbcb, kofa, pofa (anything but FALSE). If you want this to
## stay FALSE, keep in mind that if other normalizations are performed, then the
## resulting libsizes are likely to be strange (potentially negative!)## Leaving the data unconverted. It is often advisable to cpm/rpkm
## the data to normalize for sampling differences, keep in mind though that rpkm
## has some annoying biases, and voom() by default does a cpm (though hpgl_voom()
## will try to detect this).## Leaving the data unnormalized. This is necessary for DESeq, but
## EdgeR/limma might benefit from normalization. Good choices include quantile,
## size-factor, tmm, etc.## Not correcting the count-data for batch effects. If batch is
## included in EdgerR/limma's model, then this is probably wise; but in extreme
## batch effects this is a good parameter to play with.## Step 1: not doing count filtering.## Step 2: not normalizing the data.## Step 3: not converting the data.## The method is: raw.## Step 4: not doing batch correction.## Step 4: transforming the data with log2.## transform_counts: Found 181 values equal to 0, adding 1 to the matrix.plt <- plot_pca(eo_norm, plot_labels = FALSE)$plot## Not putting labels on the PC plot.pp(file = glue("images/eo_pca_normalized-v{ver}.pdf"), image = plt)## Writing the image to: images/eo_pca_normalized-v202103.pdf and calling dev.off().
plt
eo_de <- sm(all_pairwise(eo, model_batch = FALSE, filter = TRUE))
eo_tables <- combine_de_tables(eo_de, keepers = keepers,
excel = glue::glue("excel/eosinophil_clinical_all_tables-v{ver}.xlsx"))## Deleting the file excel/eosinophil_clinical_all_tables-v202103.xlsx before writing the tables.## Writing a legend of columns.## Printing a pca plot before/after surrogates/batch estimation.## Working on 1/1: fail_vs_cure which is: failure/cure.## Found table with failure_vs_cure## Adding venn plots for fail_vs_cure.
## Limma expression coefficients for fail_vs_cure; R^2: 0.973; equation: y = 0.985x + 0.0779## Deseq expression coefficients for fail_vs_cure; R^2: 0.972; equation: y = 1.02x - 0.162## Edger expression coefficients for fail_vs_cure; R^2: 0.973; equation: y = 1.02x - 0.175## Writing summary information, compare_plot is: TRUE.## Performing save of excel/eosinophil_clinical_all_tables-v202103.xlsx.
written <- write_xlsx(data = eo_tables[["data"]][[1]],
excel = glue::glue("excel/eosinophil_clinical_table-v{ver}.xlsx"))## Saving to: excel/eosinophil_clinical_table-v202103.xlsxeo_sig <- extract_significant_genes(eo_tables, according_to = "deseq")## Printing significant genes to the file:## 1/1: Creating significant table up_deseq_fail_vs_curewritten <- write_xlsx(data = eo_sig[["deseq"]][["ups"]][[1]],
excel = glue::glue("excel/eosinophil_clinical_sigup-v{ver}.xlsx"))## Saving to: excel/eosinophil_clinical_sigup-v202103.xlsxwritten <- write_xlsx(data = eo_sig[["deseq"]][["downs"]][[1]],
excel = glue::glue("excel/eosinophil_clinical_sigdown-v{ver}.xlsx"))## Saving to: excel/eosinophil_clinical_sigdown-v202103.xlsxeo_pct_sig <- extract_significant_genes(eo_tables, n = 200, lfc = NULL, p = NULL, according_to = "deseq")## Getting the top and bottom 200 genes.## Printing significant genes to the file:## 1/1: Creating significant table up_deseq_fail_vs_curewritten <- write_xlsx(data = eo_pct_sig[["deseq"]][["ups"]][[1]],
excel = glue::glue("excel/eosinophil_clinical_sigup_pct-v{ver}.xlsx"))## Saving to: excel/eosinophil_clinical_sigup_pct-v202103.xlsxwritten <- write_xlsx(data = eo_pct_sig[["deseq"]][["downs"]][[1]],
excel = glue::glue("excel/eosinophil_clinical_sigdown_pct-v{ver}.xlsx"))## Saving to: excel/eosinophil_clinical_sigdown_pct-v202103.xlsxeo_cpm <- sm(normalize_expt(eo, convert = "cpm"))
written <- write_xlsx(data = exprs(eo_cpm),
excel = glue::glue("excel/eosinophil_cpm_before_batch-v{ver}.xlsx"))## Saving to: excel/eosinophil_cpm_before_batch-v202103.xlsxeo_bcpm <- sm(normalize_expt(eo, filter = TRUE, convert = "cpm"))
written <- write_xlsx(data = exprs(eo_bcpm),
excel = glue::glue("excel/eosinophil_cpm_after_batch-v{ver}.xlsx"))## Saving to: excel/eosinophil_cpm_after_batch-v202103.xlsx8.4 Biopsies
biop <- subset_expt(hs_valid, subset = "typeofcells=='Biopsy'")## Using a subset expression.## There were 104, now there are 32 samples.biop <- set_expt_conditions(biop, fact = "clinicaloutcome")
biop <- set_expt_batches(biop, fact = "donor")
biop <- set_expt_colors(expt = biop, colors = chosen_colors)## The new colors are a character, changing according to condition.save_result <- save(biop, file = "rda/biopsy_expt.rda")
biop_norm <- normalize_expt(biop, filter = TRUE, convert = "cpm")## This function will replace the expt$expressionset slot with:## cpm(cbcb(data))## It will save copies of each step along the way
## in expt$normalized with the corresponding libsizes. Keep libsizes in mind
## when invoking limma. The appropriate libsize is non-log(cpm(normalized)).
## This is most likely kept at:
## 'new_expt$normalized$intermediate_counts$normalization$libsizes'
## A copy of this may also be found at:
## new_expt$best_libsize## Leaving the data in its current base format, keep in mind that
## some metrics are easier to see when the data is log2 transformed, but
## EdgeR/DESeq do not accept transformed data.## Leaving the data unnormalized. This is necessary for DESeq, but
## EdgeR/limma might benefit from normalization. Good choices include quantile,
## size-factor, tmm, etc.## Not correcting the count-data for batch effects. If batch is
## included in EdgerR/limma's model, then this is probably wise; but in extreme
## batch effects this is a good parameter to play with.## Step 1: performing count filter with option: cbcb## Removing 5940 low-count genes (13988 remaining).## Step 2: not normalizing the data.## Step 3: converting the data with cpm.## The method is: raw.## Step 4: not doing batch correction.## Step 4: not transforming the data.biop_norm <- normalize_expt(biop_norm, transform = "log2")## This function will replace the expt$expressionset slot with:## log2(data)## It will save copies of each step along the way
## in expt$normalized with the corresponding libsizes. Keep libsizes in mind
## when invoking limma. The appropriate libsize is non-log(cpm(normalized)).
## This is most likely kept at:
## 'new_expt$normalized$intermediate_counts$normalization$libsizes'
## A copy of this may also be found at:
## new_expt$best_libsize## Filter is false, this should likely be set to something, good
## choices include cbcb, kofa, pofa (anything but FALSE). If you want this to
## stay FALSE, keep in mind that if other normalizations are performed, then the
## resulting libsizes are likely to be strange (potentially negative!)## Leaving the data unconverted. It is often advisable to cpm/rpkm
## the data to normalize for sampling differences, keep in mind though that rpkm
## has some annoying biases, and voom() by default does a cpm (though hpgl_voom()
## will try to detect this).## Leaving the data unnormalized. This is necessary for DESeq, but
## EdgeR/limma might benefit from normalization. Good choices include quantile,
## size-factor, tmm, etc.## Not correcting the count-data for batch effects. If batch is
## included in EdgerR/limma's model, then this is probably wise; but in extreme
## batch effects this is a good parameter to play with.## Step 1: not doing count filtering.## Step 2: not normalizing the data.## Step 3: not converting the data.## The method is: raw.## Step 4: not doing batch correction.## Step 4: transforming the data with log2.## transform_counts: Found 1757 values equal to 0, adding 1 to the matrix.plt <- plot_pca(biop_norm, plot_labels = FALSE)$plot## Potentially check over the experimental design, there appear to be missing values.## Warning in plot_pca(biop_norm, plot_labels = FALSE): There are NA values in the
## component data. This can lead to weird plotting errors.## Not putting labels on the PC plot.pp(file = glue("images/biop_pca_normalized-v{ver}.pdf"), image = plt)## Writing the image to: images/biop_pca_normalized-v202103.pdf and calling dev.off().## Warning: Removed 21 rows containing missing values (geom_point).## Warning: Removed 21 rows containing missing values (geom_point).
plt## Warning: Removed 21 rows containing missing values (geom_point).
biop_de <- sm(all_pairwise(biop, model_batch = FALSE, filter = TRUE))
biop_tables <- combine_de_tables(biop_de, keepers = keepers,
excel = glue::glue("excel/biopsy_clinical_all_tables-v{ver}.xlsx"))## Deleting the file excel/biopsy_clinical_all_tables-v202103.xlsx before writing the tables.## Writing a legend of columns.## Printing a pca plot before/after surrogates/batch estimation.## Warning: Removed 21 rows containing missing values (geom_point).
## Warning: Removed 21 rows containing missing values (geom_point).
## Warning: Removed 21 rows containing missing values (geom_point).
## Warning: Removed 21 rows containing missing values (geom_point).## Working on 1/1: fail_vs_cure which is: failure/cure.## Found table with failure_vs_cure## Adding venn plots for fail_vs_cure.
## Limma expression coefficients for fail_vs_cure; R^2: 0.991; equation: y = 0.985x + 0.058## Deseq expression coefficients for fail_vs_cure; R^2: 0.992; equation: y = 0.975x + 0.284## Edger expression coefficients for fail_vs_cure; R^2: 0.992; equation: y = 0.975x + 0.173## Writing summary information, compare_plot is: TRUE.## Performing save of excel/biopsy_clinical_all_tables-v202103.xlsx.
written <- write_xlsx(data = biop_tables[["data"]][[1]],
excel = glue::glue("excel/biopsy_clinical_table-v{ver}.xlsx"))## Saving to: excel/biopsy_clinical_table-v202103.xlsxbiop_sig <- extract_significant_genes(biop_tables, according_to = "deseq")## Printing significant genes to the file:## The up table fail_vs_cure is empty.##written <- write_xlsx(data = biop_sig[["deseq"]][["ups"]][[1]],
## excel = glue::glue("excel/biopsy_clinical_sigup-v{ver}.xlsx"))
written <- write_xlsx(data = biop_sig[["deseq"]][["downs"]][[1]],
excel = glue::glue("excel/biopsy_clinical_sigdown-v{ver}.xlsx"))## Saving to: excel/biopsy_clinical_sigdown-v202103.xlsxbiop_pct_sig <- extract_significant_genes(biop_tables, n = 200, lfc = NULL, p = NULL, according_to = "deseq")## Getting the top and bottom 200 genes.## Printing significant genes to the file:## 1/1: Creating significant table up_deseq_fail_vs_curewritten <- write_xlsx(data = biop_pct_sig[["deseq"]][["ups"]][[1]],
excel = glue::glue("excel/biopsy_clinical_sigup_pct-v{ver}.xlsx"))## Saving to: excel/biopsy_clinical_sigup_pct-v202103.xlsxwritten <- write_xlsx(data = biop_pct_sig[["deseq"]][["downs"]][[1]],
excel = glue::glue("excel/biopsy_clinical_sigdown_pct-v{ver}.xlsx"))## Saving to: excel/biopsy_clinical_sigdown_pct-v202103.xlsxbiop_cpm <- sm(normalize_expt(biop, convert = "cpm"))
written <- write_xlsx(data = exprs(biop_cpm),
excel = glue::glue("excel/biopsy_cpm_before_batch-v{ver}.xlsx"))## Saving to: excel/biopsy_cpm_before_batch-v202103.xlsxbiop_bcpm <- sm(normalize_expt(biop, filter = TRUE, convert = "cpm"))
written <- write_xlsx(data = exprs(biop_bcpm),
excel = glue::glue("excel/biopsy_cpm_after_batch-v{ver}.xlsx"))## Saving to: excel/biopsy_cpm_after_batch-v202103.xlsx8.5 Macrophages
These samples are rather different from all of the others. The following section is therefore written primarily in response to a separate set of emails from Olga and Maria Adelaida; here is a snippet:
Dear all, about the samples corresponding to infected macrophages with three sensitive (2.2) and three resistant (2.3) clinical strains of L. (V.) panamensis, I send you the results of parasite burden by detection of 7SLRNA. I think these results are interesting, but the sample size is very small. Doctor Najib or Trey could you please send me the quality data and PCA analysis of these samples?
and
Hi Doctor, thank you. These samples corresponding to primary macrophages infected with clinical strains 2.2 (n = 3) and 2.3 (n = 3). These information is in the file: TMRC project 3: excel Host TMRC3 v1.1, rows 137 to 150.
Thus I added 3 columns to the end of the sample sheet which seek to include this information. The first is ‘drug’ and encodes both the infection state (no for the two controls and yes for everything else), the second is zymodeme which I took from the tmrc2 sample sheet, the last is drug, which is either no or sb.
macr <- subset_expt(hs_valid, subset = "typeofcells=='Macrophages'")## Using a subset expression.## There were 104, now there are 12 samples.macr <- set_expt_conditions(macr, fact = "zymodeme")
macr <- set_expt_batches(macr, fact = "macrdrug")
macr_norm <- normalize_expt(macr, norm = "quant", convert = "cpm", filter = TRUE)## This function will replace the expt$expressionset slot with:## cpm(quant(cbcb(data)))## It will save copies of each step along the way
## in expt$normalized with the corresponding libsizes. Keep libsizes in mind
## when invoking limma. The appropriate libsize is non-log(cpm(normalized)).
## This is most likely kept at:
## 'new_expt$normalized$intermediate_counts$normalization$libsizes'
## A copy of this may also be found at:
## new_expt$best_libsize## Leaving the data in its current base format, keep in mind that
## some metrics are easier to see when the data is log2 transformed, but
## EdgeR/DESeq do not accept transformed data.## Not correcting the count-data for batch effects. If batch is
## included in EdgerR/limma's model, then this is probably wise; but in extreme
## batch effects this is a good parameter to play with.## Step 1: performing count filter with option: cbcb## Removing 8899 low-count genes (11029 remaining).## Step 2: normalizing the data with quant.## Step 3: converting the data with cpm.## The method is: raw.## Step 4: not doing batch correction.## Step 4: not transforming the data.macr_norm <- normalize_expt(macr_norm, transform = "log2")## This function will replace the expt$expressionset slot with:## log2(data)## It will save copies of each step along the way
## in expt$normalized with the corresponding libsizes. Keep libsizes in mind
## when invoking limma. The appropriate libsize is non-log(cpm(normalized)).
## This is most likely kept at:
## 'new_expt$normalized$intermediate_counts$normalization$libsizes'
## A copy of this may also be found at:
## new_expt$best_libsize## Filter is false, this should likely be set to something, good
## choices include cbcb, kofa, pofa (anything but FALSE). If you want this to
## stay FALSE, keep in mind that if other normalizations are performed, then the
## resulting libsizes are likely to be strange (potentially negative!)## Leaving the data unconverted. It is often advisable to cpm/rpkm
## the data to normalize for sampling differences, keep in mind though that rpkm
## has some annoying biases, and voom() by default does a cpm (though hpgl_voom()
## will try to detect this).## Leaving the data unnormalized. This is necessary for DESeq, but
## EdgeR/limma might benefit from normalization. Good choices include quantile,
## size-factor, tmm, etc.## Not correcting the count-data for batch effects. If batch is
## included in EdgerR/limma's model, then this is probably wise; but in extreme
## batch effects this is a good parameter to play with.## Step 1: not doing count filtering.## Step 2: not normalizing the data.## Step 3: not converting the data.## The method is: raw.## Step 4: not doing batch correction.## Step 4: transforming the data with log2.plt <- plot_pca(macr_norm, plot_labels = FALSE)$plot## Not putting labels on the PC plot.pp(file = glue("images/macrophage_side_experiment_norm_pca-v{ver}.pdf"), image = plt)## Writing the image to: images/macrophage_side_experiment_norm_pca-v202103.pdf and calling dev.off().
plt
macr_nb <- normalize_expt(macr, filter = TRUE)## This function will replace the expt$expressionset slot with:## cbcb(data)## It will save copies of each step along the way
## in expt$normalized with the corresponding libsizes. Keep libsizes in mind
## when invoking limma. The appropriate libsize is non-log(cpm(normalized)).
## This is most likely kept at:
## 'new_expt$normalized$intermediate_counts$normalization$libsizes'
## A copy of this may also be found at:
## new_expt$best_libsize## Leaving the data in its current base format, keep in mind that
## some metrics are easier to see when the data is log2 transformed, but
## EdgeR/DESeq do not accept transformed data.## Leaving the data unconverted. It is often advisable to cpm/rpkm
## the data to normalize for sampling differences, keep in mind though that rpkm
## has some annoying biases, and voom() by default does a cpm (though hpgl_voom()
## will try to detect this).## Leaving the data unnormalized. This is necessary for DESeq, but
## EdgeR/limma might benefit from normalization. Good choices include quantile,
## size-factor, tmm, etc.## Not correcting the count-data for batch effects. If batch is
## included in EdgerR/limma's model, then this is probably wise; but in extreme
## batch effects this is a good parameter to play with.## Step 1: performing count filter with option: cbcb## Removing 8899 low-count genes (11029 remaining).## Step 2: not normalizing the data.## Step 3: not converting the data.## The method is: raw.## Step 4: not doing batch correction.## Step 4: not transforming the data.macr_nb <- normalize_expt(macr_nb, norm = "quant", convert = "cpm", transform = "log2")## This function will replace the expt$expressionset slot with:## log2(cpm(quant(data)))## It will save copies of each step along the way
## in expt$normalized with the corresponding libsizes. Keep libsizes in mind
## when invoking limma. The appropriate libsize is non-log(cpm(normalized)).
## This is most likely kept at:
## 'new_expt$normalized$intermediate_counts$normalization$libsizes'
## A copy of this may also be found at:
## new_expt$best_libsize## Filter is false, this should likely be set to something, good
## choices include cbcb, kofa, pofa (anything but FALSE). If you want this to
## stay FALSE, keep in mind that if other normalizations are performed, then the
## resulting libsizes are likely to be strange (potentially negative!)## Not correcting the count-data for batch effects. If batch is
## included in EdgerR/limma's model, then this is probably wise; but in extreme
## batch effects this is a good parameter to play with.## Step 1: not doing count filtering.## Step 2: normalizing the data with quant.## Step 3: converting the data with cpm.## The method is: raw.## Step 4: not doing batch correction.## Step 4: transforming the data with log2.plt <- plot_pca(macr_nb)$plot
pp(file = glue("images/macrophage_side_experiment-v{ver}/normbatch_pca.pdf"), image = plt)## Warning in cairo_pdf(filename = file, ...): cairo error 'error while writing to
## output stream'## Error in cairo_pdf(filename = file, ...): unable to start device 'cairo_pdf'plt
macr_written <- write_expt(macr, excel = "excel/macrophage_side_experiment/macrophage_expt.xlsx")## Deleting the file excel/macrophage_side_experiment/macrophage_expt.xlsx before writing the tables.## Writing the first sheet, containing a legend and some summary data.
## Writing the raw reads.## Graphing the raw reads.## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## Warning in doTryCatch(return(expr), name, parentenv, handler): display list
## redraw incomplete## Warning in doTryCatch(return(expr), name, parentenv, handler): display list
## redraw incomplete## Attempting mixed linear model with: ~ condition + batch## Fitting the expressionset to the model, this is slow.##
## Total:108 s## Placing factor: condition at the beginning of the model.## Writing the normalized reads.## Graphing the normalized reads.
## `geom_smooth()` using formula 'y ~ x'## `geom_smooth()` using formula 'y ~ x'
## Warning in doTryCatch(return(expr), name, parentenv, handler): display list
## redraw incomplete
## Warning in doTryCatch(return(expr), name, parentenv, handler): display list
## redraw incomplete## Attempting mixed linear model with: ~ condition + batch## Fitting the expressionset to the model, this is slow.##
## Total:72 s## Placing factor: condition at the beginning of the model.## Writing the median reads by factor.
zymo_de <- all_pairwise(macr, model_batch = FALSE, filter = TRUE)## Plotting a PCA before surrogate/batch inclusion.## Not putting labels on the PC plot.## Assuming no batch in model for testing pca.## Not putting labels on the PC plot.## Finished running DE analyses, collecting outputs.## Comparing analyses.
zymo_table <- combine_de_tables(zymo_de, excel = "images/macrophage_side_experiment/macrophage_de.xlsx")## Deleting the file images/macrophage_side_experiment/macrophage_de.xlsx before writing the tables.## Writing a legend of columns.## Printing a pca plot before/after surrogates/batch estimation.## Working on table 1/3: z22_vs_undef## Working on table 2/3: z23_vs_undef## Working on table 3/3: z23_vs_z22## Adding venn plots for z22_vs_undef.
## Limma expression coefficients for z22_vs_undef; R^2: 0.958; equation: y = 0.967x + 0.183## Deseq expression coefficients for z22_vs_undef; R^2: 0.957; equation: y = 1.02x - 0.215## Edger expression coefficients for z22_vs_undef; R^2: 0.957; equation: y = 1.02x - 0.192## Adding venn plots for z23_vs_undef.
## Limma expression coefficients for z23_vs_undef; R^2: 0.954; equation: y = 0.979x + 0.139## Deseq expression coefficients for z23_vs_undef; R^2: 0.95; equation: y = 0.967x + 0.351## Edger expression coefficients for z23_vs_undef; R^2: 0.95; equation: y = 0.967x + 0.312## Adding venn plots for z23_vs_z22.
## Limma expression coefficients for z23_vs_z22; R^2: 0.964; equation: y = 0.997x + 0.0348## Deseq expression coefficients for z23_vs_z22; R^2: 0.963; equation: y = 0.937x + 0.696## Edger expression coefficients for z23_vs_z22; R^2: 0.963; equation: y = 0.937x + 0.478## Writing summary information, compare_plot is: TRUE.## Performing save of images/macrophage_side_experiment/macrophage_de.xlsx.
8.5.1 Followup email from Olga
- Macrophages infected with 2.3 vs macrophagues infected with 2.3 + SbV
- Macrophages uninfected (M0) vs macrophages uninfected + SbV
- Macrophages uninfected vs macrophages infected with 2.3
- Macrophages uninfected + SbV vs Macrophagues infected with 2.3 + SbV
With this comparisons we can define the effect of infection with 2.3 and the drug effect with and without infection. In this moment we can’t evaluate the conditions with 2.2 because the samples are incomplete. Mariana will complete the samples in the next shipment.
new_factor <- paste0(pData(macr)[["macrdrug"]], "_", pData(macr)[["zymodeme"]])
new_factor <- gsub(x = new_factor, pattern = "undef", replacement = "uninf")
macr_v2 <- set_expt_conditions(macr, fact = new_factor)
macr_v2$conditions## [1] "no_uninf" "sb_uninf" "no_z2.3" "sb_z2.3" "no_z2.3" "sb_z2.3"
## [7] "no_z2.2" "sb_z2.2" "sb_z2.2" "no_z2.2" "no_z2.3" "sb_z2.3"macr_v2_norm <- normalize_expt(macr_v2, onvert = "cpm", filter = TRUE, norm = "quant")## This function will replace the expt$expressionset slot with:## quant(cbcb(data))## It will save copies of each step along the way
## in expt$normalized with the corresponding libsizes. Keep libsizes in mind
## when invoking limma. The appropriate libsize is non-log(cpm(normalized)).
## This is most likely kept at:
## 'new_expt$normalized$intermediate_counts$normalization$libsizes'
## A copy of this may also be found at:
## new_expt$best_libsize## Leaving the data in its current base format, keep in mind that
## some metrics are easier to see when the data is log2 transformed, but
## EdgeR/DESeq do not accept transformed data.## Leaving the data unconverted. It is often advisable to cpm/rpkm
## the data to normalize for sampling differences, keep in mind though that rpkm
## has some annoying biases, and voom() by default does a cpm (though hpgl_voom()
## will try to detect this).## Not correcting the count-data for batch effects. If batch is
## included in EdgerR/limma's model, then this is probably wise; but in extreme
## batch effects this is a good parameter to play with.## Step 1: performing count filter with option: cbcb## Removing 8899 low-count genes (11029 remaining).## Step 2: normalizing the data with quant.## Step 3: not converting the data.## The method is: raw.## Step 4: not doing batch correction.## Step 4: not transforming the data.macr_v2_norm <- normalize_expt(macr_v2_norm, transform = "log2")## This function will replace the expt$expressionset slot with:## log2(data)## It will save copies of each step along the way
## in expt$normalized with the corresponding libsizes. Keep libsizes in mind
## when invoking limma. The appropriate libsize is non-log(cpm(normalized)).
## This is most likely kept at:
## 'new_expt$normalized$intermediate_counts$normalization$libsizes'
## A copy of this may also be found at:
## new_expt$best_libsize## Filter is false, this should likely be set to something, good
## choices include cbcb, kofa, pofa (anything but FALSE). If you want this to
## stay FALSE, keep in mind that if other normalizations are performed, then the
## resulting libsizes are likely to be strange (potentially negative!)## Leaving the data unconverted. It is often advisable to cpm/rpkm
## the data to normalize for sampling differences, keep in mind though that rpkm
## has some annoying biases, and voom() by default does a cpm (though hpgl_voom()
## will try to detect this).## Leaving the data unnormalized. This is necessary for DESeq, but
## EdgeR/limma might benefit from normalization. Good choices include quantile,
## size-factor, tmm, etc.## Not correcting the count-data for batch effects. If batch is
## included in EdgerR/limma's model, then this is probably wise; but in extreme
## batch effects this is a good parameter to play with.## Step 1: not doing count filtering.## Step 2: not normalizing the data.## Step 3: not converting the data.## The method is: raw.## Step 4: not doing batch correction.## Step 4: transforming the data with log2.plot_pca(macr_v2_norm)$plot
keepers <- list(
"zymo_sbv" = c("sb_z22", "sb_z23"),
"uninf_drug" = c("no_uninf", "sb_uninf"),
"uninfnodrug_z23" = c("no_uninf", "no_z23"),
"uninfdrug_z23" = c("sb_uninf", "sb_z23"),
"z23_drug_nodrug" = c("sb_z23", "no_z23"),
"z22_drug_nodrug" = c("sb_z22", "no_z22")
)
olga_pairwise <- all_pairwise(macr_v2, do_deseq = FALSE, do_edger = FALSE, do_ebseq = FALSE)## Plotting a PCA before surrogate/batch inclusion.## Not putting labels on the PC plot.## Using limma's removeBatchEffect to visualize with(out) batch inclusion.## Not putting labels on the PC plot.## Finished running DE analyses, collecting outputs.## Comparing analyses.olga_zymo_sb <- olga_pairwise[["limma"]][["all_tables"]][["sb_z23_vs_sb_z22"]]
olga_zymo_sb <- merge(olga_zymo_sb, fData(macr_v2), by = "row.names")
rownames(olga_zymo_sb) <- olga_zymo_sb[["Row.names"]]
olga_zymo_sb[["Row.names"]] <- NULL
olga_zymo_sb[["transcript"]] <- NULL
written <- write_xlsx(data = olga_zymo_sb,
excel = glue::glue("excel/sb_23_vs22-v{ver}.xlsx"))## Saving to: excel/sb_23_vs22-v202103.xlsxolga_uninf_drug <- olga_pairwise[["limma"]][["all_tables"]][["sb_uninf_vs_no_uninf"]]
olga_uninf_drug <- merge(olga_uninf_drug, fData(macr_v2), by = "row.names")
rownames(olga_uninf_drug) <- olga_uninf_drug[["Row.names"]]
olga_uninf_drug[["Row.names"]] <- NULL
written <- write_xlsx(data = olga_uninf_drug,
excel = glue::glue("excel/uninf_sb_vs_nosb-v{ver}.xlsx"))## Saving to: excel/uninf_sb_vs_nosb-v202103.xlsxolga_uninfnodrug_z23 <- olga_pairwise[["limma"]][["all_tables"]][["no_z23_vs_no_uninf"]]
olga_uninfnodrug_z23 <- merge(olga_uninfnodrug_z23, fData(macr_v2), by = "row.names")
rownames(olga_uninfnodrug_z23) <- olga_uninfnodrug_z23[["Row.names"]]
olga_uninfnodrug_z23[["Row.names"]] <- NULL
written <- write_xlsx(data = olga_uninfnodrug_z23,
excel = glue::glue("excel/no_z23_vs_nosb_uninf-v{ver}.xlsx"))## Saving to: excel/no_z23_vs_nosb_uninf-v202103.xlsxolga_uninfdrug_z23 <- olga_pairwise[["limma"]][["all_tables"]][["sb_z23_vs_sb_uninf"]]
olga_uninfdrug_z23 <- merge(olga_uninfdrug_z23, fData(macr_v2), by = "row.names")
rownames(olga_uninfdrug_z23) <- olga_uninfdrug_z23[["Row.names"]]
olga_uninfdrug_z23[["Row.names"]] <- NULL
written <- write_xlsx(data = olga_uninfdrug_z23,
excel = glue::glue("excel/sb_z23_vs_sb_uninf-v{ver}.xlsx"))## Saving to: excel/sb_z23_vs_sb_uninf-v202103.xlsxolga_z23_drugnodrug <- olga_pairwise[["limma"]][["all_tables"]][["sb_z23_vs_no_z23"]]
olga_z23_drugnodrug <- merge(olga_z23_drugnodrug, fData(macr_v2), by = "row.names")
rownames(olga_z23_drugnodrug) <- olga_z23_drugnodrug[["Row.names"]]
olga_z23_drugnodrug[["Row.names"]] <- NULL
olga_z23_drugnodrug[["transcript"]] <- NULL
written <- write_xlsx(data = olga_z23_drugnodrug,
excel = glue::glue("excel/z23_drug_vs_nodrug-v{ver}.xlsx"))## Saving to: excel/z23_drug_vs_nodrug-v202103.xlsxolga_z22_drugnodrug <- olga_pairwise[["limma"]][["all_tables"]][["sb_z22_vs_no_z22"]]
olga_z22_drugnodrug <- merge(olga_z22_drugnodrug, fData(macr_v2), by = "row.names")
rownames(olga_z22_drugnodrug) <- olga_z22_drugnodrug[["Row.names"]]
olga_z22_drugnodrug[["Row.names"]] <- NULL
olga_z22_drugnodrug[["transcript"]] <- NULL
written <- write_xlsx(data = olga_z22_drugnodrug,
excel = glue::glue("excel/z22_drug_vs_nodrug-v{ver}.xlsx"))## Saving to: excel/z22_drug_vs_nodrug-v202103.xlsx8.6 Donor effect
donor <- set_expt_conditions(hs_expt, fact = "donor")
donor <- set_expt_batches(donor, fact = "time")
save_result <- save(donor, file = "rda/donor_expt.rda")
test <- normalize_expt(donor, filter = TRUE, transform = "log2", convert = "cpm", norm = "quant")## This function will replace the expt$expressionset slot with:## log2(cpm(quant(cbcb(data))))## It will save copies of each step along the way
## in expt$normalized with the corresponding libsizes. Keep libsizes in mind
## when invoking limma. The appropriate libsize is non-log(cpm(normalized)).
## This is most likely kept at:
## 'new_expt$normalized$intermediate_counts$normalization$libsizes'
## A copy of this may also be found at:
## new_expt$best_libsize## Not correcting the count-data for batch effects. If batch is
## included in EdgerR/limma's model, then this is probably wise; but in extreme
## batch effects this is a good parameter to play with.## Step 1: performing count filter with option: cbcb## Removing 4889 low-count genes (15039 remaining).## Step 2: normalizing the data with quant.## Step 3: converting the data with cpm.## The method is: raw.## Step 4: not doing batch correction.## Step 4: transforming the data with log2.## transform_counts: Found 82 values equal to 0, adding 1 to the matrix.plot_pca(test)$plot## Potentially check over the experimental design, there appear to be missing values.## Warning in plot_pca(test): There are NA values in the component data. This can
## lead to weird plotting errors.## plot labels was not set and there are more than 100 samples, disabling it.## Not putting labels on the PC plot.## Warning in MASS::cov.trob(data[, vars]): Probable convergence failure## Warning in MASS::cov.trob(data[, vars]): Probable convergence failure
## Warning in MASS::cov.trob(data[, vars]): Probable convergence failure
## Warning in MASS::cov.trob(data[, vars]): Probable convergence failure
## Warning in MASS::cov.trob(data[, vars]): Probable convergence failure
## Warning in MASS::cov.trob(data[, vars]): Probable convergence failure
## Warning in MASS::cov.trob(data[, vars]): Probable convergence failure
## Warning in MASS::cov.trob(data[, vars]): Probable convergence failure## Warning: Removed 21 rows containing missing values (geom_point).
8.7 Question 1
I interpreted question 1 as: pull out tmrc3000[1-6] and look at them.
I am not entirely certain what is meant by the heirarchical clustering request. I can see a couple of possibilities for what this means. The most similar thing I recall in the cruzi context was a post-DE visualization of some fairly specific genes.
hs_q1 <- subset_expt(hs_valid, subset = "donor=='d1010'|donor=='d1011'")## Using a subset expression.## There were 104, now there are 6 samples.q1_norm <- sm(normalize_expt(hs_q1, norm = "quant", transform = "log2", convert = "cpm", batch = FALSE,
filter = TRUE))
q1_pca <- plot_pca(q1_norm)
q1_pca$plot
knitr::kable(q1_pca$table)| sampleid | condition | batch | batch_int | colors | labels | PC1 | PC2 | pc_1 | pc_2 | pc_3 | pc_4 | pc_5 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1010-2 | 1010-2 | PBMC | d1010 | 1 | #1B9E77 | 1010-2 | -0.5218 | 0.3678 | -0.5218 | 0.3678 | 0.3820 | -0.1944 | 0.4920 |
| 1010-7 | 1010-7 | PBMC | d1010 | 1 | #1B9E77 | 1010-7 | 0.1079 | 0.4854 | 0.1079 | 0.4854 | -0.5377 | 0.5431 | 0.0449 |
| 1010-12 | 1010-12 | PBMC | d1010 | 1 | #1B9E77 | 1010-12 | 0.4706 | 0.3590 | 0.4706 | 0.3590 | 0.3162 | -0.3669 | -0.4984 |
| 1011-2 | 1011-2 | PBMC | d1011 | 2 | #1B9E77 | 1011-2 | -0.5452 | -0.3253 | -0.5452 | -0.3253 | -0.3798 | -0.2445 | -0.4756 |
| 1011-7 | 1011-7 | PBMC | d1011 | 2 | #1B9E77 | 1011-7 | 0.0469 | -0.4685 | 0.0469 | -0.4685 | 0.4948 | 0.5995 | -0.0862 |
| 1011-12 | 1011-12 | PBMC | d1011 | 2 | #1B9E77 | 1011-12 | 0.4417 | -0.4183 | 0.4417 | -0.4183 | -0.2755 | -0.3368 | 0.5233 |
write.csv(q1_pca$table, file = "coords/q1_pca_coords.csv")
## Looks like PC1 == Time and PC2 is donor, and they have pretty much the same amount of variance
hs_time <- set_expt_conditions(hs_q1, fact = "time")
time_norm <- sm(normalize_expt(hs_time, filter = TRUE))
time_norm <- normalize_expt(time_norm, transform = "log2")## This function will replace the expt$expressionset slot with:## log2(data)## It will save copies of each step along the way
## in expt$normalized with the corresponding libsizes. Keep libsizes in mind
## when invoking limma. The appropriate libsize is non-log(cpm(normalized)).
## This is most likely kept at:
## 'new_expt$normalized$intermediate_counts$normalization$libsizes'
## A copy of this may also be found at:
## new_expt$best_libsize## Filter is false, this should likely be set to something, good
## choices include cbcb, kofa, pofa (anything but FALSE). If you want this to
## stay FALSE, keep in mind that if other normalizations are performed, then the
## resulting libsizes are likely to be strange (potentially negative!)## Leaving the data unconverted. It is often advisable to cpm/rpkm
## the data to normalize for sampling differences, keep in mind though that rpkm
## has some annoying biases, and voom() by default does a cpm (though hpgl_voom()
## will try to detect this).## Leaving the data unnormalized. This is necessary for DESeq, but
## EdgeR/limma might benefit from normalization. Good choices include quantile,
## size-factor, tmm, etc.## Not correcting the count-data for batch effects. If batch is
## included in EdgerR/limma's model, then this is probably wise; but in extreme
## batch effects this is a good parameter to play with.## Step 1: not doing count filtering.## Step 2: not normalizing the data.## Step 3: not converting the data.## The method is: raw.## Step 4: not doing batch correction.## Step 4: transforming the data with log2.## transform_counts: Found 21 values equal to 0, adding 1 to the matrix.## Get a set of genes with high PC loads for PC1(time), and PC2(donor):
high_scores <- pca_highscores(time_norm, n = 40)
time_stuff <- high_scores$highest[, c(1, 2)]
time_stuff## Comp.1 Comp.2
## [1,] "34.47:ENSG00000129824" "11.18:ENSG00000073756"
## [2,] "34.4:ENSG00000012817" "8.806:ENSG00000125538"
## [3,] "30.72:ENSG00000067048" "8.515:ENSG00000093134"
## [4,] "27.17:ENSG00000114374" "7.41:ENSG00000112299"
## [5,] "26.06:ENSG00000198692" "6.848:ENSG00000168329"
## [6,] "24.77:ENSG00000067646" "6.815:ENSG00000012817"
## [7,] "22.01:ENSG00000099725" "6.657:ENSG00000143365"
## [8,] "15.41:ENSG00000241945" "6.231:ENSG00000123700"
## [9,] "14.24:ENSG00000112139" "6.224:ENSG00000140092"
## [10,] "13.89:ENSG00000160201" "6.114:ENSG00000143450"
## [11,] "13.56:ENSG00000183878" "5.851:ENSG00000081041"
## [12,] "12.46:ENSG00000144115" "5.806:ENSG00000126243"
## [13,] "12.22:ENSG00000080007" "5.584:ENSG00000263002"
## [14,] "12.14:ENSG00000049247" "5.563:ENSG00000123975"
## [15,] "11.95:ENSG00000204001" "5.462:ENSG00000171860"
## [16,] "11.66:ENSG00000248905" "5.447:ENSG00000163568"
## [17,] "10.98:ENSG00000075391" "5.423:ENSG00000180626"
## [18,] "10.58:ENSG00000198848" "5.399:ENSG00000198435"
## [19,] "9.471:ENSG00000154620" "5.389:ENSG00000125703"
## [20,] "8.654:ENSG00000129422" "5.375:ENSG00000171115"
## [21,] "8.03:ENSG00000131459" "5.291:ENSG00000162739"
## [22,] "7.825:ENSG00000168874" "5.288:ENSG00000133574"
## [23,] "7.498:ENSG00000196436" "5.282:ENSG00000129824"
## [24,] "7.219:ENSG00000109321" "5.19:ENSG00000188886"
## [25,] "7.147:ENSG00000107731" "5.141:ENSG00000250510"
## [26,] "7.112:ENSG00000186193" "5.092:ENSG00000185220"
## [27,] "6.866:ENSG00000214102" "4.987:ENSG00000106351"
## [28,] "6.857:ENSG00000134531" "4.962:ENSG00000282804"
## [29,] "6.73:ENSG00000177990" "4.956:ENSG00000163563"
## [30,] "6.699:ENSG00000179915" "4.916:ENSG00000164011"
## [31,] "6.585:ENSG00000136848" "4.908:ENSG00000164530"
## [32,] "6.567:ENSG00000276085" "4.889:ENSG00000121797"
## [33,] "6.317:ENSG00000004809" "4.84:ENSG00000163508"
## [34,] "6.215:ENSG00000144130" "4.826:ENSG00000165181"
## [35,] "6.215:ENSG00000171451" "4.706:ENSG00000168310"
## [36,] "6.207:ENSG00000169507" "4.665:ENSG00000138061"
## [37,] "6.116:ENSG00000168765" "4.663:ENSG00000091106"
## [38,] "6.097:ENSG00000160307" "4.648:ENSG00000213694"
## [39,] "6.041:ENSG00000169071" "4.641:ENSG00000161298"
## [40,] "5.927:ENSG00000197134" "4.633:ENSG00000197044"## Column 1 should be winners vs. time and column 2 by donor.
time_genes <- gsub(x = time_stuff[, "Comp.1"], pattern = "^.*:(.*)$", replacement = "\\1")
head(time_genes)## [1] "ENSG00000129824" "ENSG00000012817" "ENSG00000067048" "ENSG00000114374"
## [5] "ENSG00000198692" "ENSG00000067646"time_subset <- exprs(time_norm)[time_genes, ]
plot_sample_heatmap(time_norm, row_label = rownames(time_norm))
hs_donor <- set_expt_conditions(hs_q1, fact = "donor")
donor_norm <- sm(normalize_expt(hs_donor, convert = "cpm", filter = TRUE))
donor_norm <- normalize_expt(donor_norm, transform = "log2")## This function will replace the expt$expressionset slot with:## log2(data)## It will save copies of each step along the way
## in expt$normalized with the corresponding libsizes. Keep libsizes in mind
## when invoking limma. The appropriate libsize is non-log(cpm(normalized)).
## This is most likely kept at:
## 'new_expt$normalized$intermediate_counts$normalization$libsizes'
## A copy of this may also be found at:
## new_expt$best_libsize## Filter is false, this should likely be set to something, good
## choices include cbcb, kofa, pofa (anything but FALSE). If you want this to
## stay FALSE, keep in mind that if other normalizations are performed, then the
## resulting libsizes are likely to be strange (potentially negative!)## Leaving the data unconverted. It is often advisable to cpm/rpkm
## the data to normalize for sampling differences, keep in mind though that rpkm
## has some annoying biases, and voom() by default does a cpm (though hpgl_voom()
## will try to detect this).## Leaving the data unnormalized. This is necessary for DESeq, but
## EdgeR/limma might benefit from normalization. Good choices include quantile,
## size-factor, tmm, etc.## Not correcting the count-data for batch effects. If batch is
## included in EdgerR/limma's model, then this is probably wise; but in extreme
## batch effects this is a good parameter to play with.## Step 1: not doing count filtering.## Step 2: not normalizing the data.## Step 3: not converting the data.## The method is: raw.## Step 4: not doing batch correction.## Step 4: transforming the data with log2.## transform_counts: Found 21 values equal to 0, adding 1 to the matrix.## Get a set of genes with high PC loads for PC1(donor), and PC2(donor):
high_scores <- pca_highscores(donor_norm, n = 40)
donor_stuff <- high_scores$highest[, c(1, 2)]
donor_stuff## Comp.1 Comp.2
## [1,] "11.76:ENSG00000073756" "27.69:ENSG00000129824"
## [2,] "9.371:ENSG00000125538" "25.06:ENSG00000012817"
## [3,] "9.104:ENSG00000106565" "24.87:ENSG00000067048"
## [4,] "8.987:ENSG00000176092" "21.81:ENSG00000114374"
## [5,] "8.124:ENSG00000093134" "19.73:ENSG00000099725"
## [6,] "7.922:ENSG00000168329" "16.12:ENSG00000198692"
## [7,] "7.225:ENSG00000185736" "15.76:ENSG00000067646"
## [8,] "6.558:ENSG00000111728" "13.52:ENSG00000183878"
## [9,] "6.204:ENSG00000125618" "11.32:ENSG00000112139"
## [10,] "6.166:ENSG00000106351" "10.31:ENSG00000248905"
## [11,] "6.018:ENSG00000002933" "10.19:ENSG00000075391"
## [12,] "6.014:ENSG00000111863" "9.597:ENSG00000198848"
## [13,] "5.993:ENSG00000112299" "9.343:ENSG00000049247"
## [14,] "5.96:ENSG00000127954" "8.426:ENSG00000109321"
## [15,] "5.87:ENSG00000160185" "8.374:ENSG00000276085"
## [16,] "5.85:ENSG00000008517" "8.101:ENSG00000129422"
## [17,] "5.746:ENSG00000165617" "7.123:ENSG00000241945"
## [18,] "5.674:ENSG00000133574" "7.107:ENSG00000144115"
## [19,] "5.666:ENSG00000127585" "6.5:ENSG00000137959"
## [20,] "5.611:ENSG00000171051" "6.134:ENSG00000171860"
## [21,] "5.599:ENSG00000165272" "6.029:ENSG00000171451"
## [22,] "5.59:ENSG00000164530" "5.863:ENSG00000101916"
## [23,] "5.486:ENSG00000131747" "5.748:ENSG00000196209"
## [24,] "5.385:ENSG00000126243" "5.703:ENSG00000093217"
## [25,] "5.299:ENSG00000162739" "5.693:ENSG00000112299"
## [26,] "5.209:ENSG00000143167" "5.673:ENSG00000073756"
## [27,] "5.191:ENSG00000123358" "5.668:ENSG00000090382"
## [28,] "5.178:ENSG00000113070" "5.607:ENSG00000088827"
## [29,] "5.163:ENSG00000168280" "5.499:ENSG00000138829"
## [30,] "5.152:ENSG00000124216" "5.458:ENSG00000131459"
## [31,] "5.106:ENSG00000196329" "5.458:ENSG00000004799"
## [32,] "5.085:ENSG00000165899" "5.42:ENSG00000168765"
## [33,] "5.069:ENSG00000261150" "5.419:ENSG00000179344"
## [34,] "5.048:ENSG00000100721" "5.41:ENSG00000154620"
## [35,] "5.041:ENSG00000203972" "5.385:ENSG00000122025"
## [36,] "5.027:ENSG00000171848" "5.321:ENSG00000123700"
## [37,] "4.998:ENSG00000119640" "5.258:ENSG00000163563"
## [38,] "4.995:ENSG00000107317" "5.18:ENSG00000204001"
## [39,] "4.989:ENSG00000177301" "5.123:ENSG00000160307"
## [40,] "4.985:ENSG00000036448" "5.115:ENSG00000080007"## Column 1 should be winners vs. donor and column 2 by donor.
donor_genes <- gsub(x = donor_stuff[, "Comp.1"], pattern = "^.*:(.*)$", replacement = "\\1")
head(donor_genes)## [1] "ENSG00000073756" "ENSG00000125538" "ENSG00000106565" "ENSG00000176092"
## [5] "ENSG00000093134" "ENSG00000168329"donor_subset <- exprs(donor_norm)[donor_genes, ]
plot_sample_heatmap(donor_norm, row_label = rownames(donor_norm))
time_keepers <- list(
"2hr_vs_7hr" = c("t2hr", "t7hr"),
"2hr_vs_12hr" = c("t2hr", "t12hr"),
"7hr_vs_12hr" = c("t7hr", "t12hr"))
q1_time <- set_expt_conditions(hs_q1, fact = "time")
time_de <- sm(all_pairwise(q1_time, model_batch = FALSE, parallel = FALSE))time_all_tables <- sm(combine_de_tables(time_de,
excel = glue::glue("excel/time_de_tables-v{ver}.xlsx")))save_result <- save(time_all_tables, file = "rda/time_all_tables.rda")
time_all_tables_all <- sm(combine_de_tables(
time_de,
keepers = "all",
excel = glue::glue("excel/time_de_all_tables-v{ver}.xlsx")))time_all_tables <- sm(combine_de_tables(
time_de,
keepers = time_keepers,
excel = glue::glue("excel/{rundate}-time_de_tables-v{ver}.xlsx")))q1_donor <- set_expt_conditions(hs_q1, fact = "donor")
donor_de <- sm(all_pairwise(q1_donor, model_batch = FALSE))
donor_tables <- sm(combine_de_tables(
donor_de, excel = glue::glue("excel/donor_de_tables-v{ver}.xlsx")))save_result <- save(donor_tables, file = "rda/donor_tables.rda")hs_q2 <- subset_expt(hs_valid, subset = "donor!='d1010'&donor!='d1011'")## Using a subset expression.## There were 104, now there are 77 samples.q2_norm <- sm(normalize_expt(hs_q2, transform = "log2", convert = "cpm", norm = "quant", filter = TRUE))
q2_pca <- plot_pca(q2_norm)
knitr::kable(q2_pca$table)| sampleid | condition | batch | batch_int | colors | labels | PC1 | PC2 | pc_1 | pc_2 | pc_3 | pc_4 | pc_5 | pc_6 | pc_7 | pc_8 | pc_9 | pc_10 | pc_11 | pc_12 | pc_13 | pc_14 | pc_15 | pc_16 | pc_17 | pc_18 | pc_19 | pc_20 | pc_21 | pc_22 | pc_23 | pc_24 | pc_25 | pc_26 | pc_27 | pc_28 | pc_29 | pc_30 | pc_31 | pc_32 | pc_33 | pc_34 | pc_35 | pc_36 | pc_37 | pc_38 | pc_39 | pc_40 | pc_41 | pc_42 | pc_43 | pc_44 | pc_45 | pc_46 | pc_47 | pc_48 | pc_49 | pc_50 | pc_51 | pc_52 | pc_53 | pc_54 | pc_55 | pc_56 | pc_57 | pc_58 | pc_59 | pc_60 | pc_61 | pc_62 | pc_63 | pc_64 | pc_65 | pc_66 | pc_67 | pc_68 | pc_69 | pc_70 | pc_71 | pc_72 | pc_73 | pc_74 | pc_75 | pc_76 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1017m1 | 1017m1 | Monocyte | d1017 | 1 | #7570B3 | 1017m1 | -0.0276 | -0.0971 | -0.0276 | -0.0971 | -0.0284 | -0.1389 | -0.1672 | 0.1670 | 0.2158 | -0.0157 | 0.0520 | -0.1030 | 0.0350 | -0.1461 | 0.0232 | 0.0480 | -0.1293 | 0.0711 | -0.1063 | 0.0173 | -0.0802 | 0.1264 | -0.0545 | 0.1639 | -0.0386 | 0.0182 | -0.2063 | 0.0137 | -0.0100 | 0.0522 | -0.1391 | -0.0276 | 0.0774 | -0.0125 | -0.0383 | -0.0874 | 0.1118 | 0.0836 | 0.1280 | -0.1960 | -0.3668 | -0.3208 | 0.1980 | 0.1404 | 0.3204 | -0.0157 | -0.1579 | -0.1122 | -0.0837 | -0.1541 | 0.1737 | 0.0495 | 0.0094 | 0.0389 | -0.1355 | -0.0331 | -0.0511 | -0.0489 | -0.0068 | -0.0205 | -0.0065 | -0.1331 | -0.0405 | 0.0009 | 0.0411 | 0.0937 | 0.0654 | 0.0176 | 0.0047 | -0.0706 | -0.0473 | -0.0299 | 0.0000 | -0.0497 | -0.0142 | -0.0107 | -0.0241 | -0.0019 |
| 1034n1 | 1034n1 | Neutrophil | d1034 | 2 | #D95F02 | 1034n1 | 0.1424 | 0.0868 | 0.1424 | 0.0868 | -0.0941 | 0.0386 | -0.0669 | 0.2053 | 0.0944 | -0.0154 | -0.2093 | 0.1022 | 0.0641 | 0.0434 | 0.1286 | 0.0524 | -0.2392 | -0.1682 | 0.0661 | -0.0132 | 0.1563 | -0.1295 | -0.0090 | -0.1772 | -0.0251 | -0.1736 | 0.2966 | -0.0216 | 0.1045 | -0.0487 | 0.0304 | -0.1755 | -0.0060 | -0.0081 | -0.1164 | -0.1683 | -0.0585 | -0.1417 | 0.4213 | 0.0536 | -0.0729 | -0.2662 | 0.1050 | -0.1080 | -0.3131 | -0.0300 | 0.0276 | 0.0950 | 0.0164 | -0.1383 | 0.0157 | -0.0448 | -0.0026 | 0.0638 | 0.0247 | -0.0414 | 0.0144 | -0.0230 | -0.0414 | -0.0141 | -0.0302 | -0.0174 | 0.0271 | -0.0034 | 0.0063 | -0.0104 | -0.0066 | 0.0026 | -0.0033 | 0.0051 | -0.0015 | -0.0107 | 0.0043 | 0.0092 | 0.0055 | 0.0013 | -0.0006 | 0.0043 |
| 1034bp1 | 1034bp1 | Biopsy | d1034 | 2 | #E7298A | 1034bp1 | -0.1714 | 0.2089 | -0.1714 | 0.2089 | 0.0841 | -0.0421 | 0.1599 | 0.0747 | 0.1092 | 0.1332 | -0.0719 | -0.1149 | -0.1123 | -0.0401 | 0.0289 | 0.0840 | -0.0235 | 0.0182 | -0.0757 | 0.0505 | -0.0371 | 0.1188 | 0.0101 | -0.1000 | 0.1582 | 0.0295 | -0.0015 | 0.0454 | 0.0188 | 0.0762 | -0.0018 | -0.0258 | 0.0373 | -0.2166 | 0.1456 | 0.0656 | -0.3253 | 0.1591 | -0.0515 | 0.5891 | 0.0327 | -0.1744 | 0.1733 | 0.0227 | 0.0711 | 0.1274 | 0.0519 | -0.0261 | -0.2710 | 0.0169 | -0.0244 | 0.0488 | -0.0811 | -0.0678 | 0.0391 | -0.0338 | 0.0044 | -0.0112 | -0.0009 | 0.0075 | 0.0068 | -0.0211 | 0.0048 | -0.0224 | 0.0100 | 0.0058 | 0.0035 | 0.0176 | 0.0001 | -0.0167 | -0.0116 | 0.0085 | -0.0022 | -0.0050 | -0.0010 | 0.0104 | 0.0007 | -0.0098 |
| 1034n2 | 1034n2 | Neutrophil | d1034 | 2 | #D95F02 | 1034n2 | 0.1431 | 0.1022 | 0.1431 | 0.1022 | -0.1111 | 0.0414 | -0.0267 | 0.2238 | 0.1085 | -0.0702 | -0.2612 | 0.1573 | -0.0062 | 0.1568 | 0.2058 | -0.0362 | -0.2314 | -0.0615 | -0.0290 | 0.0008 | 0.1879 | -0.0996 | -0.2148 | -0.2532 | -0.1297 | 0.2300 | 0.0730 | 0.0457 | -0.0341 | -0.0857 | -0.3022 | 0.0987 | 0.0058 | -0.1501 | 0.1491 | 0.0937 | 0.1459 | 0.2177 | -0.1670 | -0.0616 | 0.0667 | 0.2152 | -0.0531 | 0.0288 | 0.2439 | 0.0289 | -0.0405 | -0.1436 | 0.0160 | 0.0958 | -0.0532 | -0.0272 | 0.0251 | -0.0677 | -0.0012 | 0.0083 | -0.0050 | -0.0146 | -0.0093 | -0.0206 | 0.0218 | 0.0120 | -0.0371 | -0.0104 | 0.0018 | -0.0129 | 0.0208 | 0.0025 | -0.0093 | 0.0004 | 0.0036 | -0.0024 | -0.0065 | -0.0005 | -0.0026 | 0.0091 | 0.0015 | 0.0003 |
| 1034m2 | 1034m2 | Monocyte | d1034 | 2 | #7570B3 | 1034m2 | -0.0292 | -0.0951 | -0.0292 | -0.0951 | -0.0520 | -0.1202 | -0.1989 | 0.2833 | 0.1362 | 0.0693 | -0.0445 | -0.0385 | 0.0279 | -0.1559 | 0.0306 | 0.0314 | -0.1958 | -0.0037 | -0.1497 | 0.0708 | -0.1859 | -0.0198 | -0.0036 | 0.1099 | 0.0079 | 0.0409 | -0.0220 | 0.1262 | -0.0125 | 0.0169 | 0.2144 | 0.0243 | -0.0188 | 0.0940 | -0.0556 | 0.0667 | -0.0979 | -0.0523 | -0.1053 | -0.0013 | 0.1614 | 0.1849 | -0.0303 | 0.0111 | -0.0988 | -0.0492 | 0.0114 | 0.0723 | 0.0080 | -0.0199 | -0.0423 | 0.0650 | 0.0532 | 0.0807 | 0.0987 | 0.0586 | -0.0075 | -0.0006 | -0.0629 | 0.0273 | 0.2185 | -0.0329 | -0.0009 | -0.4125 | -0.3432 | -0.0071 | 0.1109 | -0.3061 | 0.0695 | 0.0485 | 0.0567 | -0.0138 | -0.0366 | 0.0093 | -0.0148 | -0.0185 | 0.0134 | -0.0104 |
| 1034m2- | 1034m2- | Monocyte | d1034 | 2 | #7570B3 | 1034m2- | -0.0236 | -0.0935 | -0.0236 | -0.0935 | -0.0441 | -0.1165 | -0.2076 | 0.2817 | 0.1576 | 0.0828 | -0.0634 | -0.0367 | 0.0336 | -0.1620 | 0.0483 | -0.0580 | -0.1706 | -0.0123 | -0.1460 | 0.0615 | -0.1992 | -0.0228 | 0.0316 | 0.1336 | -0.0233 | 0.0481 | -0.0413 | 0.0031 | 0.0652 | -0.0213 | 0.1384 | 0.0073 | -0.0849 | 0.1076 | -0.0339 | 0.0577 | -0.0629 | -0.0546 | -0.1614 | 0.0245 | 0.1280 | 0.1385 | -0.0225 | -0.0462 | -0.1121 | -0.0241 | 0.0419 | 0.0947 | -0.0022 | 0.0752 | -0.0044 | -0.0646 | -0.0395 | -0.0476 | 0.0027 | -0.0252 | 0.0271 | 0.0051 | 0.1316 | -0.0020 | -0.2679 | 0.1746 | 0.0107 | 0.4069 | 0.2859 | -0.0565 | -0.1283 | 0.3118 | -0.0759 | -0.0465 | -0.0189 | 0.0056 | -0.0002 | 0.0178 | 0.0168 | 0.0060 | -0.0253 | 0.0150 |
| 2050bp1 | 2050bp1 | Biopsy | d2050 | 3 | #E7298A | 2050bp1 | -0.1625 | 0.1718 | -0.1625 | 0.1718 | 0.0764 | -0.0363 | 0.0740 | 0.0231 | -0.0413 | -0.3089 | 0.1068 | 0.0997 | 0.2895 | -0.2090 | -0.0259 | -0.3039 | -0.2148 | 0.3831 | 0.0435 | -0.1349 | 0.1281 | -0.0237 | -0.0627 | 0.0397 | 0.1086 | -0.0387 | 0.0795 | -0.0823 | -0.0189 | 0.1719 | -0.0718 | 0.0960 | 0.1488 | 0.0781 | -0.1532 | -0.1835 | -0.1141 | 0.1330 | -0.0401 | 0.0498 | 0.0310 | -0.0024 | 0.0183 | -0.1611 | 0.0624 | -0.2327 | 0.1178 | 0.0713 | 0.0865 | 0.1516 | -0.0691 | 0.0381 | 0.0194 | 0.0230 | -0.0289 | -0.0295 | 0.0140 | 0.0052 | -0.0290 | -0.0259 | 0.0408 | -0.0213 | 0.0119 | 0.0008 | -0.0044 | 0.0077 | -0.0032 | 0.0233 | -0.0018 | 0.0139 | -0.0327 | 0.0088 | -0.0310 | 0.0422 | -0.0020 | 0.0154 | -0.0065 | -0.0034 |
| 2052bp1 | 2052bp1 | Biopsy | d2052 | 4 | #E7298A | 2052bp1 | -0.1677 | 0.2193 | -0.1677 | 0.2193 | 0.0955 | -0.0442 | 0.1887 | 0.0420 | 0.1229 | 0.1694 | -0.0605 | -0.1607 | -0.1959 | -0.0206 | 0.0142 | 0.0669 | 0.0229 | 0.0728 | -0.0074 | 0.0000 | 0.0759 | 0.1076 | 0.0109 | 0.0346 | 0.2567 | 0.1705 | 0.1157 | -0.0628 | 0.1065 | -0.1256 | -0.0425 | 0.0284 | 0.3053 | 0.1950 | 0.0493 | -0.2620 | -0.0446 | 0.0531 | 0.0087 | -0.3023 | -0.0535 | 0.2805 | 0.0213 | -0.0402 | -0.2701 | 0.2069 | -0.0461 | -0.2115 | 0.0791 | -0.1091 | 0.0693 | 0.0302 | -0.0139 | 0.0156 | -0.0687 | -0.0057 | -0.0036 | 0.0240 | -0.0006 | 0.0170 | -0.0396 | 0.0005 | -0.0397 | 0.0109 | 0.0073 | -0.0123 | 0.0063 | -0.0082 | 0.0189 | -0.0114 | -0.0084 | -0.0045 | 0.0036 | 0.0016 | 0.0037 | -0.0070 | 0.0058 | 0.0005 |
| 2052n1 | 2052n1 | Neutrophil | d2052 | 4 | #D95F02 | 2052n1 | 0.1280 | 0.0996 | 0.1280 | 0.0996 | -0.0846 | 0.0114 | -0.0361 | -0.1213 | -0.2387 | 0.2052 | 0.4090 | -0.3442 | -0.0181 | 0.0459 | 0.1687 | -0.2886 | -0.4024 | -0.1869 | 0.2877 | 0.3626 | 0.0011 | 0.1003 | -0.0477 | -0.0508 | -0.0146 | 0.0145 | -0.0464 | 0.0321 | -0.0295 | -0.0072 | -0.0232 | 0.0263 | -0.0150 | 0.0051 | -0.0056 | -0.0480 | 0.0123 | 0.0006 | -0.0172 | 0.0245 | -0.0020 | 0.0201 | 0.0051 | -0.0126 | 0.0032 | 0.0109 | -0.0092 | -0.0046 | 0.0042 | -0.0009 | 0.0076 | 0.0032 | 0.0013 | 0.0302 | 0.0114 | 0.0042 | 0.0144 | 0.0131 | 0.0057 | 0.0055 | -0.0112 | -0.0163 | 0.0085 | -0.0034 | -0.0009 | -0.0056 | -0.0013 | 0.0012 | -0.0006 | 0.0009 | 0.0037 | -0.0009 | 0.0009 | -0.0019 | -0.0019 | 0.0005 | 0.0001 | -0.0021 |
| 2052e1 | 2052e1 | Eosinophil | d2052 | 4 | #66A61E | 2052e1 | 0.0857 | -0.0687 | 0.0857 | -0.0687 | 0.2089 | 0.0648 | -0.0025 | -0.0530 | -0.0968 | 0.0578 | 0.1890 | -0.0375 | -0.0163 | 0.0853 | -0.0377 | 0.2618 | -0.1103 | 0.1932 | -0.0564 | -0.1113 | 0.0958 | 0.0150 | -0.3428 | -0.0781 | -0.0719 | -0.1503 | 0.1048 | 0.2368 | 0.2874 | 0.0529 | 0.0043 | -0.2320 | 0.1697 | 0.2674 | 0.1472 | 0.3287 | -0.0019 | -0.0401 | -0.0598 | -0.0571 | 0.1035 | 0.0148 | 0.1374 | -0.0898 | 0.0586 | -0.0696 | -0.0624 | 0.0859 | -0.0708 | 0.0411 | 0.1179 | -0.0104 | -0.0953 | 0.1179 | -0.0285 | -0.0569 | -0.0061 | 0.0196 | 0.0091 | -0.0408 | -0.0231 | 0.0203 | -0.0655 | 0.0716 | -0.0025 | 0.0214 | -0.0098 | -0.0430 | 0.0223 | 0.0359 | -0.0228 | 0.0485 | 0.0011 | -0.0176 | -0.0153 | 0.0134 | -0.0282 | 0.0102 |
| 2052n2 | 2052n2 | Neutrophil | d2052 | 4 | #D95F02 | 2052n2 | 0.1421 | 0.0987 | 0.1421 | 0.0987 | -0.1084 | 0.0314 | -0.0340 | 0.0072 | -0.0989 | 0.0903 | 0.1641 | -0.1082 | -0.0671 | 0.2595 | 0.1976 | -0.1182 | -0.0127 | 0.0764 | -0.1471 | -0.5813 | -0.3877 | -0.0533 | 0.1776 | 0.1309 | 0.1338 | -0.0560 | 0.2324 | 0.1708 | -0.0699 | 0.0186 | -0.1192 | -0.0423 | -0.0693 | -0.1568 | -0.0250 | -0.0700 | 0.0950 | 0.0200 | -0.0401 | -0.0043 | -0.0193 | 0.0254 | 0.0901 | -0.0716 | 0.0254 | 0.0487 | -0.0159 | 0.0046 | 0.0152 | -0.0159 | 0.0131 | -0.0046 | 0.0293 | 0.0146 | 0.0232 | 0.0179 | 0.0387 | -0.0013 | 0.0161 | 0.0156 | 0.0088 | 0.0092 | -0.0038 | -0.0006 | -0.0011 | -0.0188 | 0.0053 | -0.0059 | -0.0023 | 0.0013 | 0.0083 | -0.0060 | 0.0027 | -0.0044 | -0.0052 | -0.0010 | 0.0019 | 0.0011 |
| 2052m3 | 2052m3 | Monocyte | d2052 | 4 | #7570B3 | 2052m3 | -0.0219 | -0.0814 | -0.0219 | -0.0814 | -0.0473 | -0.1587 | -0.0229 | 0.0299 | -0.0944 | 0.0943 | 0.1642 | -0.0428 | -0.0325 | 0.0749 | 0.0055 | -0.0135 | 0.1087 | 0.1024 | -0.1361 | -0.0763 | 0.1672 | -0.0851 | -0.0395 | -0.1896 | -0.1405 | 0.1834 | 0.1194 | -0.3877 | 0.0954 | -0.0587 | 0.1570 | -0.0604 | -0.0620 | 0.0786 | -0.0030 | -0.2211 | -0.0862 | -0.0892 | -0.1291 | -0.0874 | -0.0509 | -0.0446 | 0.1582 | 0.1224 | 0.1427 | 0.1047 | -0.1479 | 0.2283 | -0.1532 | 0.1098 | -0.0138 | -0.0434 | 0.2019 | -0.1107 | 0.3247 | -0.0571 | 0.1629 | -0.0958 | 0.0648 | -0.0003 | 0.0487 | -0.0265 | 0.0534 | -0.0811 | 0.0230 | -0.0167 | -0.0350 | 0.0365 | -0.0518 | 0.0439 | 0.0358 | -0.0845 | -0.0061 | -0.0102 | -0.0442 | 0.0092 | 0.0251 | -0.0007 |
| 2052n3 | 2052n3 | Neutrophil | d2052 | 4 | #D95F02 | 2052n3 | 0.1418 | 0.1078 | 0.1418 | 0.1078 | -0.1006 | 0.0244 | 0.0210 | 0.0170 | -0.0508 | 0.0362 | 0.0994 | -0.0210 | -0.0835 | 0.1434 | 0.1753 | -0.0753 | 0.0362 | 0.1310 | -0.1776 | -0.1579 | -0.0231 | -0.0345 | -0.0662 | -0.0384 | -0.0708 | 0.1462 | -0.2375 | -0.1950 | 0.0501 | -0.0673 | 0.1778 | 0.0991 | 0.1079 | 0.1768 | 0.1534 | 0.0950 | -0.1736 | 0.0262 | 0.2847 | 0.0510 | 0.0429 | -0.0927 | -0.2290 | 0.3205 | -0.0546 | -0.2098 | 0.0690 | 0.0010 | 0.0855 | -0.0912 | -0.2061 | -0.0287 | -0.0970 | -0.0889 | -0.1900 | 0.0337 | -0.2786 | -0.0224 | -0.0561 | 0.0006 | 0.0300 | 0.0532 | 0.0330 | 0.0021 | 0.0122 | 0.0649 | 0.0084 | 0.0182 | 0.0066 | -0.0219 | -0.0088 | 0.0184 | -0.0160 | 0.0147 | 0.0267 | -0.0075 | 0.0037 | -0.0006 |
| 2065m1 | 2065m1 | Monocyte | d2065 | 5 | #7570B3 | 2065m1 | -0.0404 | -0.0964 | -0.0404 | -0.0964 | -0.0379 | -0.1637 | 0.1367 | -0.1773 | -0.1881 | 0.0108 | -0.1628 | 0.1814 | -0.0303 | -0.0372 | 0.1438 | 0.0522 | -0.1144 | 0.0516 | 0.0596 | 0.0425 | -0.1040 | 0.0348 | 0.0781 | -0.0382 | -0.0385 | 0.1012 | 0.1602 | 0.1370 | 0.0330 | 0.0005 | 0.2415 | 0.0800 | 0.0222 | 0.0137 | -0.0390 | 0.0552 | -0.0571 | -0.0375 | -0.0567 | -0.0423 | -0.1498 | -0.0548 | -0.0238 | -0.0361 | 0.0424 | -0.0412 | 0.0658 | -0.1001 | -0.1373 | 0.0821 | 0.1542 | -0.1754 | 0.2275 | -0.1258 | -0.3416 | 0.0499 | -0.1596 | -0.0983 | 0.1727 | 0.0457 | -0.0422 | -0.2375 | 0.0063 | 0.0116 | -0.0980 | -0.2180 | -0.1713 | -0.0126 | -0.0586 | 0.1391 | 0.1735 | -0.0513 | 0.0944 | 0.0958 | -0.0378 | 0.0135 | 0.0074 | 0.0075 |
| 2065n1 | 2065n1 | Neutrophil | d2065 | 5 | #D95F02 | 2065n1 | 0.1201 | 0.0865 | 0.1201 | 0.0865 | -0.0904 | -0.0051 | 0.0716 | -0.1122 | -0.1647 | 0.0941 | -0.0439 | 0.0537 | -0.0111 | -0.2606 | -0.0214 | 0.0467 | -0.0040 | -0.0234 | -0.1666 | 0.1563 | -0.1252 | -0.3886 | 0.0466 | -0.1047 | 0.1967 | 0.0068 | -0.0782 | -0.0206 | 0.0628 | 0.0719 | -0.1178 | -0.1729 | 0.0846 | 0.0092 | -0.1351 | 0.0404 | 0.0185 | -0.0657 | -0.0926 | -0.1201 | 0.0705 | -0.1893 | -0.0272 | -0.0678 | 0.0797 | 0.1150 | 0.1030 | -0.0777 | 0.0254 | -0.0383 | -0.0019 | 0.0050 | 0.0376 | -0.2163 | -0.0176 | 0.0669 | -0.0165 | -0.0342 | 0.0061 | 0.0459 | 0.3444 | 0.2506 | -0.1988 | 0.1001 | 0.0173 | 0.1506 | -0.1248 | -0.0325 | 0.1137 | -0.0626 | -0.0813 | -0.0688 | -0.0440 | 0.0095 | 0.0152 | 0.0036 | 0.0107 | 0.0052 |
| 2065bp1 | 2065bp1 | Biopsy | d2065 | 5 | #E7298A | 2065bp1 | -0.1719 | 0.2202 | -0.1719 | 0.2202 | 0.0713 | -0.0499 | 0.3050 | 0.0361 | 0.1691 | 0.2660 | -0.1198 | -0.1136 | -0.0856 | 0.0234 | -0.0070 | -0.0199 | -0.0019 | 0.1287 | 0.3154 | -0.0656 | 0.0086 | -0.4003 | -0.0655 | 0.2783 | -0.3789 | -0.1416 | -0.0572 | 0.0152 | -0.0718 | -0.1268 | 0.0370 | 0.0146 | 0.0241 | -0.0458 | -0.2045 | 0.1072 | 0.1440 | -0.0748 | -0.0615 | 0.0239 | -0.0164 | -0.0116 | -0.0034 | 0.1044 | 0.0452 | -0.0741 | -0.0245 | 0.0956 | -0.0224 | -0.0176 | -0.0229 | -0.0209 | 0.0162 | -0.0078 | 0.0262 | -0.0018 | -0.0080 | 0.0070 | -0.0196 | -0.0184 | -0.0007 | -0.0030 | -0.0062 | -0.0102 | -0.0073 | 0.0095 | 0.0009 | 0.0035 | -0.0182 | -0.0036 | 0.0072 | 0.0029 | -0.0077 | -0.0008 | 0.0022 | 0.0091 | -0.0007 | 0.0032 |
| 2066m1 | 2066m1 | Monocyte | d2066 | 6 | #7570B3 | 2066m1 | -0.0369 | -0.1035 | -0.0369 | -0.1035 | -0.0348 | -0.1648 | 0.0510 | 0.0174 | 0.0008 | -0.0346 | 0.0852 | -0.0392 | -0.0187 | 0.0001 | 0.0262 | 0.0434 | 0.0509 | -0.1108 | -0.1094 | -0.0720 | 0.0346 | 0.1166 | -0.0566 | -0.1937 | -0.1416 | -0.2925 | -0.1478 | -0.0868 | -0.1321 | 0.0461 | -0.1760 | 0.0511 | 0.1752 | -0.0308 | -0.1586 | -0.0130 | 0.0239 | 0.0610 | -0.1609 | 0.0205 | 0.0415 | -0.0023 | 0.0478 | -0.1002 | -0.1659 | 0.0542 | -0.0948 | 0.1346 | 0.0426 | -0.1505 | -0.0488 | -0.1231 | -0.0201 | -0.2663 | -0.3119 | 0.0632 | 0.0874 | 0.0562 | 0.1911 | -0.1657 | -0.0552 | 0.2295 | 0.1821 | -0.2160 | 0.0973 | -0.1063 | 0.0389 | -0.1323 | -0.0638 | -0.0198 | 0.0553 | 0.1428 | 0.0150 | -0.0069 | 0.0543 | 0.0021 | -0.0044 | -0.0040 |
| 2066n1 | 2066n1 | Neutrophil | d2066 | 6 | #D95F02 | 2066n1 | 0.1402 | 0.0810 | 0.1402 | 0.0810 | -0.0893 | 0.0183 | 0.0043 | -0.0075 | 0.0237 | -0.0171 | 0.0433 | -0.0395 | 0.0257 | -0.1002 | 0.0477 | 0.1028 | -0.0300 | -0.1238 | -0.1017 | -0.1629 | 0.0904 | 0.1214 | 0.1210 | -0.0240 | -0.1687 | -0.3795 | -0.0999 | -0.2072 | 0.0464 | 0.0531 | 0.0477 | 0.1024 | 0.1320 | 0.0129 | -0.1496 | 0.0486 | -0.0324 | -0.0231 | 0.0197 | 0.1094 | -0.0113 | 0.1434 | -0.2488 | 0.0410 | 0.0048 | 0.0194 | -0.0048 | -0.4033 | -0.0926 | 0.2669 | 0.3406 | 0.0567 | 0.0775 | 0.0807 | 0.1876 | -0.0594 | 0.0115 | -0.0454 | -0.0757 | 0.0672 | 0.0007 | 0.0000 | -0.0261 | -0.0094 | 0.0355 | 0.0347 | -0.1279 | -0.0137 | 0.0466 | 0.0140 | 0.0048 | -0.0205 | -0.0111 | -0.0089 | -0.0318 | 0.0009 | -0.0098 | -0.0040 |
| 2066bp1 | 2066bp1 | Biopsy | d2066 | 6 | #E7298A | 2066bp1 | -0.1676 | 0.2239 | -0.1676 | 0.2239 | 0.0800 | -0.0434 | 0.0777 | 0.0210 | 0.0530 | 0.1426 | -0.0660 | -0.1277 | -0.1316 | 0.0445 | 0.0144 | 0.1359 | 0.0420 | -0.1003 | -0.1442 | 0.0497 | 0.0203 | 0.2141 | 0.0397 | -0.1429 | -0.0531 | -0.0725 | 0.0460 | -0.0163 | 0.0015 | 0.4164 | -0.1269 | 0.0500 | -0.3997 | 0.0774 | 0.0180 | 0.0010 | 0.1148 | -0.0482 | 0.0075 | -0.2491 | 0.0275 | -0.0742 | 0.0510 | 0.0977 | 0.0043 | -0.2311 | 0.3490 | 0.0542 | 0.1295 | 0.1672 | -0.0728 | 0.0081 | 0.0747 | 0.0527 | -0.0279 | -0.0274 | 0.0012 | 0.0085 | 0.0122 | -0.0107 | 0.0408 | -0.0090 | 0.0355 | 0.0162 | -0.0069 | 0.0038 | -0.0186 | -0.0172 | -0.0172 | 0.0252 | -0.0207 | -0.0171 | 0.0054 | 0.0125 | -0.0001 | -0.0033 | -0.0030 | 0.0068 |
| 2065m2 | 2065m2 | Monocyte | d2065 | 5 | #7570B3 | 2065m2 | -0.0363 | -0.1123 | -0.0363 | -0.1123 | -0.0341 | -0.1564 | 0.1233 | -0.2155 | -0.0372 | -0.0458 | -0.1172 | 0.1743 | -0.0431 | -0.0559 | 0.1886 | 0.0528 | -0.1132 | 0.0883 | 0.1052 | -0.0062 | -0.1697 | 0.1340 | 0.0826 | -0.0003 | -0.1553 | 0.1638 | -0.0279 | 0.0856 | 0.1135 | 0.0264 | 0.1191 | 0.0265 | -0.0303 | 0.0848 | 0.0532 | -0.0458 | 0.1290 | -0.0655 | 0.1133 | 0.0479 | -0.2321 | -0.0039 | 0.0126 | -0.0767 | -0.0333 | 0.1439 | -0.0170 | -0.0818 | -0.0298 | 0.2986 | -0.1784 | -0.0776 | -0.1860 | 0.0942 | 0.0961 | -0.0518 | 0.1297 | 0.1294 | -0.1059 | -0.1759 | 0.0602 | 0.2959 | 0.0729 | -0.1143 | 0.1365 | 0.1058 | 0.1419 | -0.0685 | 0.0089 | -0.1191 | -0.1724 | 0.0631 | -0.0720 | -0.0722 | 0.0418 | -0.0134 | -0.0156 | -0.0214 |
| 2065n2 | 2065n2 | Neutrophil | d2065 | 5 | #D95F02 | 2065n2 | 0.1333 | 0.0862 | 0.1333 | 0.0862 | -0.0903 | 0.0110 | 0.0900 | -0.1931 | 0.0002 | 0.0034 | 0.0019 | 0.0463 | -0.0088 | -0.1868 | -0.0412 | 0.0462 | 0.0823 | 0.1272 | -0.1937 | 0.1269 | -0.2597 | -0.1731 | -0.0232 | -0.2023 | -0.1403 | 0.1758 | -0.1732 | 0.0367 | -0.0225 | -0.0120 | -0.1785 | -0.0981 | -0.0094 | 0.1588 | -0.0320 | -0.1688 | 0.2418 | 0.1207 | 0.1520 | 0.1989 | 0.1550 | 0.0700 | 0.0028 | -0.0272 | -0.0861 | -0.0158 | 0.0447 | 0.0742 | 0.0151 | -0.0457 | 0.2017 | 0.1474 | 0.0152 | 0.1416 | 0.0763 | 0.0026 | 0.0765 | 0.0596 | 0.0451 | -0.0006 | -0.2544 | -0.2588 | 0.1512 | -0.0216 | -0.0463 | -0.0820 | 0.1024 | 0.0092 | -0.0767 | 0.0508 | 0.0567 | 0.0459 | 0.0377 | -0.0021 | -0.0126 | -0.0044 | -0.0054 | -0.0044 |
| 2066m2 | 2066m2 | Monocyte | d2066 | 6 | #7570B3 | 2066m2 | -0.0320 | -0.1007 | -0.0320 | -0.1007 | -0.0335 | -0.1565 | 0.0607 | -0.1962 | -0.0793 | 0.0195 | -0.0962 | 0.1195 | -0.0696 | -0.0774 | 0.1643 | 0.0929 | -0.1111 | 0.0472 | -0.0529 | -0.0561 | -0.0197 | 0.2236 | -0.0382 | -0.0827 | -0.1435 | -0.1545 | -0.0788 | -0.1300 | -0.0453 | 0.0640 | -0.0086 | 0.0591 | 0.0522 | -0.1762 | -0.0601 | 0.0239 | -0.0056 | -0.0072 | -0.1731 | -0.0309 | 0.0084 | 0.0520 | 0.0396 | -0.0866 | -0.1053 | 0.0752 | -0.1107 | 0.1461 | 0.1618 | -0.2817 | -0.2562 | 0.0595 | -0.1304 | 0.1074 | 0.1785 | 0.0566 | -0.1166 | -0.0210 | -0.1707 | 0.1985 | 0.0373 | -0.2653 | -0.2182 | 0.2327 | -0.1150 | 0.0692 | 0.0346 | 0.1396 | 0.0707 | 0.0087 | 0.0010 | -0.0689 | 0.0096 | -0.0043 | -0.0218 | -0.0015 | 0.0176 | 0.0016 |
| 2068m1 | 2068m1 | Monocyte | d2068 | 7 | #7570B3 | 2068m1 | -0.0406 | -0.1006 | -0.0406 | -0.1006 | -0.0282 | -0.1771 | 0.1103 | -0.1913 | 0.0209 | -0.1175 | -0.1637 | 0.0379 | -0.0028 | 0.1957 | 0.0251 | -0.0207 | -0.1194 | -0.0824 | 0.0567 | 0.0330 | -0.0224 | -0.0161 | 0.0710 | 0.0792 | 0.1672 | -0.0941 | -0.0042 | -0.0132 | 0.0403 | -0.0202 | -0.0230 | -0.0446 | -0.0438 | 0.1557 | 0.0353 | 0.1561 | -0.2044 | 0.1665 | 0.0798 | 0.0087 | -0.0666 | 0.1611 | 0.0102 | 0.1440 | 0.1746 | -0.0888 | 0.1188 | 0.1517 | 0.1229 | -0.2780 | 0.2276 | 0.1157 | 0.2394 | -0.0078 | 0.1458 | 0.2722 | 0.2228 | -0.0081 | -0.1732 | -0.0403 | -0.0645 | 0.1300 | 0.0221 | -0.0636 | 0.1881 | 0.0933 | 0.0115 | 0.0275 | 0.0293 | 0.0314 | 0.0437 | 0.0414 | 0.0330 | 0.0575 | 0.0756 | 0.0355 | 0.0083 | 0.0038 |
| 2068n1 | 2068n1 | Neutrophil | d2068 | 7 | #D95F02 | 2068n1 | 0.1309 | 0.0732 | 0.1309 | 0.0732 | -0.0742 | 0.0017 | 0.0585 | -0.1799 | 0.0862 | -0.0767 | -0.0347 | -0.0854 | 0.0698 | -0.0721 | -0.2189 | 0.0260 | -0.0638 | -0.0507 | -0.1500 | 0.1300 | -0.0662 | -0.0738 | 0.0604 | 0.0606 | 0.0208 | -0.1289 | 0.2120 | -0.0740 | -0.0582 | -0.0292 | -0.0726 | -0.1195 | 0.0036 | -0.1227 | 0.0189 | 0.1111 | -0.1915 | -0.0049 | 0.1250 | -0.1637 | -0.1098 | 0.1933 | 0.1017 | 0.1486 | 0.0524 | -0.0982 | -0.2501 | 0.0127 | -0.1157 | 0.2191 | -0.2795 | 0.0128 | -0.0688 | -0.0160 | -0.0419 | 0.1036 | -0.0836 | 0.4069 | 0.1570 | 0.1707 | -0.0960 | -0.0772 | 0.1182 | -0.0503 | 0.0149 | 0.0451 | -0.0177 | 0.0146 | 0.0657 | 0.0099 | 0.0255 | -0.0183 | 0.0148 | 0.0256 | 0.0121 | 0.0021 | -0.0078 | 0.0096 |
| 2068e1 | 2068e1 | Eosinophil | d2068 | 7 | #66A61E | 2068e1 | 0.0798 | -0.0641 | 0.0798 | -0.0641 | 0.2084 | 0.0453 | 0.0071 | -0.1740 | 0.0488 | -0.0093 | -0.1444 | -0.0052 | 0.0116 | 0.0262 | -0.0442 | -0.1372 | -0.0326 | -0.1077 | -0.1636 | 0.0759 | -0.0939 | -0.1311 | -0.1640 | 0.0448 | 0.1022 | -0.1695 | 0.0566 | -0.0064 | 0.0476 | -0.0449 | 0.0327 | 0.2772 | -0.0152 | -0.1000 | 0.1543 | -0.2706 | 0.0024 | 0.0264 | -0.0992 | -0.0118 | -0.0281 | -0.0397 | -0.0598 | 0.2440 | -0.0196 | 0.0091 | -0.0153 | 0.2015 | 0.0431 | 0.0524 | 0.2116 | -0.2082 | -0.2861 | 0.2127 | 0.0262 | -0.0468 | -0.0272 | -0.0656 | 0.1062 | -0.0973 | 0.1549 | -0.0248 | -0.1616 | -0.0966 | 0.0321 | -0.0516 | -0.1591 | 0.0260 | -0.0473 | 0.0194 | -0.0400 | 0.2414 | 0.0397 | -0.0412 | -0.0496 | -0.0014 | 0.0003 | -0.0060 |
| 2068bp1 | 2068bp1 | Biopsy | d2068 | 7 | #E7298A | 2068bp1 | -0.1653 | 0.1961 | -0.1653 | 0.1961 | 0.0758 | -0.0321 | -0.2075 | -0.0387 | -0.1678 | -0.3516 | 0.0171 | -0.0012 | 0.0221 | 0.0068 | -0.0005 | -0.0567 | -0.0100 | 0.0396 | 0.0012 | 0.0099 | 0.0272 | 0.0401 | 0.0018 | 0.0733 | -0.0373 | 0.0944 | 0.1114 | 0.0156 | 0.2285 | 0.0487 | -0.1564 | 0.0946 | -0.1304 | 0.0193 | -0.2686 | 0.1848 | 0.1388 | -0.2006 | 0.0253 | 0.0673 | 0.1012 | 0.0697 | -0.0054 | 0.4071 | -0.1212 | 0.3269 | 0.0160 | -0.0223 | -0.2103 | -0.1977 | 0.0132 | -0.0233 | -0.0594 | -0.0528 | -0.0574 | -0.0239 | 0.0135 | 0.0387 | -0.0041 | 0.0260 | -0.0276 | -0.0216 | -0.0314 | -0.0173 | -0.0095 | 0.0105 | -0.0056 | -0.0195 | -0.0172 | -0.0251 | -0.0168 | -0.0103 | 0.0140 | -0.0184 | 0.0115 | 0.0121 | 0.0014 | 0.0171 |
| 2072m1 | 2072m1 | Monocyte | d2072 | 9 | #7570B3 | 2072m1 | -0.0198 | -0.1005 | -0.0198 | -0.1005 | -0.0361 | -0.1550 | -0.1895 | -0.2126 | 0.0336 | 0.1425 | -0.1277 | 0.1072 | -0.0114 | 0.0347 | 0.0396 | 0.0629 | -0.0590 | 0.1462 | 0.2200 | -0.0092 | 0.0061 | 0.0699 | -0.0175 | 0.1612 | 0.1441 | 0.0085 | -0.0092 | -0.1117 | 0.0220 | 0.0866 | -0.2033 | -0.1134 | 0.0464 | -0.1731 | 0.2000 | -0.0840 | 0.0249 | -0.1993 | 0.0479 | 0.0057 | 0.2104 | 0.0248 | -0.1423 | 0.0638 | -0.0391 | -0.2930 | -0.0524 | -0.0796 | -0.0572 | -0.0878 | 0.0682 | 0.1148 | -0.0298 | -0.2260 | 0.0964 | -0.3891 | 0.0652 | -0.0315 | 0.2092 | 0.0733 | 0.0109 | 0.0561 | 0.0719 | -0.0191 | -0.0450 | 0.0297 | 0.0152 | -0.0702 | -0.0442 | -0.0337 | 0.0730 | 0.0429 | -0.0074 | -0.0113 | 0.0522 | 0.0052 | -0.0214 | -0.0050 |
| 2072n1 | 2072n1 | Neutrophil | d2072 | 9 | #D95F02 | 2072n1 | 0.1402 | 0.0749 | 0.1402 | 0.0749 | -0.0848 | 0.0184 | -0.1371 | -0.1783 | 0.1131 | 0.0697 | -0.0160 | -0.0576 | 0.0741 | -0.1554 | -0.1554 | 0.1005 | 0.0091 | 0.0716 | 0.0757 | -0.0150 | 0.0577 | -0.0354 | 0.2541 | -0.0799 | 0.0040 | -0.0798 | 0.0269 | 0.1522 | 0.0234 | -0.1062 | -0.1296 | 0.1569 | -0.0083 | 0.0587 | 0.0721 | -0.0283 | -0.1405 | -0.1677 | -0.0524 | -0.0529 | 0.0807 | 0.0403 | 0.1182 | 0.0169 | 0.1747 | 0.1062 | -0.0089 | 0.0304 | 0.0806 | 0.0035 | -0.2266 | -0.0277 | -0.0254 | 0.0068 | -0.0586 | -0.0838 | -0.1252 | -0.4576 | -0.2809 | -0.1761 | -0.2571 | 0.0126 | 0.1175 | -0.1090 | 0.0200 | -0.0049 | -0.1388 | -0.0402 | 0.0030 | 0.0401 | -0.0051 | 0.0026 | -0.0173 | 0.0044 | 0.0076 | -0.0002 | -0.0074 | -0.0038 |
| 2072e1 | 2072e1 | Eosinophil | d2072 | 9 | #66A61E | 2072e1 | 0.0689 | -0.0492 | 0.0689 | -0.0492 | 0.1760 | 0.0340 | -0.1636 | -0.1924 | 0.1023 | 0.1573 | -0.2275 | -0.0519 | -0.0299 | -0.2241 | 0.1830 | -0.5762 | 0.3195 | -0.1380 | -0.0869 | -0.0691 | 0.1638 | 0.1245 | -0.0295 | 0.0353 | -0.1155 | -0.0178 | 0.0882 | 0.1156 | 0.1038 | -0.0006 | -0.0144 | -0.1760 | 0.0552 | 0.1227 | 0.0182 | 0.1332 | 0.0362 | 0.0662 | -0.0231 | 0.0190 | -0.0150 | -0.0582 | 0.0368 | -0.0543 | -0.0299 | -0.0284 | -0.0327 | -0.0572 | -0.0475 | -0.0142 | -0.0842 | 0.1314 | 0.0956 | 0.0156 | -0.0089 | 0.0424 | 0.0044 | -0.0257 | -0.0021 | 0.0384 | 0.0728 | -0.0075 | 0.0520 | -0.0790 | 0.0033 | -0.0511 | -0.0145 | 0.0714 | -0.0209 | -0.0439 | 0.0092 | -0.0294 | -0.0116 | -0.0085 | 0.0096 | 0.0052 | -0.0034 | 0.0140 |
| 2072bp1 | 2072bp1 | Biopsy | d2072 | 9 | #E7298A | 2072bp1 | -0.1648 | 0.1925 | -0.1648 | 0.1925 | 0.0592 | -0.0241 | -0.2801 | -0.0373 | -0.1418 | -0.2109 | 0.0243 | 0.0642 | 0.0666 | 0.0232 | 0.0030 | 0.0067 | 0.0219 | -0.0175 | 0.0763 | -0.0023 | -0.0825 | -0.1885 | 0.0169 | -0.0576 | -0.1637 | -0.1754 | -0.1711 | -0.0267 | -0.2185 | -0.1310 | 0.1517 | -0.1747 | 0.0177 | -0.0445 | 0.4224 | -0.0241 | -0.0061 | 0.0887 | -0.1304 | -0.1047 | -0.1317 | 0.0511 | 0.3205 | 0.0546 | -0.2363 | 0.0219 | 0.2328 | -0.1887 | -0.0161 | -0.0025 | 0.0007 | 0.0674 | 0.0609 | 0.0042 | 0.0396 | -0.0205 | -0.0475 | -0.0223 | 0.0314 | 0.0033 | 0.0118 | 0.0156 | -0.0223 | -0.0094 | 0.0300 | 0.0019 | 0.0017 | -0.0016 | -0.0024 | -0.0004 | 0.0125 | -0.0033 | 0.0022 | -0.0080 | -0.0060 | -0.0198 | -0.0051 | -0.0113 |
| 2071bp1 | 2071bp1 | Biopsy | d2071 | 8 | #E7298A | 2071bp1 | -0.1560 | 0.1967 | -0.1560 | 0.1967 | 0.0691 | -0.0295 | -0.3518 | -0.0469 | -0.2021 | -0.1465 | -0.0904 | -0.1302 | -0.2398 | 0.0826 | 0.0442 | 0.2151 | 0.1510 | -0.1453 | -0.0366 | 0.0955 | -0.0329 | 0.0410 | 0.0190 | 0.0195 | 0.0259 | 0.1107 | 0.0036 | 0.0646 | 0.1036 | -0.2803 | 0.0036 | 0.0040 | 0.2618 | -0.1246 | -0.2835 | -0.0415 | 0.0208 | 0.0425 | -0.0179 | 0.0752 | -0.0687 | -0.0559 | -0.0544 | -0.1323 | 0.1416 | -0.3381 | -0.0301 | 0.1289 | 0.1664 | 0.1703 | -0.0304 | -0.0069 | -0.0265 | 0.0549 | 0.0693 | 0.0445 | 0.0041 | -0.0255 | -0.0091 | 0.0170 | -0.0124 | 0.0425 | 0.0208 | 0.0021 | 0.0101 | -0.0170 | 0.0090 | 0.0097 | 0.0005 | -0.0007 | 0.0119 | 0.0001 | 0.0227 | -0.0181 | -0.0163 | -0.0116 | -0.0034 | -0.0045 |
| 2073m1 | 2073m1 | Monocyte | d2073 | 10 | #7570B3 | 2073m1 | -0.0319 | -0.1114 | -0.0319 | -0.1114 | -0.0334 | -0.1614 | 0.0095 | -0.0023 | -0.0658 | -0.0001 | 0.0274 | 0.0201 | -0.0152 | -0.0709 | -0.0229 | 0.0470 | 0.0701 | -0.1236 | 0.1114 | -0.0688 | -0.0298 | -0.0150 | -0.1239 | 0.0090 | 0.0559 | -0.0672 | 0.0686 | 0.0923 | -0.1373 | -0.0780 | -0.0850 | 0.1094 | 0.0915 | -0.0400 | 0.0618 | 0.0323 | -0.1060 | 0.1171 | 0.1203 | -0.1005 | 0.1362 | -0.0543 | -0.1545 | 0.1071 | -0.0528 | 0.0759 | 0.0835 | 0.1520 | -0.0903 | 0.0865 | 0.0162 | 0.1000 | 0.1730 | 0.0157 | -0.0486 | -0.0122 | 0.0295 | 0.0384 | 0.0105 | -0.1003 | -0.0372 | -0.0438 | 0.0756 | 0.1016 | -0.1112 | -0.1045 | 0.0347 | 0.0370 | -0.0144 | -0.0240 | -0.4826 | -0.2478 | -0.2199 | -0.2177 | -0.3626 | -0.1235 | 0.0029 | -0.0131 |
| 2073n1 | 2073n1 | Neutrophil | d2073 | 10 | #D95F02 | 2073n1 | 0.1355 | 0.0712 | 0.1355 | 0.0712 | -0.0888 | 0.0156 | 0.0329 | 0.0084 | -0.0648 | -0.0184 | 0.0294 | 0.0032 | 0.0039 | -0.2330 | -0.0665 | 0.1121 | 0.0321 | -0.1673 | 0.1097 | -0.0967 | 0.0467 | -0.0509 | -0.0028 | 0.0742 | 0.1414 | -0.0549 | 0.0644 | -0.0688 | 0.1031 | 0.0216 | 0.0823 | -0.0628 | -0.0675 | 0.0244 | 0.0043 | 0.0034 | -0.0242 | 0.0932 | -0.1405 | -0.0399 | -0.0495 | -0.0174 | -0.0581 | 0.1058 | 0.0712 | -0.0322 | -0.0003 | -0.0772 | -0.0377 | -0.0430 | -0.0646 | -0.0653 | -0.0040 | 0.0047 | -0.0002 | -0.0380 | -0.0140 | -0.0088 | -0.1298 | -0.0626 | 0.1127 | 0.0438 | 0.0406 | 0.1564 | -0.0861 | -0.2571 | 0.5855 | 0.1244 | -0.3406 | 0.0937 | 0.0870 | 0.2302 | 0.0725 | -0.0021 | 0.0034 | -0.0014 | 0.0174 | -0.0162 |
| 2073e1 | 2073e1 | Eosinophil | d2073 | 10 | #66A61E | 2073e1 | 0.0757 | -0.0689 | 0.0757 | -0.0689 | 0.2109 | 0.0503 | 0.0907 | 0.0014 | -0.0408 | -0.0681 | -0.0167 | -0.0139 | -0.0268 | -0.1291 | 0.0197 | -0.0491 | 0.0496 | -0.1803 | 0.0547 | -0.1218 | -0.0645 | -0.0250 | -0.0925 | -0.0531 | -0.0120 | 0.1220 | -0.0444 | -0.0868 | -0.1548 | -0.0207 | -0.0821 | 0.0542 | -0.0280 | -0.0464 | -0.0144 | 0.0057 | -0.1848 | -0.1897 | 0.0248 | 0.0079 | -0.0039 | 0.1219 | 0.1243 | 0.0331 | 0.0270 | -0.0620 | 0.0123 | 0.0622 | 0.0182 | 0.0380 | 0.2064 | -0.1225 | -0.1382 | 0.1260 | -0.0986 | -0.0813 | 0.0196 | -0.0497 | 0.0009 | -0.1041 | -0.0047 | 0.0684 | 0.0386 | 0.1507 | -0.0259 | 0.0217 | 0.2408 | -0.1199 | 0.1669 | -0.0919 | 0.2049 | -0.5243 | -0.0699 | 0.1184 | 0.2283 | 0.0418 | 0.0014 | -0.0046 |
| 2073bp1 | 2073bp1 | Biopsy | d2073 | 10 | #E7298A | 2073bp1 | -0.1584 | 0.1858 | -0.1584 | 0.1858 | 0.0537 | -0.0336 | -0.1210 | 0.0334 | -0.1476 | -0.1291 | -0.1017 | -0.1450 | -0.0604 | -0.0368 | -0.0030 | 0.0140 | 0.0529 | 0.0017 | 0.0304 | 0.0115 | -0.0423 | -0.1325 | -0.0232 | 0.0681 | -0.1563 | -0.0211 | 0.0402 | -0.0081 | -0.0920 | 0.2867 | 0.0371 | 0.0358 | -0.1918 | 0.1040 | 0.2794 | -0.0308 | -0.1447 | 0.0376 | 0.1786 | -0.0423 | 0.0723 | -0.0621 | -0.3239 | -0.3301 | 0.1359 | 0.1835 | -0.4307 | -0.0019 | 0.0667 | -0.0973 | 0.0733 | -0.0774 | -0.0267 | -0.0245 | -0.0012 | 0.0811 | 0.0144 | -0.0331 | 0.0129 | 0.0039 | -0.0119 | 0.0228 | 0.0199 | 0.0085 | -0.0064 | -0.0303 | 0.0064 | -0.0011 | 0.0377 | 0.0186 | 0.0451 | 0.0081 | 0.0149 | -0.0110 | -0.0053 | -0.0060 | 0.0048 | -0.0062 |
| 2068m2 | 2068m2 | Monocyte | d2068 | 7 | #7570B3 | 2068m2 | -0.0334 | -0.1010 | -0.0334 | -0.1010 | -0.0341 | -0.1657 | 0.1071 | 0.0781 | 0.1168 | -0.1777 | 0.0255 | -0.1172 | 0.0146 | 0.2091 | -0.1240 | -0.1306 | 0.0550 | -0.1600 | -0.0104 | 0.0347 | -0.0406 | -0.1157 | 0.1179 | -0.0363 | 0.0764 | -0.0694 | -0.1001 | 0.0345 | 0.0953 | 0.0043 | -0.1112 | -0.0596 | 0.0297 | 0.2757 | 0.0102 | 0.1412 | 0.0722 | 0.0073 | 0.0583 | 0.1463 | -0.2939 | 0.1472 | -0.0530 | -0.1412 | 0.0568 | 0.0492 | 0.1151 | 0.0243 | 0.1087 | -0.0045 | -0.1266 | -0.1438 | -0.1654 | -0.1750 | 0.1433 | -0.3482 | -0.0480 | -0.1204 | 0.1367 | 0.1111 | 0.1220 | -0.2014 | -0.0053 | 0.0581 | -0.0727 | -0.1457 | 0.0123 | -0.0294 | 0.0676 | 0.0851 | -0.0839 | -0.0687 | -0.0008 | 0.0013 | -0.0474 | -0.0125 | 0.0231 | -0.0089 |
| 2068n2 | 2068n2 | Neutrophil | d2068 | 7 | #D95F02 | 2068n2 | 0.1412 | 0.0906 | 0.1412 | 0.0906 | -0.0922 | 0.0164 | 0.1060 | 0.0055 | 0.1668 | -0.2018 | -0.0447 | -0.0338 | 0.0411 | 0.1913 | -0.1511 | -0.0667 | 0.0720 | 0.0265 | 0.0140 | 0.1594 | -0.2209 | 0.2902 | -0.1630 | 0.0723 | -0.1909 | 0.0561 | 0.2526 | -0.0437 | -0.2351 | 0.0156 | 0.1270 | -0.3463 | 0.1998 | 0.0138 | -0.0377 | -0.0876 | -0.0556 | -0.1688 | -0.1781 | -0.0352 | 0.1240 | -0.1730 | -0.1822 | 0.0543 | 0.1351 | 0.1473 | 0.1889 | -0.0508 | 0.1186 | -0.0122 | 0.0330 | 0.0246 | -0.0786 | -0.0286 | -0.0063 | 0.0063 | 0.0133 | -0.0839 | -0.0420 | -0.0341 | -0.0193 | 0.0145 | -0.0079 | -0.0002 | -0.0057 | 0.0061 | 0.0243 | 0.0157 | -0.0062 | -0.0036 | -0.0079 | 0.0116 | -0.0019 | 0.0060 | 0.0047 | -0.0015 | 0.0003 | 0.0006 |
| 2068e2 | 2068e2 | Eosinophil | d2068 | 7 | #66A61E | 2068e2 | 0.0760 | -0.0641 | 0.0760 | -0.0641 | 0.2192 | 0.0462 | 0.1636 | 0.0451 | 0.0940 | -0.2212 | -0.0560 | -0.0804 | 0.0064 | 0.2363 | -0.0785 | -0.0604 | -0.0580 | -0.1192 | -0.0042 | 0.0801 | -0.1920 | -0.0506 | 0.1212 | -0.0542 | 0.0457 | -0.0443 | -0.0881 | -0.1243 | 0.2088 | 0.0447 | 0.0570 | 0.2066 | 0.1213 | 0.0037 | 0.1193 | -0.0674 | 0.2098 | -0.1452 | -0.0724 | -0.0271 | 0.1377 | -0.1423 | 0.0316 | -0.1149 | -0.0386 | -0.1305 | -0.1583 | -0.0357 | -0.2637 | 0.0105 | -0.1928 | 0.3102 | 0.2609 | 0.0406 | -0.0572 | 0.0340 | -0.0155 | -0.0191 | -0.0931 | -0.0835 | -0.0487 | 0.0581 | -0.1085 | 0.0947 | -0.0271 | -0.1148 | 0.0592 | -0.0445 | 0.0638 | -0.0953 | 0.0860 | -0.0120 | -0.0097 | 0.0177 | -0.0444 | 0.0038 | 0.0002 | -0.0044 |
| 2072m2 | 2072m2 | Monocyte | d2072 | 9 | #7570B3 | 2072m2 | -0.0219 | -0.1033 | -0.0219 | -0.1033 | -0.0371 | -0.1584 | -0.0980 | 0.0636 | 0.0947 | 0.0161 | 0.0415 | -0.0722 | 0.0108 | 0.0571 | -0.0810 | -0.0566 | 0.1067 | 0.0716 | 0.0994 | -0.0027 | 0.0323 | -0.0393 | 0.0178 | -0.0560 | 0.0835 | 0.0187 | -0.0826 | -0.0448 | 0.0350 | 0.1019 | -0.1342 | -0.1358 | 0.1109 | -0.0749 | 0.1374 | -0.0323 | 0.1021 | -0.1115 | 0.0217 | 0.1144 | -0.0576 | 0.0431 | -0.0509 | 0.0437 | -0.0650 | -0.0474 | 0.0123 | -0.0465 | -0.0289 | 0.0838 | -0.0212 | -0.3382 | 0.0105 | 0.0962 | -0.0562 | 0.3272 | 0.1634 | 0.1297 | -0.2986 | -0.0483 | 0.0569 | -0.0307 | 0.0821 | 0.2359 | -0.3224 | 0.0317 | -0.2821 | -0.0710 | -0.2538 | -0.0468 | 0.0921 | -0.0098 | -0.0188 | -0.0527 | 0.0666 | 0.0023 | -0.0296 | 0.0127 |
| 2072n2 | 2072n2 | Neutrophil | d2072 | 9 | #D95F02 | 2072n2 | 0.1506 | 0.0928 | 0.1506 | 0.0928 | -0.1037 | 0.0328 | -0.1351 | -0.0304 | 0.1576 | 0.0071 | -0.0304 | 0.0039 | 0.0415 | 0.0537 | -0.0906 | 0.0575 | 0.1277 | 0.1291 | 0.1943 | -0.0188 | 0.0304 | 0.1339 | 0.1160 | -0.1357 | -0.0975 | -0.0045 | -0.0616 | 0.1719 | -0.0376 | -0.0816 | -0.0353 | 0.2376 | 0.0058 | 0.1893 | 0.0336 | -0.1439 | -0.0738 | -0.0747 | -0.0638 | 0.1032 | 0.0495 | -0.0188 | 0.0647 | -0.0476 | 0.0964 | 0.0588 | 0.0392 | 0.1356 | -0.0284 | -0.1250 | 0.0770 | -0.1549 | 0.2638 | 0.1751 | -0.0646 | -0.1098 | -0.1032 | 0.3375 | 0.0823 | 0.3358 | 0.2686 | 0.1666 | -0.0368 | 0.0712 | 0.0806 | 0.0180 | 0.0535 | 0.0369 | 0.0383 | -0.0241 | 0.0091 | 0.0089 | 0.0038 | 0.0029 | -0.0104 | -0.0111 | 0.0107 | -0.0019 |
| 2072e2 | 2072e2 | Eosinophil | d2072 | 9 | #66A61E | 2072e2 | 0.0946 | -0.0550 | 0.0946 | -0.0550 | 0.2093 | 0.0672 | -0.1351 | 0.0187 | 0.0565 | 0.0727 | -0.0448 | -0.0161 | 0.0218 | 0.0161 | -0.0114 | -0.0478 | 0.0743 | 0.0936 | 0.1621 | 0.0184 | -0.0208 | 0.0284 | 0.1526 | -0.1416 | 0.0499 | -0.0209 | -0.0049 | -0.0029 | 0.0083 | 0.1925 | 0.1870 | -0.0116 | 0.1016 | -0.1062 | -0.1278 | -0.0085 | 0.0862 | 0.1708 | 0.1271 | -0.0569 | 0.1033 | 0.0647 | 0.0530 | 0.0230 | 0.1169 | 0.0647 | 0.0753 | -0.0159 | 0.0409 | -0.0659 | 0.0403 | -0.2148 | 0.0340 | -0.2252 | 0.3051 | 0.0725 | -0.3342 | 0.2135 | 0.1169 | -0.3272 | -0.2079 | -0.0276 | -0.1769 | 0.0394 | -0.0185 | 0.0556 | 0.0984 | -0.1369 | 0.0383 | 0.0557 | -0.0351 | -0.0003 | -0.0074 | -0.0034 | -0.0122 | -0.0122 | -0.0266 | -0.0073 |
| 2073m2 | 2073m2 | Monocyte | d2073 | 10 | #7570B3 | 2073m2 | -0.0314 | -0.1030 | -0.0314 | -0.1030 | -0.0373 | -0.1571 | 0.0816 | 0.1280 | -0.0939 | -0.0300 | 0.0959 | -0.0336 | 0.0006 | -0.0607 | -0.1100 | -0.0148 | 0.1427 | -0.1059 | 0.0590 | -0.0183 | -0.0294 | -0.0517 | -0.0687 | -0.1100 | 0.0240 | 0.0183 | 0.1276 | 0.1738 | -0.0776 | 0.0260 | -0.0217 | 0.1048 | 0.0730 | -0.0392 | 0.0335 | 0.0736 | 0.0167 | 0.0586 | 0.0689 | -0.0640 | -0.0573 | -0.0061 | -0.1446 | -0.0341 | -0.1111 | 0.0260 | 0.0634 | 0.1148 | -0.0795 | 0.1181 | 0.0060 | 0.0516 | 0.0944 | -0.0153 | 0.0368 | -0.1195 | -0.1242 | -0.0859 | -0.0244 | 0.1410 | -0.1460 | -0.0711 | -0.2324 | -0.1782 | 0.0511 | 0.4292 | 0.0548 | 0.1313 | -0.2833 | -0.3005 | 0.2029 | 0.1131 | -0.1169 | 0.0673 | 0.1671 | 0.0804 | -0.0040 | 0.0051 |
| 2073n2 | 2073n2 | Neutrophil | d2073 | 10 | #D95F02 | 2073n2 | 0.1422 | 0.0899 | 0.1422 | 0.0899 | -0.1115 | 0.0311 | 0.0637 | 0.1093 | -0.0846 | -0.0528 | -0.0242 | 0.1003 | -0.0350 | -0.0636 | -0.0103 | 0.0353 | 0.1290 | -0.0893 | 0.1845 | -0.0344 | -0.0339 | 0.0187 | -0.1719 | 0.0444 | 0.1898 | 0.0573 | -0.0235 | -0.0311 | 0.0400 | 0.1344 | 0.0739 | -0.0650 | -0.1211 | 0.0544 | -0.0334 | -0.0318 | 0.1074 | 0.1762 | -0.3220 | -0.0276 | -0.1958 | -0.0593 | -0.0747 | 0.0008 | -0.1128 | -0.1127 | -0.0923 | 0.1086 | -0.1552 | -0.1337 | 0.0623 | 0.0226 | -0.0909 | 0.0211 | 0.0952 | -0.1400 | -0.2465 | 0.1300 | -0.2509 | 0.0314 | -0.1520 | 0.0769 | 0.2827 | -0.1137 | 0.0518 | -0.0200 | -0.2870 | -0.0591 | 0.1906 | -0.0187 | 0.0028 | -0.0746 | -0.0077 | 0.0024 | -0.0209 | -0.0007 | -0.0086 | 0.0130 |
| 2073e2 | 2073e2 | Eosinophil | d2073 | 10 | #66A61E | 2073e2 | 0.0787 | -0.0653 | 0.0787 | -0.0653 | 0.2110 | 0.0600 | 0.0777 | 0.1203 | -0.0372 | -0.0714 | -0.0169 | -0.0068 | -0.0311 | -0.0930 | 0.0407 | -0.0425 | 0.0495 | -0.1466 | 0.1352 | -0.1033 | -0.1053 | 0.0201 | 0.0400 | -0.0647 | -0.0347 | 0.2148 | -0.0635 | -0.1585 | -0.1319 | -0.0082 | -0.0489 | -0.0206 | -0.0369 | -0.0206 | -0.0590 | 0.0811 | -0.1178 | -0.1710 | 0.0359 | -0.0253 | 0.0011 | 0.0926 | 0.1271 | -0.1113 | 0.0211 | -0.0677 | -0.0334 | 0.0100 | -0.0444 | -0.0143 | 0.0622 | 0.0918 | -0.0173 | 0.0049 | -0.1157 | -0.0658 | 0.0112 | 0.0030 | -0.0500 | 0.1055 | -0.0189 | 0.0189 | -0.0530 | 0.0031 | -0.0130 | 0.2917 | -0.1124 | -0.0703 | -0.1599 | 0.4865 | -0.2781 | 0.2758 | 0.1979 | -0.1088 | -0.0209 | -0.0296 | 0.0150 | 0.0022 |
| 2068m3 | 2068m3 | Monocyte | d2068 | 7 | #7570B3 | 2068m3 | -0.0183 | -0.1002 | -0.0183 | -0.1002 | -0.0415 | -0.1616 | -0.1054 | -0.0831 | 0.0482 | 0.0620 | 0.0031 | -0.0334 | -0.0085 | 0.1256 | -0.0781 | -0.0122 | 0.0079 | 0.0858 | 0.0127 | -0.0078 | 0.0721 | -0.0653 | -0.1223 | 0.0503 | 0.0785 | -0.0017 | 0.0152 | -0.3141 | 0.1104 | -0.1444 | -0.0284 | -0.0733 | -0.2748 | -0.0989 | -0.1016 | 0.0705 | -0.0266 | 0.0341 | 0.0154 | 0.1096 | -0.0605 | 0.0357 | -0.0082 | -0.2195 | 0.0767 | 0.2553 | 0.1292 | -0.0238 | 0.1760 | 0.0685 | 0.0469 | 0.3070 | 0.0262 | 0.2684 | -0.2119 | -0.0430 | -0.3288 | 0.0961 | 0.1006 | -0.1666 | 0.0047 | 0.1155 | -0.0121 | 0.0142 | -0.1056 | -0.0054 | -0.0495 | -0.0636 | -0.0939 | -0.0365 | 0.1095 | 0.0365 | -0.0553 | -0.0093 | -0.0332 | -0.0094 | -0.0162 | 0.0013 |
| 2068n3 | 2068n3 | Neutrophil | d2068 | 7 | #D95F02 | 2068n3 | 0.1484 | 0.0911 | 0.1484 | 0.0911 | -0.0959 | 0.0248 | 0.0176 | -0.1031 | 0.0871 | -0.0294 | 0.0314 | -0.0420 | 0.0250 | 0.0635 | -0.1872 | -0.0229 | 0.1226 | 0.2054 | -0.0674 | 0.1538 | -0.1160 | 0.1541 | -0.2144 | 0.0506 | -0.0462 | 0.0778 | 0.0306 | -0.0173 | -0.0599 | -0.0955 | 0.0080 | 0.1166 | -0.2299 | -0.1325 | -0.1522 | 0.1943 | -0.0663 | 0.2178 | 0.1444 | -0.0364 | -0.0613 | 0.0771 | 0.1270 | -0.1398 | -0.3014 | -0.1838 | -0.1576 | -0.1249 | -0.0861 | -0.0614 | 0.0182 | -0.1897 | 0.1667 | -0.0605 | 0.0677 | -0.1014 | 0.1082 | -0.2355 | -0.0659 | -0.1573 | 0.1685 | 0.0853 | -0.1565 | 0.0780 | 0.0042 | -0.0322 | -0.0272 | -0.0149 | -0.0049 | -0.0038 | -0.0348 | 0.0028 | -0.0117 | -0.0426 | -0.0113 | 0.0010 | 0.0062 | -0.0098 |
| 2068e3 | 2068e3 | Eosinophil | d2068 | 7 | #66A61E | 2068e3 | 0.0921 | -0.0670 | 0.0921 | -0.0670 | 0.2180 | 0.0669 | -0.0666 | -0.0644 | 0.0534 | 0.0386 | -0.0018 | -0.0276 | 0.0183 | 0.0618 | -0.0911 | 0.0166 | -0.0584 | 0.0688 | -0.0536 | 0.0183 | -0.0543 | -0.0447 | -0.2104 | 0.0545 | 0.0390 | -0.1131 | -0.0005 | -0.0246 | 0.2020 | -0.1364 | 0.1478 | 0.2517 | -0.1227 | -0.0933 | 0.0074 | -0.0521 | 0.0760 | 0.0719 | -0.0259 | -0.0246 | 0.0416 | -0.1638 | -0.0071 | -0.1462 | 0.0130 | 0.1242 | 0.1596 | -0.1735 | 0.1535 | -0.0011 | -0.0126 | -0.0602 | -0.0334 | -0.3027 | -0.0133 | 0.0614 | 0.1487 | 0.0770 | -0.1164 | 0.3174 | -0.1034 | -0.0721 | 0.3011 | -0.0807 | -0.0231 | 0.1763 | 0.1038 | 0.0306 | -0.1173 | 0.1150 | -0.0719 | -0.2106 | -0.0226 | 0.0002 | 0.1125 | -0.0154 | 0.0003 | -0.0045 |
| 2072m3 | 2072m3 | Monocyte | d2072 | 9 | #7570B3 | 2072m3 | -0.0161 | -0.0989 | -0.0161 | -0.0989 | -0.0484 | -0.1549 | -0.0985 | 0.0504 | 0.0164 | 0.1029 | 0.0289 | -0.0396 | 0.0231 | 0.0640 | -0.1124 | -0.0453 | 0.0919 | 0.1301 | 0.0852 | 0.0157 | 0.0636 | -0.0930 | 0.0035 | -0.0934 | 0.1077 | 0.0427 | 0.0259 | -0.0935 | 0.0471 | 0.0839 | -0.0542 | -0.1030 | -0.0423 | -0.1284 | 0.0457 | 0.0293 | 0.1004 | -0.1616 | -0.0999 | 0.1122 | -0.0028 | 0.0178 | -0.0901 | -0.0184 | -0.1422 | -0.0971 | -0.0125 | -0.0775 | 0.0044 | 0.1096 | 0.0811 | -0.1433 | -0.0032 | 0.0931 | -0.1709 | 0.2520 | -0.0649 | -0.1080 | -0.0786 | 0.1165 | -0.0611 | -0.1350 | -0.1369 | -0.2255 | 0.3143 | -0.0394 | 0.3154 | 0.1359 | 0.3673 | 0.0634 | -0.2065 | 0.0107 | 0.0850 | 0.0244 | -0.0587 | -0.0026 | 0.0476 | -0.0089 |
| 2072n3 | 2072n3 | Neutrophil | d2072 | 9 | #D95F02 | 2072n3 | 0.1542 | 0.1032 | 0.1542 | 0.1032 | -0.1130 | 0.0367 | -0.0815 | 0.0532 | 0.0285 | 0.0443 | -0.0756 | 0.0525 | 0.0196 | 0.1193 | 0.0098 | -0.0371 | 0.1042 | 0.1637 | 0.1533 | -0.0069 | 0.0549 | -0.0208 | 0.1004 | -0.1370 | 0.1046 | 0.0263 | -0.1293 | 0.0810 | -0.0349 | -0.0034 | -0.0281 | 0.2085 | -0.0445 | 0.1170 | -0.0678 | 0.1884 | -0.2323 | -0.1240 | -0.2079 | -0.1458 | -0.1827 | -0.2408 | -0.0678 | -0.0191 | -0.1393 | -0.0055 | -0.0321 | -0.0250 | 0.0098 | 0.0672 | 0.0683 | 0.2670 | -0.2811 | -0.0192 | 0.1069 | 0.1918 | 0.2658 | 0.0876 | 0.2442 | -0.1524 | -0.0659 | -0.1625 | -0.0645 | 0.0240 | -0.0926 | 0.0052 | 0.0392 | -0.0124 | -0.0602 | -0.0095 | 0.0348 | -0.0044 | 0.0016 | 0.0183 | -0.0134 | 0.0108 | -0.0052 | 0.0041 |
| 2072e3 | 2072e3 | Eosinophil | d2072 | 9 | #66A61E | 2072e3 | 0.0881 | -0.0617 | 0.0881 | -0.0617 | 0.2114 | 0.0649 | -0.1090 | 0.0492 | 0.0160 | 0.0681 | -0.0433 | -0.0097 | 0.0222 | 0.0902 | -0.0253 | 0.0597 | -0.0061 | 0.1454 | 0.1588 | 0.0387 | -0.0279 | -0.0486 | 0.1720 | -0.2262 | 0.0814 | -0.0070 | -0.0275 | -0.0267 | -0.1297 | 0.2020 | 0.2638 | -0.1129 | 0.0836 | -0.1247 | -0.1781 | 0.0783 | 0.1025 | 0.2954 | 0.1231 | -0.0777 | 0.0031 | 0.1352 | -0.0528 | 0.1688 | 0.0307 | 0.1322 | -0.0788 | 0.1005 | 0.0153 | 0.0477 | -0.0528 | 0.1515 | -0.1097 | 0.1450 | -0.2175 | -0.1318 | 0.2301 | -0.2090 | 0.0008 | 0.1957 | 0.1634 | 0.0385 | 0.1755 | 0.0544 | 0.0463 | -0.0601 | -0.0553 | 0.0652 | 0.0114 | -0.0786 | 0.0615 | 0.0082 | -0.0181 | 0.0334 | -0.0186 | 0.0022 | 0.0348 | -0.0040 |
| 2159bp1 | 2159bp1 | Biopsy | d2159 | 11 | #E7298A | 2159bp1 | -0.1791 | 0.2086 | -0.1791 | 0.2086 | 0.0883 | -0.0385 | 0.1056 | 0.0003 | 0.0926 | 0.0140 | 0.0657 | 0.0930 | 0.1105 | -0.1105 | 0.0353 | -0.0789 | -0.0685 | 0.0437 | -0.0935 | -0.0537 | 0.0371 | 0.1990 | 0.0019 | -0.2557 | 0.3243 | -0.1406 | -0.0890 | 0.0413 | -0.2221 | -0.4291 | 0.1823 | -0.1198 | -0.1994 | 0.0058 | 0.0575 | 0.1376 | 0.2980 | -0.2032 | 0.0746 | -0.0422 | 0.0285 | -0.0218 | -0.1789 | -0.0217 | 0.1426 | -0.0231 | -0.1501 | 0.0877 | -0.0533 | -0.0201 | 0.0458 | -0.0496 | -0.0023 | -0.0335 | 0.0425 | 0.0076 | 0.0017 | 0.0234 | 0.0018 | -0.0223 | -0.0056 | 0.0197 | -0.0013 | 0.0095 | -0.0106 | 0.0045 | -0.0020 | -0.0027 | -0.0036 | 0.0028 | 0.0111 | 0.0034 | -0.0058 | -0.0015 | 0.0003 | 0.0020 | 0.0078 | 0.0076 |
| 2073m3 | 2073m3 | Monocyte | d2073 | 10 | #7570B3 | 2073m3 | -0.0272 | -0.1186 | -0.0272 | -0.1186 | -0.0283 | -0.1589 | 0.0238 | 0.0721 | 0.0147 | -0.0293 | 0.1149 | -0.0237 | -0.0022 | -0.1364 | -0.0468 | 0.0328 | 0.0655 | -0.0584 | 0.1240 | -0.1161 | 0.0008 | 0.0439 | -0.1649 | 0.0683 | 0.0232 | 0.0491 | -0.0424 | 0.1055 | -0.0206 | -0.1083 | -0.1016 | 0.0923 | 0.0572 | -0.0835 | 0.0422 | 0.0379 | 0.0221 | 0.1260 | 0.2007 | -0.1180 | 0.1420 | -0.2243 | -0.0545 | 0.0755 | -0.0542 | 0.1904 | 0.1170 | -0.0103 | 0.0182 | 0.1646 | -0.1614 | 0.0663 | 0.0578 | 0.0738 | 0.2029 | 0.0454 | 0.0446 | 0.0015 | 0.0565 | 0.0089 | 0.0603 | 0.0192 | 0.0453 | 0.1078 | 0.0621 | -0.1805 | -0.1168 | -0.0562 | 0.3247 | 0.2369 | 0.1919 | 0.1464 | 0.2926 | 0.1361 | 0.2395 | 0.0488 | -0.0039 | 0.0241 |
| 2073n3 | 2073n3 | Neutrophil | d2073 | 10 | #D95F02 | 2073n3 | 0.1368 | 0.0684 | 0.1368 | 0.0684 | -0.0907 | 0.0314 | 0.0359 | 0.0089 | 0.0474 | -0.0877 | 0.0608 | -0.0212 | 0.0116 | -0.2167 | -0.0830 | 0.1571 | 0.0324 | -0.1670 | 0.2107 | -0.1715 | 0.0424 | 0.1057 | 0.0061 | 0.0985 | -0.0223 | 0.0217 | 0.0625 | -0.0696 | 0.1946 | -0.0520 | 0.1614 | -0.0677 | -0.1351 | 0.0796 | 0.1187 | -0.1380 | 0.1825 | 0.1704 | -0.0502 | 0.2089 | 0.0487 | 0.0700 | 0.0027 | 0.0657 | 0.0644 | -0.0852 | -0.0208 | -0.0171 | 0.1375 | -0.0903 | -0.1805 | 0.0077 | 0.0043 | -0.0101 | -0.1768 | 0.1351 | 0.2465 | -0.0677 | 0.3371 | -0.0859 | -0.0014 | -0.1103 | -0.2503 | -0.0375 | -0.0146 | 0.1949 | -0.1506 | -0.0681 | 0.0712 | -0.0512 | -0.0480 | -0.1259 | -0.0218 | -0.0101 | 0.0119 | -0.0023 | -0.0043 | -0.0010 |
| 2073e3 | 2073e3 | Eosinophil | d2073 | 10 | #66A61E | 2073e3 | 0.0864 | -0.0709 | 0.0864 | -0.0709 | 0.2124 | 0.0691 | 0.0559 | 0.0466 | -0.0215 | -0.0402 | 0.0715 | -0.0079 | -0.0042 | -0.1214 | -0.0275 | 0.1128 | -0.0099 | -0.0635 | 0.1197 | -0.1334 | -0.0506 | 0.0165 | -0.1355 | -0.0224 | -0.0105 | 0.1673 | -0.0659 | -0.0291 | -0.0688 | 0.0020 | -0.0261 | 0.0281 | -0.0786 | -0.0378 | -0.0515 | 0.0272 | -0.0954 | -0.2289 | 0.1427 | 0.0048 | -0.0075 | 0.0951 | 0.1350 | -0.0767 | 0.1051 | -0.0402 | 0.1046 | -0.1535 | 0.1320 | -0.0957 | -0.0129 | 0.0163 | -0.0412 | -0.1419 | 0.1292 | 0.1454 | 0.0074 | 0.0583 | -0.0202 | 0.0899 | 0.0720 | -0.0715 | 0.0201 | -0.1830 | 0.0530 | -0.3437 | -0.1835 | 0.1972 | 0.0123 | -0.3960 | 0.0552 | 0.2268 | -0.1302 | 0.0078 | -0.2196 | -0.0071 | -0.0072 | 0.0038 |
| 2162m1 | 2162m1 | Monocyte | d2162 | 12 | #7570B3 | 2162m1 | -0.0425 | -0.1007 | -0.0425 | -0.1007 | -0.0338 | -0.1657 | 0.0313 | -0.1423 | 0.0218 | -0.0673 | -0.0445 | 0.0207 | -0.0260 | 0.2322 | -0.0294 | 0.0129 | -0.0681 | -0.1665 | -0.0073 | -0.0896 | 0.2117 | -0.0544 | 0.0894 | 0.1131 | 0.0793 | 0.0867 | -0.1404 | 0.0681 | -0.2107 | -0.0394 | 0.0508 | -0.1008 | -0.1510 | 0.2603 | -0.2095 | -0.1134 | -0.0287 | 0.1916 | -0.0324 | -0.0525 | 0.4340 | -0.1767 | 0.2195 | -0.0193 | -0.0210 | -0.0481 | -0.1283 | -0.2095 | -0.1242 | 0.1171 | 0.0125 | -0.1193 | -0.2206 | 0.0447 | -0.0094 | 0.0271 | -0.0806 | -0.0052 | -0.0484 | 0.1077 | -0.0270 | -0.0394 | -0.0738 | -0.0246 | -0.0185 | 0.0362 | -0.0150 | 0.0234 | -0.0650 | -0.0650 | -0.0080 | -0.0062 | -0.0374 | 0.0046 | -0.0094 | -0.0124 | 0.0005 | -0.0018 |
| 2162n1 | 2162n1 | Neutrophil | d2162 | 12 | #D95F02 | 2162n1 | 0.1225 | 0.0711 | 0.1225 | 0.0711 | -0.0672 | -0.0105 | 0.0483 | -0.1530 | 0.0930 | -0.0964 | 0.0524 | -0.1531 | 0.0155 | -0.0558 | -0.1368 | 0.0429 | -0.1571 | -0.0623 | -0.2089 | -0.0999 | 0.3801 | -0.0107 | 0.2605 | 0.1988 | -0.0863 | 0.3116 | -0.0324 | 0.0100 | -0.0573 | 0.0782 | 0.1678 | 0.0565 | 0.1316 | -0.2632 | 0.1663 | 0.2022 | 0.1713 | -0.0163 | -0.0068 | -0.0677 | -0.0133 | -0.0633 | -0.0252 | -0.1831 | -0.1093 | 0.0279 | 0.1631 | 0.2389 | -0.0255 | -0.0781 | 0.1912 | 0.0098 | -0.0609 | -0.0264 | 0.0009 | -0.0369 | 0.0333 | 0.0709 | -0.0210 | -0.0155 | 0.0209 | 0.0240 | 0.0293 | -0.0267 | -0.0003 | -0.0547 | -0.0149 | 0.0077 | -0.0023 | -0.0109 | -0.0400 | -0.0099 | 0.0017 | -0.0087 | 0.0059 | -0.0003 | 0.0024 | 0.0046 |
| 2162e1 | 2162e1 | Eosinophil | d2162 | 12 | #66A61E | 2162e1 | 0.0811 | -0.0609 | 0.0811 | -0.0609 | 0.2102 | 0.0523 | -0.0761 | -0.1254 | 0.0056 | 0.0407 | 0.0107 | -0.0092 | 0.0049 | 0.0961 | -0.0675 | 0.1321 | -0.1182 | -0.0235 | -0.1821 | -0.0569 | 0.2382 | -0.0966 | -0.2013 | 0.0761 | 0.0715 | 0.0774 | -0.0952 | 0.2924 | -0.3011 | 0.1464 | 0.0372 | -0.0499 | -0.0508 | 0.0549 | -0.1365 | -0.2381 | -0.0099 | -0.1571 | -0.0699 | 0.2185 | -0.2805 | 0.0874 | -0.2001 | 0.0970 | -0.1022 | 0.0383 | -0.0397 | -0.0996 | -0.0036 | -0.0289 | -0.2155 | 0.1130 | 0.2437 | -0.0247 | 0.0498 | 0.0294 | -0.0213 | -0.0252 | 0.0658 | -0.0948 | -0.0308 | 0.0395 | 0.0034 | 0.1058 | 0.0419 | 0.0814 | 0.0219 | -0.0069 | -0.0039 | 0.0903 | 0.0086 | -0.0193 | 0.0591 | -0.0035 | -0.0108 | -0.0051 | -0.0029 | 0.0001 |
| 2162bp1 | 2162bp1 | Biopsy | d2162 | 12 | #E7298A | 2162bp1 | -0.1665 | 0.2377 | -0.1665 | 0.2377 | 0.0961 | -0.0355 | -0.0622 | -0.0678 | 0.1532 | 0.2802 | 0.3140 | 0.5361 | 0.3166 | 0.1709 | -0.1171 | -0.0158 | 0.0473 | -0.3398 | -0.0390 | 0.0066 | -0.1010 | 0.0033 | 0.0283 | 0.0534 | -0.0944 | 0.1481 | -0.0284 | 0.0159 | 0.1619 | 0.1008 | -0.0187 | 0.0172 | 0.0998 | -0.0572 | -0.0354 | 0.0247 | -0.0964 | 0.0325 | 0.0104 | -0.0292 | -0.0206 | 0.0040 | -0.0385 | -0.0436 | 0.0128 | 0.0199 | -0.0990 | 0.0052 | 0.0688 | -0.0293 | 0.0144 | -0.0078 | 0.0077 | 0.0410 | -0.0183 | 0.0250 | 0.0026 | -0.0184 | -0.0008 | 0.0061 | -0.0010 | -0.0137 | 0.0122 | 0.0122 | -0.0058 | -0.0011 | 0.0068 | -0.0041 | 0.0073 | 0.0023 | 0.0010 | 0.0043 | -0.0071 | -0.0008 | 0.0060 | -0.0027 | -0.0039 | 0.0009 |
| macrofagos | Macrofagos | Macrophage | unknown | 14 | #E6AB02 | Macrofagos | -0.1372 | -0.1077 | -0.1372 | -0.1077 | -0.0816 | 0.1683 | 0.1238 | -0.0285 | 0.0979 | -0.1432 | 0.0240 | -0.1580 | 0.1954 | 0.0224 | 0.2302 | 0.0717 | 0.0891 | -0.0250 | 0.0174 | 0.0297 | 0.0115 | -0.0444 | 0.0503 | -0.0385 | 0.0339 | 0.0510 | -0.0878 | 0.1419 | 0.1364 | 0.0135 | 0.0371 | -0.0923 | -0.0478 | -0.1122 | -0.0021 | 0.0214 | -0.0747 | -0.0414 | -0.0999 | 0.0118 | 0.0811 | -0.0658 | 0.0682 | 0.1492 | -0.1247 | 0.0398 | -0.2238 | -0.0128 | 0.3626 | 0.1389 | 0.1199 | -0.0031 | 0.1822 | -0.0949 | -0.0696 | -0.1978 | -0.0012 | 0.1305 | -0.0714 | -0.1708 | 0.0629 | -0.0856 | 0.0709 | -0.0298 | -0.1382 | 0.1131 | -0.0159 | 0.3141 | 0.1450 | 0.0684 | 0.0623 | 0.0746 | -0.1121 | -0.0067 | 0.0384 | -0.1276 | 0.0536 | -0.1411 |
| macrofagos+sbv | Macrofagos+SbV | Macrophage | unknown | 14 | #E6AB02 | Mcrfgs+SbV | -0.1290 | -0.1113 | -0.1290 | -0.1113 | -0.0853 | 0.1934 | 0.0685 | -0.0569 | 0.1861 | -0.1440 | 0.1586 | 0.0535 | -0.1671 | -0.0079 | 0.0790 | -0.0435 | 0.0109 | 0.0422 | 0.0216 | 0.0232 | 0.0299 | -0.0556 | 0.0205 | -0.0679 | -0.0338 | -0.0434 | -0.0767 | 0.1034 | 0.1196 | 0.0125 | 0.0145 | -0.0768 | -0.0771 | -0.1186 | -0.0263 | -0.0855 | -0.1158 | 0.0201 | 0.0041 | -0.0851 | -0.0138 | 0.0503 | -0.0651 | -0.0290 | 0.0188 | -0.0278 | 0.0500 | -0.0036 | -0.0603 | -0.0223 | 0.0267 | -0.0326 | -0.0249 | 0.0282 | 0.0271 | 0.0275 | -0.0290 | -0.0928 | 0.1023 | 0.0678 | -0.1131 | 0.2052 | 0.0064 | -0.0600 | -0.2431 | 0.0444 | 0.0469 | 0.1192 | 0.0160 | -0.1606 | -0.0550 | -0.0853 | 0.4113 | -0.0840 | -0.1219 | 0.0481 | 0.1466 | 0.5027 |
| macrofagos+10772 | Macrofagos+10772 | Macrophage | unknown | 14 | #E6AB02 | Mcrf+10772 | -0.1318 | -0.0956 | -0.1318 | -0.0956 | -0.0957 | 0.1795 | 0.0396 | 0.0133 | -0.0577 | 0.0300 | -0.0800 | -0.1358 | 0.2320 | 0.0181 | 0.0359 | 0.0325 | 0.0431 | 0.0018 | -0.0066 | -0.0254 | -0.0059 | 0.0356 | -0.0075 | -0.0042 | -0.0334 | 0.0269 | -0.1125 | 0.0723 | 0.0981 | -0.0548 | 0.0024 | -0.1053 | -0.0552 | -0.0982 | 0.0083 | -0.0625 | -0.0897 | 0.0080 | -0.0232 | -0.0338 | 0.0430 | 0.0119 | -0.0604 | 0.0353 | -0.0477 | 0.0277 | -0.0730 | 0.0531 | 0.1014 | 0.0572 | 0.0012 | 0.0758 | 0.0162 | -0.0537 | -0.0146 | -0.0561 | -0.0257 | 0.0825 | -0.1422 | 0.0282 | 0.0836 | -0.1704 | -0.0921 | 0.0478 | 0.3282 | -0.1381 | -0.0417 | -0.4070 | -0.2944 | -0.1457 | -0.1958 | -0.1201 | 0.2046 | 0.2629 | -0.0622 | 0.2342 | -0.0991 | 0.0669 |
| macrofagos+10772+sbv | Macrofagos+10772+SbV | Macrophage | unknown | 14 | #E6AB02 | M+10772+SV | -0.1259 | -0.1061 | -0.1259 | -0.1061 | -0.0900 | 0.1981 | 0.0844 | -0.0035 | 0.1124 | -0.0996 | 0.1183 | 0.0873 | -0.1932 | -0.0462 | -0.0061 | -0.0881 | -0.0274 | 0.0425 | 0.0165 | 0.0161 | 0.0420 | -0.0389 | 0.0341 | -0.0452 | 0.0111 | 0.0059 | -0.0019 | 0.0662 | 0.0579 | 0.0285 | 0.0059 | -0.0493 | -0.0451 | -0.0731 | -0.0150 | 0.0050 | -0.0584 | -0.0037 | -0.0116 | -0.0368 | -0.0138 | -0.0280 | -0.0319 | 0.0417 | -0.0258 | -0.0157 | -0.0025 | 0.0093 | 0.0659 | 0.0117 | 0.0032 | 0.0065 | -0.0058 | 0.0254 | 0.0523 | 0.0894 | -0.0523 | -0.1041 | 0.0618 | 0.2113 | -0.0660 | 0.0710 | -0.0600 | 0.0173 | 0.1461 | -0.0898 | 0.0147 | -0.2036 | -0.0586 | 0.0274 | 0.1891 | -0.0632 | 0.0036 | -0.5013 | -0.0162 | -0.1104 | -0.3864 | -0.3957 |
| macrofagos+2169 | Macrofagos+2169 | Macrophage | unknown | 14 | #E6AB02 | Mcrfg+2169 | -0.1254 | -0.0768 | -0.1254 | -0.0768 | -0.1014 | 0.1732 | 0.0939 | 0.0708 | -0.2346 | 0.0978 | -0.1982 | -0.0945 | 0.2073 | 0.0374 | -0.2070 | -0.0609 | -0.0547 | 0.0121 | -0.0219 | -0.0855 | 0.0048 | 0.1208 | -0.0079 | -0.0179 | -0.0652 | 0.0386 | -0.1504 | 0.0761 | 0.1302 | -0.0314 | -0.0361 | -0.0831 | -0.0024 | -0.0831 | -0.0134 | -0.0719 | -0.0276 | 0.0051 | 0.0296 | -0.0543 | -0.0398 | 0.0036 | -0.0288 | 0.0165 | 0.0193 | -0.0228 | 0.0394 | -0.0163 | -0.0650 | -0.0438 | -0.0166 | -0.0060 | -0.0396 | 0.0712 | 0.0687 | 0.0980 | -0.0419 | -0.0781 | 0.1085 | 0.1152 | -0.0824 | 0.0926 | -0.0204 | 0.0235 | 0.0453 | -0.0784 | -0.0106 | -0.1098 | -0.0312 | 0.1031 | 0.0743 | 0.0326 | -0.2938 | -0.1711 | 0.1275 | -0.2587 | 0.5278 | 0.0809 |
| macrofagos+2169+sbv | Macrofagos+2169+SbV | Macrophage | unknown | 14 | #E6AB02 | Mc+2169+SV | -0.1186 | -0.0862 | -0.1186 | -0.0862 | -0.0978 | 0.2033 | -0.0145 | 0.0381 | -0.0654 | 0.0308 | -0.0157 | 0.1384 | -0.2710 | -0.0366 | -0.2132 | -0.0984 | -0.1147 | -0.0103 | -0.0008 | -0.0107 | -0.0124 | 0.0300 | 0.0398 | 0.0143 | 0.0438 | 0.0192 | 0.0619 | -0.0727 | -0.1028 | 0.0493 | -0.0053 | 0.0693 | 0.0793 | 0.0677 | 0.0515 | 0.1201 | 0.1078 | -0.0224 | -0.0151 | 0.1180 | -0.0156 | -0.1315 | 0.1231 | 0.1172 | -0.0587 | 0.0264 | -0.1584 | -0.0460 | 0.2546 | 0.0601 | 0.0332 | -0.0164 | 0.1044 | 0.0223 | 0.0317 | -0.0228 | -0.0078 | -0.0043 | 0.0207 | 0.0377 | -0.0141 | 0.0168 | 0.0278 | 0.0213 | 0.0159 | -0.0361 | 0.0172 | -0.0401 | 0.0094 | 0.1627 | -0.0018 | 0.0708 | -0.3743 | 0.0504 | -0.0225 | 0.2870 | -0.1806 | 0.4488 |
| macrofagos+12309 | Macrofagos+12309 | Macrophage | unknown | 14 | #E6AB02 | Mcrf+12309 | -0.1299 | -0.1008 | -0.1299 | -0.1008 | -0.0874 | 0.1838 | 0.0066 | -0.0704 | 0.0366 | 0.0354 | 0.0097 | -0.1050 | 0.2758 | 0.0063 | 0.2379 | 0.1472 | 0.1302 | -0.0119 | -0.0119 | 0.0871 | 0.0025 | -0.0506 | -0.0527 | 0.0596 | 0.0871 | 0.0408 | 0.1797 | -0.1623 | -0.1599 | 0.0540 | -0.0268 | 0.1443 | 0.0886 | 0.1034 | 0.0211 | 0.1244 | 0.1380 | -0.0003 | 0.0013 | 0.0743 | -0.0551 | -0.0372 | 0.0575 | -0.0185 | 0.0145 | -0.0128 | 0.0205 | -0.0296 | -0.0468 | -0.0587 | -0.0401 | -0.0448 | -0.0325 | 0.1059 | 0.0912 | 0.0979 | -0.0215 | -0.1236 | 0.1097 | 0.1376 | -0.0889 | 0.1384 | 0.0023 | 0.0023 | -0.0579 | -0.0128 | 0.0224 | -0.0609 | -0.0491 | -0.0129 | -0.0623 | 0.0153 | -0.0088 | 0.2693 | 0.0168 | -0.4892 | -0.2638 | 0.1254 |
| macrofagos+12309+sbv | Macrofagos+12309+SbV | Macrophage | unknown | 14 | #E6AB02 | M+12309+SV | -0.1277 | -0.1040 | -0.1277 | -0.1040 | -0.0867 | 0.2022 | -0.0309 | -0.0576 | 0.1537 | -0.0850 | 0.1427 | 0.0641 | -0.2154 | -0.0084 | 0.0536 | -0.0088 | 0.0277 | 0.0137 | 0.0102 | 0.0222 | 0.0255 | -0.0259 | -0.0104 | -0.0180 | -0.0434 | -0.0358 | -0.0290 | 0.0323 | 0.0471 | -0.0051 | -0.0165 | -0.0453 | -0.0488 | -0.0741 | -0.0212 | -0.0852 | -0.0325 | 0.0048 | 0.0222 | -0.0769 | -0.0352 | 0.0445 | -0.0753 | -0.0592 | 0.0426 | -0.0231 | 0.0666 | -0.0033 | -0.2053 | -0.0843 | -0.0126 | -0.0117 | -0.0467 | 0.0606 | 0.0173 | 0.0883 | -0.0078 | -0.0620 | 0.0895 | 0.0418 | -0.0481 | 0.0906 | 0.0163 | 0.0152 | -0.1310 | -0.0240 | -0.0180 | 0.0474 | 0.0051 | 0.1143 | -0.2001 | 0.0811 | -0.2883 | 0.4915 | 0.0395 | 0.2536 | 0.1146 | -0.4189 |
| macrofagos+12367+sbv | Macrofagos+12367+SbV | Macrophage | unknown | 14 | #E6AB02 | M+12367+SV | -0.1230 | -0.0981 | -0.1230 | -0.0981 | -0.0929 | 0.2109 | -0.1360 | -0.0483 | 0.1211 | 0.0024 | 0.1210 | 0.1058 | -0.2362 | -0.0033 | 0.0312 | 0.0139 | 0.0079 | -0.0171 | 0.0060 | 0.0245 | 0.0002 | -0.0565 | -0.0038 | 0.0229 | -0.0180 | -0.0656 | 0.0563 | -0.0331 | -0.0492 | -0.0405 | 0.0181 | 0.0148 | -0.0313 | 0.0481 | -0.0302 | -0.0728 | -0.0468 | 0.0020 | 0.0235 | -0.0426 | 0.0466 | 0.1060 | -0.0961 | -0.1381 | 0.0713 | 0.0008 | 0.1439 | 0.0598 | -0.2516 | -0.0165 | -0.0427 | 0.0547 | -0.0724 | -0.1241 | -0.1380 | -0.0690 | 0.0971 | 0.1549 | -0.2143 | -0.2415 | 0.2180 | -0.3434 | -0.0265 | 0.0349 | 0.2675 | 0.0617 | -0.0248 | 0.0008 | 0.0644 | 0.0481 | 0.1470 | 0.0567 | -0.1105 | -0.0285 | 0.1016 | -0.3142 | 0.0396 | 0.1966 |
| macrofagos+11126 | Macrofagos+11126 | Macrophage | unknown | 14 | #E6AB02 | Mcrf+11126 | -0.1274 | -0.0996 | -0.1274 | -0.0996 | -0.0891 | 0.1884 | -0.0691 | -0.0725 | 0.0502 | 0.0641 | 0.0136 | -0.0703 | 0.2336 | 0.0072 | 0.2251 | 0.1598 | 0.1094 | -0.0321 | -0.0127 | 0.0820 | -0.0151 | -0.0648 | -0.0439 | 0.0820 | 0.0877 | 0.0107 | 0.1860 | -0.1585 | -0.1909 | 0.0309 | 0.0081 | 0.1548 | 0.0846 | 0.1578 | 0.0095 | 0.0915 | 0.1113 | 0.0030 | 0.0172 | 0.0568 | -0.0001 | -0.0064 | 0.0440 | -0.0616 | 0.0365 | -0.0095 | 0.0896 | -0.0213 | -0.0677 | -0.0267 | -0.0540 | -0.0081 | -0.0448 | 0.0037 | -0.0139 | 0.0207 | 0.0113 | 0.0249 | -0.0139 | -0.0227 | -0.0016 | -0.0336 | 0.0107 | 0.0145 | 0.0211 | 0.0036 | -0.0013 | 0.0035 | 0.0524 | -0.0080 | 0.0786 | -0.0150 | 0.0539 | -0.3745 | 0.0006 | 0.5128 | 0.3043 | -0.0923 |
| macrofagos+12251 | Macrofagos+12251 | Macrophage | unknown | 14 | #E6AB02 | Mcrf+12251 | -0.1253 | -0.0695 | -0.1253 | -0.0695 | -0.1053 | 0.1804 | 0.0063 | 0.0497 | -0.2636 | 0.1679 | -0.2306 | -0.0887 | 0.2129 | 0.0901 | -0.2212 | -0.0279 | -0.0404 | -0.0381 | -0.0294 | -0.0923 | -0.0222 | 0.1152 | -0.0016 | -0.0070 | -0.0715 | 0.0022 | -0.1298 | -0.0027 | 0.0555 | -0.0771 | -0.0239 | -0.0316 | 0.0044 | 0.0135 | -0.0314 | -0.1060 | -0.0082 | 0.0284 | 0.0605 | -0.0581 | -0.0113 | 0.0933 | -0.0477 | -0.0741 | 0.0561 | 0.0122 | 0.1368 | 0.0336 | -0.2201 | -0.0556 | -0.0303 | -0.0262 | -0.0743 | -0.0305 | -0.0579 | -0.0051 | 0.0610 | 0.0117 | -0.0097 | -0.1154 | 0.0486 | 0.0227 | 0.0460 | -0.0483 | -0.1811 | 0.1300 | 0.0490 | 0.2788 | 0.1854 | 0.0361 | 0.0750 | 0.0209 | 0.0824 | -0.0225 | -0.1104 | 0.1611 | -0.4833 | -0.0378 |
| macrofagos+12251+sbv | Macrofagos+12251+SbV | Macrophage | unknown | 14 | #E6AB02 | M+12251+SV | -0.1164 | -0.0766 | -0.1164 | -0.0766 | -0.1045 | 0.2056 | -0.0570 | 0.0548 | -0.1470 | 0.1245 | -0.0724 | 0.1523 | -0.2242 | -0.0098 | -0.2989 | -0.1114 | -0.1467 | -0.0100 | -0.0202 | -0.0427 | -0.0211 | 0.0478 | 0.0068 | 0.0185 | 0.0245 | -0.0282 | 0.0785 | -0.1041 | -0.1204 | 0.0305 | -0.0114 | 0.0904 | 0.0941 | 0.1346 | 0.0346 | 0.1250 | 0.0986 | -0.0178 | 0.0136 | 0.1045 | 0.0049 | -0.0587 | 0.1025 | 0.0477 | 0.0052 | 0.0230 | -0.0771 | -0.0153 | 0.1431 | 0.0400 | 0.0246 | 0.0073 | 0.0681 | -0.0120 | 0.0050 | -0.0776 | 0.0125 | 0.0648 | -0.0400 | -0.0958 | 0.0003 | -0.0058 | 0.0266 | -0.0302 | -0.0767 | 0.0317 | -0.0326 | 0.0667 | -0.0453 | -0.2196 | -0.1017 | -0.0676 | 0.4311 | 0.1121 | 0.0109 | -0.1986 | 0.2387 | -0.3325 |
| 2168e1 | 2168e1 | undefined | d2168 | 13 | #A6761D | 2168e1 | 0.0834 | -0.0640 | 0.0834 | -0.0640 | 0.2116 | 0.0626 | -0.0148 | 0.0721 | -0.0940 | 0.0811 | 0.0452 | 0.0834 | 0.0167 | -0.0200 | 0.0740 | 0.0153 | 0.0231 | 0.0787 | -0.0815 | 0.0930 | 0.0761 | 0.0347 | 0.1856 | 0.1363 | -0.1054 | -0.0140 | 0.0683 | -0.0764 | -0.0644 | -0.1762 | -0.1508 | -0.0787 | -0.1441 | 0.0754 | 0.0359 | -0.0537 | -0.0730 | 0.1538 | -0.0433 | 0.0378 | -0.1141 | -0.0921 | -0.1066 | 0.0547 | -0.0321 | 0.0284 | 0.0606 | 0.0995 | 0.0362 | 0.1004 | 0.0256 | -0.0499 | 0.0147 | -0.1672 | -0.1389 | -0.1388 | 0.1859 | 0.1077 | -0.2185 | 0.1527 | -0.2818 | 0.0879 | -0.2922 | 0.0877 | -0.2011 | -0.0731 | 0.0613 | -0.2039 | 0.2452 | -0.1012 | 0.0901 | 0.1848 | -0.0418 | 0.0468 | -0.1399 | -0.0289 | 0.0412 | -0.0190 |
| 2168m2 | 2168m2 | undefined | d2168 | 13 | #A6761D | 2168m2 | -0.0280 | -0.1074 | -0.0280 | -0.1074 | -0.0426 | -0.1568 | 0.0872 | 0.1403 | -0.0902 | 0.0190 | 0.1275 | 0.0352 | 0.0247 | -0.0248 | -0.0434 | -0.0330 | 0.1596 | 0.0550 | -0.0671 | 0.1304 | 0.0808 | -0.0060 | 0.0756 | -0.0153 | -0.0908 | -0.0374 | 0.1737 | 0.1843 | -0.0068 | -0.0071 | 0.0237 | 0.1037 | 0.0021 | -0.0693 | 0.0660 | -0.0755 | 0.1087 | -0.0242 | 0.0932 | 0.0691 | 0.0276 | 0.0406 | 0.0666 | 0.0597 | 0.0425 | -0.1261 | -0.0287 | -0.1618 | 0.0836 | -0.1335 | 0.0157 | 0.1686 | -0.2604 | -0.0314 | 0.0106 | -0.0010 | 0.0150 | 0.1082 | -0.1031 | -0.0587 | 0.0741 | 0.2132 | -0.0608 | -0.0214 | 0.1165 | 0.0359 | -0.0422 | 0.0752 | -0.0981 | 0.2829 | 0.2680 | -0.2169 | 0.0293 | 0.1366 | -0.4299 | -0.0231 | 0.0855 | -0.0279 |
| 2168n2 | 2168n2 | undefined | d2168 | 13 | #A6761D | 2168n2 | 0.1492 | 0.0983 | 0.1492 | 0.0983 | -0.1065 | 0.0330 | 0.0526 | 0.1257 | -0.0750 | -0.0244 | -0.0270 | 0.1636 | -0.0255 | 0.0720 | 0.0991 | 0.0002 | 0.1521 | 0.0314 | 0.0224 | 0.1436 | 0.1128 | 0.1421 | -0.0643 | 0.2719 | 0.0583 | -0.1360 | -0.1102 | 0.0748 | 0.0050 | 0.0629 | 0.0155 | 0.0266 | -0.0134 | 0.0175 | 0.0576 | -0.1346 | 0.1167 | -0.1006 | 0.1470 | 0.0614 | 0.0156 | 0.2082 | 0.2443 | -0.0198 | 0.0040 | 0.1128 | -0.0646 | 0.2689 | -0.0154 | 0.2737 | 0.1516 | 0.1750 | -0.0521 | -0.2850 | -0.0308 | 0.2442 | -0.1709 | -0.1976 | 0.0115 | 0.0752 | 0.1212 | -0.0177 | 0.0808 | 0.1380 | -0.0619 | 0.0434 | 0.0807 | -0.0200 | 0.0296 | -0.0437 | -0.0226 | 0.0708 | 0.0086 | -0.0017 | 0.0304 | 0.0003 | 0.0141 | 0.0024 |
| 2168e2 | 2168e2 | undefined | d2168 | 13 | #A6761D | 2168e2 | 0.0889 | -0.0668 | 0.0889 | -0.0668 | 0.2129 | 0.0676 | 0.0435 | 0.1336 | -0.0860 | 0.0122 | 0.0838 | 0.0943 | 0.0145 | 0.0278 | 0.0374 | 0.1431 | -0.0159 | 0.1220 | 0.0071 | 0.1328 | 0.0058 | 0.0751 | 0.2364 | 0.1155 | -0.0613 | -0.0422 | 0.0307 | -0.0312 | -0.0491 | -0.0931 | -0.2060 | -0.0919 | -0.0457 | 0.0583 | 0.0475 | -0.0253 | 0.0161 | 0.0914 | -0.0104 | 0.0312 | -0.0142 | -0.0290 | -0.1140 | 0.0123 | -0.0561 | -0.0202 | -0.0366 | 0.0320 | -0.0196 | -0.0085 | -0.0215 | 0.0621 | -0.0087 | 0.0143 | 0.0486 | 0.0585 | -0.1206 | 0.0347 | 0.0045 | -0.0648 | 0.1216 | 0.0450 | -0.1341 | -0.2655 | -0.0085 | -0.2490 | -0.1147 | 0.3081 | -0.2086 | 0.1165 | -0.1482 | -0.2717 | 0.0085 | -0.0794 | 0.4132 | 0.0221 | -0.0709 | 0.0399 |
| 2168m3 | 2168m3 | undefined | d2168 | 13 | #A6761D | 2168m3 | -0.0307 | -0.0936 | -0.0307 | -0.0936 | -0.0496 | -0.1552 | 0.1026 | 0.1357 | -0.1757 | 0.0269 | 0.0785 | 0.0169 | 0.0098 | 0.0189 | -0.0386 | -0.0655 | 0.1558 | 0.0291 | -0.1344 | 0.1159 | 0.1299 | -0.0924 | 0.0899 | -0.0900 | -0.0939 | -0.0395 | 0.2124 | 0.1470 | -0.0041 | -0.0487 | 0.1743 | 0.1130 | -0.0619 | -0.0940 | -0.0095 | -0.0829 | -0.0315 | -0.0531 | -0.0361 | 0.0281 | 0.0352 | 0.0756 | 0.0512 | 0.0263 | 0.1361 | -0.1554 | -0.0102 | -0.2226 | 0.0522 | -0.2239 | 0.0511 | 0.1257 | -0.0553 | 0.0713 | -0.0266 | -0.0651 | 0.0224 | 0.0521 | 0.0179 | -0.0434 | -0.0073 | -0.0531 | 0.1524 | 0.1991 | -0.0835 | -0.0304 | -0.0009 | -0.1184 | 0.1565 | -0.1983 | -0.2934 | 0.2007 | 0.0030 | -0.1154 | 0.3691 | 0.0249 | -0.0995 | 0.0304 |
| 2168n3 | 2168n3 | undefined | d2168 | 13 | #A6761D | 2168n3 | 0.1325 | 0.1034 | 0.1325 | 0.1034 | -0.1017 | 0.0140 | 0.0811 | 0.1337 | -0.1654 | -0.0038 | -0.0436 | 0.1504 | -0.0787 | 0.0634 | 0.1500 | -0.0626 | 0.1700 | 0.0391 | -0.1019 | 0.1517 | 0.1056 | -0.0313 | -0.1024 | 0.2217 | 0.1941 | -0.1180 | -0.2581 | -0.0038 | 0.0035 | 0.1174 | 0.1083 | -0.0145 | 0.0886 | -0.1293 | -0.0161 | 0.0561 | -0.0378 | -0.1422 | 0.0807 | -0.0727 | 0.0885 | 0.0405 | 0.0987 | -0.0867 | 0.1623 | 0.1147 | 0.0789 | -0.0407 | 0.0220 | -0.0509 | -0.1834 | -0.1963 | 0.1395 | 0.2805 | -0.0449 | -0.2243 | 0.2503 | 0.0991 | 0.0415 | -0.0220 | -0.2220 | -0.0641 | -0.1144 | -0.1818 | 0.0622 | -0.0675 | -0.0238 | 0.0292 | -0.0678 | 0.0483 | 0.0095 | -0.0538 | -0.0085 | -0.0001 | -0.0096 | 0.0028 | -0.0177 | -0.0029 |
| 2168e3 | 2168e3 | undefined | d2168 | 13 | #A6761D | 2168e3 | 0.0839 | -0.0606 | 0.0839 | -0.0606 | 0.2096 | 0.0576 | 0.1218 | 0.1474 | -0.1061 | -0.0416 | 0.0374 | 0.0793 | -0.0156 | -0.0089 | 0.0668 | 0.0401 | -0.0234 | 0.0685 | -0.0364 | 0.1179 | 0.0368 | 0.0533 | 0.2365 | 0.1647 | -0.0240 | -0.0040 | 0.0170 | -0.0506 | 0.0277 | -0.1463 | -0.1877 | -0.0719 | -0.0343 | -0.0144 | 0.0456 | 0.0761 | -0.0435 | 0.0476 | -0.1043 | -0.0290 | 0.0693 | -0.0875 | -0.0982 | -0.0396 | -0.0824 | 0.0092 | 0.0305 | -0.0544 | -0.0064 | -0.0750 | -0.0660 | -0.1997 | 0.1006 | 0.1992 | 0.1211 | 0.0821 | -0.0922 | -0.1259 | 0.2084 | -0.0985 | 0.1673 | -0.1711 | 0.4019 | 0.0737 | 0.1425 | 0.2960 | 0.0589 | -0.0849 | 0.0070 | -0.1239 | 0.0864 | 0.0800 | 0.0042 | 0.0365 | -0.2397 | 0.0124 | 0.0466 | -0.0200 |
write.csv(q2_pca$table, file = "coords/q2_pca_coords.csv")9 Request on 202101
Maria Adelaida sent an email which said: “I have been working on the gene lists and the easiest and cleanest way I found was to get the GSEA hallmark gene sets that may be of interest to us. The idea is that we use some of these gene lists (to star the xenobiotic list) to map the changes in gene expression over the course of treatment and in the different cell populations.”
Upon speaking with her, I think the goal is to have a plot with one line per MSigDB Hallmark category gene. The x-axis will therefore be the three time points for those patients for whom we have 3 visits; the y-axis will be normalized cpm. Each dot will be a single Hallmark gene.
With that in mind, the most difficult challenge is picking out the samples for which we have the three time points.
meta <- hpgltools::extract_metadata(samplesheet)## Dropped 103 rows from the sample metadata because they were blank.na_idx <- is.na(meta)
meta[na_idx] <- ""
visit_3_samples <- meta[["visitnumber"]] == 3
threes <- meta[visit_3_samples, ]
visit_2_samples <- meta[["visitnumber"]] == 2
twos <- meta[visit_2_samples, ]
visit_1_samples <- meta[["visitnumber"]] == 1
ones <- meta[visit_1_samples, ]
ones[["tubelabelorigin"]] %in% twos[["tubelabelorigin"]]## [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE FALSE
## [13] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [25] TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE FALSE
## [37] FALSE FALSE FALSE FALSE TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE
## [49] FALSE FALSE FALSE FALSE FALSE FALSE FALSEones_twos <- ones[["tubelabelorigin"]] %in% twos[["tubelabelorigin"]]
one_two <- meta[ones_twos, ]
all_three_idx <- threes[["tubelabelorigin"]] %in% one_two[["tubelabelorigin"]]
all_three <- threes[all_three_idx, ]
wanted_patients <- levels(as.factor(all_three[["tubelabelorigin"]]))
## So, it appears we have 3 visits for patients: su2068, su2072, and su2073.
plot_expt <- subset_expt(expt = all_norm, subset = "tubelabelorigin=='su2068'|tubelabelorigin=='su2072'|tubelabelorigin=='su2073'")## Using a subset expression.## There were 104, now there are 30 samples.plot_pca(plot_expt)$plot
plot_monocyte <- subset_expt(plot_expt, subset = "typeofcells=='Monocytes'&tubelabelorigin!='su2072'")## Using a subset expression.## There were 30, now there are 6 samples.up_monocyte_genes <- rownames(mono_sig[["deseq"]][["ups"]][["fail_vs_cure"]])
down_monocyte_genes <- rownames(mono_sig[["deseq"]][["downs"]][["fail_vs_cure"]])
plot_monocyte <- exclude_genes_expt(plot_monocyte, ids = down_monocyte_genes, method = "keep")## Before removal, there were 14652 entries.## Now there are 215 entries.## Percent kept: 1.714, 1.030, 1.381, 0.957, 1.352, 0.995## Percent removed: 98.286, 98.970, 98.619, 99.043, 98.648, 99.005hallmark_table <- openxlsx::read.xlsx(xlsxFile = "reference/geneset_selection_GSEA_Hallmark_modified.xlsx")
first_category <- hallmark_table[[1]]
hallmark_genes_idx <- fData(plot_monocyte)[["hgnc_symbol"]] %in% first_category
hallmark_genes <- fData(plot_monocyte)[hallmark_genes_idx, ]
wanted <- rownames(hallmark_genes)
plotted <- exprs(plot_monocyte)[wanted, ]
plot_long <- reshape2::melt(plotted)
colnames(plot_long) <- c("gene", "samplename", "exprs")
dim(plot_long)## [1] 24 3plot_long <- merge(plot_long, fData(plot_monocyte), by.x = "gene", by.y = "row.names")
dim(plot_long)## [1] 24 16plot_long <- merge(plot_long, pData(plot_monocyte), by = "samplename")
dim(plot_long)## [1] 24 97library(ggplot2)
plot_2068_idx <- plot_long[["tubelabelorigin"]] == "su2068"
plot_2068 <- plot_long[plot_2068_idx, ]
plt <- ggplot(data = plot_2068, mapping = aes_string(x = "visitnumber", y = "exprs",
colour = "gene")) +
geom_line() +
theme(legend.position = "none")
plt
probably_not <- ggplotly_url(plt, "probably_terrible_su2068.html")## Warning in ggplotly_url(plt, "probably_terrible_su2068.html"): No url df was
## provided, nor is there a column: url, not much to do.plot_2073_idx <- plot_long[["tubelabelorigin"]] == "su2073"
plot_2073 <- plot_long[plot_2073_idx, ]
plt <- ggplot(data = plot_2073, mapping = aes_string(x = "visitnumber", y = "exprs",
colour = "gene")) +
geom_line() +
theme(legend.position = "none")
plt
probably_not <- ggplotly_url(plt, "images/probably_terrible_su2073.html")## Warning in ggplotly_url(plt, "images/probably_terrible_su2073.html"): No url df
## was provided, nor is there a column: url, not much to do.if (!isTRUE(get0("skip_load"))) {
pander::pander(sessionInfo())
message(paste0("This is hpgltools commit: ", get_git_commit()))
message(paste0("Saving to ", savefile))
tmp <- sm(saveme(filename = savefile))
}## If you wish to reproduce this exact build of hpgltools, invoke the following:## > git clone http://github.com/abelew/hpgltools.git## > git reset 3866d0ef3d5bf766f01b092108ec06406921447c## This is hpgltools commit: Mon Mar 22 15:33:04 2021 -0400: 3866d0ef3d5bf766f01b092108ec06406921447c## Saving to tmrc3_02sample_estimation_v202103.rda.xztmp <- loadme(filename = savefile)---
title: "L. panamensis 202103: TMRC3 Sample Estimation"
author: "atb abelew@gmail.com"
date: "`r Sys.Date()`"
output:
 html_document:
  code_download: true
  code_folding: show
  fig_caption: true
  fig_height: 7
  fig_width: 7
  highlight: default
  keep_md: false
  mode: selfcontained
  number_sections: true
  self_contained: true
  theme: readable
  toc: true
  toc_float:
   collapsed: false
   smooth_scroll: false
---

<style>
  body .main-container {
    max-width: 1600px;
  }
</style>

```{r options, include = FALSE}
library(hpgltools)
tt <- sm(devtools::load_all("~/hpgltools"))
knitr::opts_knit$set(progress = TRUE,
                     verbose = TRUE,
                     width = 90,
                     echo = TRUE)
knitr::opts_chunk$set(error = TRUE,
                      fig.width = 8,
                      fig.height = 8,
                      dpi = 96)
old_options <- options(digits = 4,
                       stringsAsFactors = FALSE,
                       knitr.duplicate.label = "allow")
ggplot2::theme_set(ggplot2::theme_bw(base_size = 12))
ver <- "202103"
rundate <- format(Sys.Date(), format = "%Y%m%d")

rmd_file <- "tmrc3_02sample_estimation_v202103.Rmd"
savefile <- gsub(pattern = "\\.Rmd", replace = "\\.rda\\.xz", x = rmd_file)
```

# Introduction

This document is intended to provide an overview of TMRC3 samples which have
been sequenced.  It includes some plots and analyses showing the relationships
among the samples as well as some differential analyses when possible.

# Annotation

We take the annotation data from ensembl's biomart instance.  The genome which
was used to map the data was hg38 revision 91.  My default when using biomart is
to load the data from 1 year before the current date, which provides annotations
which match hg38 91 almost perfectly.

```{r hs_annot}
hs_annot <- load_biomart_annotations()
hs_annot <- hs_annot[["annotation"]]
hs_annot[["transcript"]] <- paste0(rownames(hs_annot), ".", hs_annot[["version"]])
rownames(hs_annot) <- make.names(hs_annot[["ensembl_gene_id"]], unique = TRUE)
tx_gene_map <- hs_annot[, c("transcript", "ensembl_gene_id")]
```

```{r hs_go}
hs_go <- load_biomart_go()[["go"]]
hs_length <- hs_annot[, c("ensembl_gene_id", "cds_length")]
colnames(hs_length) <- c("ID", "length")
```

# Sample Estimation

I used two mapping methods for this data, hisat2 and salmon.  Most analyses use
hisat2, which is a more traditional map-and-count method.  In contrast, salmon
uses what may be thought of as a indexed voting method (so that multi-matches are
discounted and the votes split among all matches).  Salmon also required a
pre-existing database of known transcripts (though later versions may actually
use mapping from things like hisat), while hisat uses the genome and a database
of known transcripts (and optionally can search for splicing junctions to find
new transcripts).

## Generate expressionsets

```{r samplehsheet}
samplesheet <- "sample_sheets/tmrc3_samples_202103.xlsx"
```

Caveat: This initial section is using salmon quantifications.  A majority of analyses used hisat2.

### Salmon expressionsets

Currently, I have these disabled.

### Hisat2 expressionsets

The first thing to note is the large range in coverage.  There are multiple
samples with coverage which is too low to use.  These will be removed shortly.

```{r all_new_hisat2}
hs_expt <- create_expt(samplesheet,
                       file_column = "hg38100hisatfile",
                       savefile = glue::glue("hs_expt_all-v{ver}.rda"),
                       gene_info = hs_annot)
hs_expt <- exclude_genes_expt(hs_expt, column = "gene_biotype",
                              method = "keep", pattern = "protein_coding")

libsizes <- plot_libsize(hs_expt)
libsizes$plot

nonzero <- plot_nonzero(hs_expt)
nonzero$plot
box <- plot_boxplot(hs_expt)
box
```

## Minimum coverage sample filtering

I arbitrarily chose 3,000,000 counts as a minimal level of coverage.  We may
want this to be higher.

```{r hisat2_write, fig.show = "hide"}
hs_valid <- subset_expt(hs_expt, coverage = 3000000)
plot_libsize(hs_valid)$plot

valid_write <- sm(write_expt(hs_valid, excel = glue("excel/hs_valid-v{ver}.xlsx")))
```

# Questions from Maria Adelaida

The following comes from an email 20190830 from Maria Adelaida.

1. Samples WT1010 and WT1011 PBMCs from two healthy donors processed 2h, 7h and
   12h after sample procurement. This is an analysis to explore the time-effect
   on gene expression and define steps for data analysis for patient samples
   considering time-dependent effects.

   a. An initial PCA on the raw data would be very useful to see if there is
   clustering based on time or (as usual), mostly a donor-specific effect. Then
   I think a hierarchical clustering of genes based on time-dependent
   modifications to see what is mostly affected (if any) - like what you guys
   did for T.cruzi.

2. Samples from SU1017, SU1034 Samples from TMRC CL patients. m= monocyte, n=
   neutrophil. Samples labeled "1" are taken before treatment and those "2" mid way
   through treatment. This is exiting, because these will be our first
   neutrophil transcriptomes.

In an attempt to poke at these questions, I mapped the reads to hg38_91 using
salmon and hisat2.  It is very noteworthy that the salmon mappings are
exhibiting some serious problems and should be looked into further.  The hisat2
mappings are significantly more 'normal'.  Having said that, two samples remain
basically unusable: tmrc30009 (1034n1) and (to a smaller degree) tmrc30007
(1017n1) have too few reads as shown above.

## Preparation

To address these, I added to the end of the sample sheet columns named
'condition', 'batch', 'donor', and 'time'.  These are filled in with shorthand
values according to the above.

## Global view

Before addressing the questions explicitly by subsetting the data, I want to get
a look at the samples as they are.

```{r pre_questions}
hs_valid <- set_expt_batches(hs_valid, fact = "donor")
hs_valid <- set_expt_samplenames(hs_valid, newnames = pData(hs_valid)[["samplename"]])
all_norm <- normalize_expt(hs_valid, convert = "cpm", filter = TRUE)
all_norm <- normalize_expt(all_norm, transform = "log2")

plt <- plot_pca(all_norm, plot_labels = FALSE)$plot
pp(file = glue("images/tmrc3_pca_nolabels-v{ver}.pdf"), image = plt)
all_ts <- plot_tsne(all_norm)
all_ts$plot

knitr::kable(all_pca$table)
write.csv(all_pca$table, file = "coords/hs_donor_pca_coords.csv")
plot_corheat(all_norm)$plot
plot_topn(hs_valid)$plot
```

## All celltypes clinical outcome

```{r all_clinical}
hs_clinical <- subset_expt(hs_valid, subset = "condition!='PBMC'&condition!='Macrophage'")
hs_clinical <- set_expt_conditions(hs_clinical, fact = "clinicaloutcome")
hs_clinical <- set_expt_batches(hs_clinical, fact = "typeofcells")
chosen_colors <- c("#D95F02", "#7570B3", "#1B9E77", "#FF0000")
names(chosen_colors) <- c("cure", "failure", "lost", "null")
hs_clinical <- set_expt_colors(expt = hs_clinical, colors = chosen_colors)
hs_clinical <- set_expt_samplenames(expt = hs_clinical, newnames = pData(hs_clinical)$samplename)
hs_clinical_norm <- normalize_expt(hs_clinical, filter = TRUE, convert = "cpm", norm = "quant")
hs_clinical_norm <- normalize_expt(hs_clinical, transform = "log2")
##plt <- plot_pca(hs_clinical_norm, plot_labels = FALSE, size_column = "visitnumber")$plot
plt <- plot_pca(hs_clinical_norm, plot_labels = FALSE)$plot
pp(file = glue("images/all_clinical_nobatch_pca-v{ver}.png"), image = plt, heigh = 8, width = 20)

plt <- plot_pca(hs_clinical_norm, plot_labels = FALSE, size_column = "visitnumber")$plot
pp(file = glue("images/all_clinical_nobatch_pca_sized-v{ver}.png"), image = plt, heigh = 8, width = 20)

plt <- plot_pca(hs_clinical_norm)$plot
pp(file = glue("images/all_clinical_nobatch_pca_labeled-v{ver}.png"), height = 8, width = 20, image = plt)
```

# Repeat without Biopsies

```{r clinical_no_biopies}
hs_clinic <- subset_expt(hs_clinical, subset = "batch!='Biopsy'")
hs_clinic_norm <- normalize_expt(hs_clinic, filter = TRUE, convert = "cpm", norm = "quant")
hs_clinic_norm <- normalize_expt(hs_clinic, transform = "log2")
plt <- plot_pca(hs_clinic_norm, plot_labels = FALSE)$plot
pp(file = glue("images/no_biopsy_clinical_nobatch_pca-v{ver}.png"),
   height = 8, width = 20, image = plt)

plt <- plot_pca(hs_clinic_norm, plot_labels = FALSE, size_column = "visitnumber")$plot

plt <- plot_pca(hs_clinic_norm)$plot
pp(file = glue("images/no_biopsy_clinical_nobatch_pca_labeled-v{ver}.png"),
   height = 8, width = 20, image = plt)

hs_clinic_de <- all_pairwise(hs_clinic, model_batch = FALSE)
hs_clinic_table <- combine_de_tables(hs_clinic,
                                     excel = glue::glue("excel/cure_separate_lost_failed_table-v{ver}.xlsx"))
hs_clinic_sig <- extract_significant_genes(hs_clinic_table,
                                           excel = glue::glue("excel/cure_separate_lost_failed_sig-v{ver}.xlsx"))
```

# Combine cure and lost

Najib seems interested in this, but was curious to see how they look when the
cure and lost samples are combined into a single group.

```{r curelost_together}
cl_idx <- pData(hs_clinical)[["condition"]] == "cure" |
  pData(hs_clinical)[["condition"]] == "lost"
new_factor <- pData(hs_clinical)[["condition"]]
new_factor[cl_idx] <- "cure_lost"
hs_cl <- set_expt_conditions(hs_clinical, fact = new_factor)

test <- normalize_expt(hs_cl, transform = "log2", convert = "cpm", norm = "quant", filter = TRUE)
plot_pca(test)$plot

hs_clv2 <- subset_expt(hs_cl, subset = "batch!='Biopsy'")
hs_clv2_norm <- normalize_expt(hs_clv2, filter = TRUE, convert = "cpm", norm = "quant")
hs_clv2_norm <- normalize_expt(hs_clv2, transform = "log2")
plt <- plot_pca(hs_clv2_norm, plot_labels = FALSE)$plot
plt

hs_clv2_de <- all_pairwise(hs_clv2, model_batch = TRUE)
hs_clv2_table <- combine_de_tables(hs_clv2_de,
                                   excel = glue::glue("excel/curelost_failed_table-v{ver}.xlsx"))
hs_clv2_sig <- extract_significant_genes(hs_clv2_table,
                                         excel = glue::glue("excel/curelost_failed_sig-v{ver}.xlsx"))

ups <- hs_clv2_sig[["deseq"]][["ups"]][[1]]
downs <- hs_clv2_sig[["deseq"]][["downs"]][[1]]

hs_clv2_gprofiler_ups <- simple_gprofiler(ups)
hs_clv2_gprofiler_ups$pvalue_plots$reactome_plot_over

hs_clv2_gprofiler_downs <- simple_gprofiler(downs)
hs_clv2_gprofiler_downs$pvalue_plots$bpp_plot_over
hs_clv2_gprofiler_downs$pvalue_plots$reactome_plot_over
```

```{r gsva}
hs_clv2_gsva <- simple_gsva(hs_clv2)
hs_clv2_gsva_sig <- get_sig_gsva_categories(hs_clv2_gsva)

broad_c7 <- GSEABase::getGmt("reference/msigdb/c7.all.v7.2.entrez.gmt",
                             collectionType = GSEABase::BroadCollection(category = "c7"),
                             geneIdType = GSEABase::EntrezIdentifier())
testing <- simple_gsva(hs_clv2, signatures = broad_c7,
                       msig_xml = "reference/msigdb_v7.2.xml", cores = 10)
test_sig <- get_sig_gsva_categories(testing, excel = "excel/test_gsva.xlsx")
```

# Cell types

The following blocks split the samples into a few groups by sample type and look
at the distributions between them.

## Implementation details

Get top/bottom n genes for each cell type, using clinical outcome as the factor of interest.
For the moment, use sva for the DE analysis.
Provide cpms for the top/bottom n genes.

Start with top/bottom 200.
Perform default logFC and p-value as well.

### Shared contrasts

Here is the contrast we will use throughput, I am leaving open the option to add more.

```{r keepers}
keepers <- list(
  "fail_vs_cure" = c("failure", "cure"))
```

# NOTE

Sample sheet rows 11-14 had the clinical outcome set to 'NA' instead of cure/fail/lost
In addition, some newer entries were 'Cure/Failure' instead of 'cure/failure', I changed this.

## Monocytes

```{r monocytes}
mono <- subset_expt(hs_valid, subset = "typeofcells=='Monocytes'")
mono <- set_expt_conditions(mono, fact = "clinicaloutcome")
mono <- set_expt_batches(mono, fact = "donor")
## FIXME set_expt_colors should speak up if there are mismatches here!!!
mono <- set_expt_colors(expt = mono, colors = chosen_colors)
save_result <- save(mono, file = "rda/monocyte_expt.rda")
mono_norm <- normalize_expt(mono, convert = "cpm", filter = TRUE)
mono_norm <- normalize_expt(mono_norm, transform = "log2")
plt <- plot_pca(mono_norm, plot_labels = FALSE)$plot
pp(file = glue("images/mono_pca_normalized-v{ver}.pdf"), image = plt)
plt
mono_de <- sm(all_pairwise(mono, model_batch = FALSE, filter = TRUE))
mono_tables <- combine_de_tables(mono_de, keepers = keepers,
                                 excel = glue::glue("excel/monocyte_clinical_all_tables-v{ver}.xlsx"))
written <- write_xlsx(data = mono_tables[["data"]][[1]],
                      excel = glue::glue("excel/monocyte_clinical_table-v{ver}.xlsx"))
mono_sig <- extract_significant_genes(mono_tables, according_to = "deseq")
written <- write_xlsx(data = mono_sig[["deseq"]][["ups"]][[1]],
                      excel = glue::glue("excel/monocyte_clinical_sigup-v{ver}.xlsx"))
written <- write_xlsx(data = mono_sig[["deseq"]][["downs"]][[1]],
                      excel = glue::glue("excel/monocyte_clinical_sigdown-v{ver}.xlsx"))
mono_pct_sig <- extract_significant_genes(mono_tables, n = 200, lfc = NULL, p = NULL, according_to = "deseq")
written <- write_xlsx(data = mono_pct_sig[["deseq"]][["ups"]][[1]],
                      excel = glue::glue("excel/monocyte_clinical_sigup_pct-v{ver}.xlsx"))
written <- write_xlsx(data = mono_pct_sig[["deseq"]][["downs"]][[1]],
                      excel = glue::glue("excel/monocyte_clinical_sigdown_pct-v{ver}.xlsx"))
mono_sig$summary_df

mono_cpm <- sm(normalize_expt(mono, convert = "cpm"))
written <- write_xlsx(data = exprs(mono_cpm),
                      excel = glue::glue("excel/monocyte_cpm_before_batch-v{ver}.xlsx"))
mono_bcpm <- sm(normalize_expt(mono, filter = TRUE, convert = "cpm"))
written <- write_xlsx(data = exprs(mono_bcpm),
                      excel = glue::glue("excel/monocyte_cpm_after_batch-v{ver}.xlsx"))
```

```{r mono_gsva_goseq}
ups <- mono_sig[["deseq"]][["ups"]][["fail_vs_cure"]]
downs <- mono_sig[["deseq"]][["downs"]][["fail_vs_cure"]]

up_goseq <- simple_goseq(sig_genes = ups, go_db = hs_go, length_db = hs_length)
```

```{r gsva_goseq}
broad_c7 <- GSEABase::getGmt("reference/msigdb/c7.all.v7.2.entrez.gmt",
                             collectionType = GSEABase::BroadCollection(category = "c7"),
                             geneIdType = GSEABase::EntrezIdentifier())

up_goseq_gsva <- goseq_msigdb(sig_genes = ups, signatures = broad_c7,
                              signature_category = "c7", length_db = hs_length)
```

## Neutrophils

```{r neutrophils}
neut <- subset_expt(hs_valid, subset = "typeofcells=='Neutrophils'")
neut <- set_expt_conditions(neut, fact = "clinicaloutcome")
neut <- set_expt_batches(neut, fact = "donor")
neut <- set_expt_colors(expt = neut, colors = chosen_colors)
save_result <- save(neut, file = "rda/neutrophil_expt.rda")
neut_norm <- sm(normalize_expt(neut, convert = "cpm", filter = TRUE))
neut_norm <- normalize_expt(neut_norm, transform = "log2")
plt <- plot_pca(neut_norm, plot_labels = FALSE)$plot
pp(file = glue("images/neut_pca_normalized-v{ver}.pdf"), image = plt)
plt
neut_de <- sm(all_pairwise(neut, model_batch = FALSE, filter = TRUE))
neut_tables <- combine_de_tables(neut_de, keepers = keepers,
                                 excel = glue::glue("excel/neutrophil_clinical_all_tables-v{ver}.xlsx"))
written <- write_xlsx(data = neut_tables[["data"]][[1]],
                      excel = glue::glue("excel/neutrophil_clinical_table-v{ver}.xlsx"))
neut_sig <- extract_significant_genes(neut_tables, according_to = "deseq")
written <- write_xlsx(data = neut_sig[["deseq"]][["ups"]][[1]],
                      excel = glue::glue("excel/neutrophil_clinical_sigup-v{ver}.xlsx"))
written <- write_xlsx(data = neut_sig[["deseq"]][["downs"]][[1]],
                      excel = glue::glue("excel/neutrophil_clinical_sigdown-v{ver}.xlsx"))
neut_pct_sig <- extract_significant_genes(neut_tables, n = 200, lfc = NULL, p = NULL, according_to = "deseq")
written <- write_xlsx(data = neut_sig[["deseq"]][["ups"]][[1]],
                      excel = glue::glue("excel/neutrophil_clinical_sigup_pct-v{ver}.xlsx"))
written <- write_xlsx(data = neut_sig[["deseq"]][["downs"]][[1]],
                      excel = glue::glue("excel/neutrophil_clinical_sigdown_pct-v{ver}.xlsx"))
neut_cpm <- sm(normalize_expt(neut, convert = "cpm"))
written <- write_xlsx(data = exprs(neut_cpm),
                      excel = glue::glue("excel/neutrophil_cpm_before_batch-v{ver}.xlsx"))
neut_bcpm <- sm(normalize_expt(neut, filter = TRUE, convert = "cpm"))
written <- write_xlsx(data = exprs(neut_bcpm),
                      excel = glue::glue("excel/neutrophil_cpm_after_batch-v{ver}.xlsx"))
```

## Eosinophils

```{r eosinophils}
eo <- subset_expt(hs_valid, subset = "typeofcells=='Eosinophils'")
eo <- set_expt_conditions(eo, fact = "clinicaloutcome")
eo <- set_expt_batches(eo, fact = "donor")
eo <- set_expt_colors(expt = eo, colors = chosen_colors)
save_result <- save(eo, file = "rda/eosinophil_expt.rda")
eo_norm <- sm(normalize_expt(eo, convert = "cpm", filter = TRUE))
eo_norm <- normalize_expt(eo_norm, transform = "log2")
plt <- plot_pca(eo_norm, plot_labels = FALSE)$plot
pp(file = glue("images/eo_pca_normalized-v{ver}.pdf"), image = plt)
plt
eo_de <- sm(all_pairwise(eo, model_batch = FALSE, filter = TRUE))
eo_tables <- combine_de_tables(eo_de, keepers = keepers,
                               excel = glue::glue("excel/eosinophil_clinical_all_tables-v{ver}.xlsx"))
written <- write_xlsx(data = eo_tables[["data"]][[1]],
                      excel = glue::glue("excel/eosinophil_clinical_table-v{ver}.xlsx"))
eo_sig <- extract_significant_genes(eo_tables, according_to = "deseq")
written <- write_xlsx(data = eo_sig[["deseq"]][["ups"]][[1]],
                      excel = glue::glue("excel/eosinophil_clinical_sigup-v{ver}.xlsx"))
written <- write_xlsx(data = eo_sig[["deseq"]][["downs"]][[1]],
                      excel = glue::glue("excel/eosinophil_clinical_sigdown-v{ver}.xlsx"))
eo_pct_sig <- extract_significant_genes(eo_tables, n = 200, lfc = NULL, p = NULL, according_to = "deseq")
written <- write_xlsx(data = eo_pct_sig[["deseq"]][["ups"]][[1]],
                      excel = glue::glue("excel/eosinophil_clinical_sigup_pct-v{ver}.xlsx"))
written <- write_xlsx(data = eo_pct_sig[["deseq"]][["downs"]][[1]],
                      excel = glue::glue("excel/eosinophil_clinical_sigdown_pct-v{ver}.xlsx"))

eo_cpm <- sm(normalize_expt(eo, convert = "cpm"))
written <- write_xlsx(data = exprs(eo_cpm),
                      excel = glue::glue("excel/eosinophil_cpm_before_batch-v{ver}.xlsx"))
eo_bcpm <- sm(normalize_expt(eo, filter = TRUE, convert = "cpm"))
written <- write_xlsx(data = exprs(eo_bcpm),
                      excel = glue::glue("excel/eosinophil_cpm_after_batch-v{ver}.xlsx"))
```

## Biopsies

```{r biopsies}
biop <- subset_expt(hs_valid, subset = "typeofcells=='Biopsy'")
biop <- set_expt_conditions(biop, fact = "clinicaloutcome")
biop <- set_expt_batches(biop, fact = "donor")
biop <- set_expt_colors(expt = biop, colors = chosen_colors)
save_result <- save(biop, file = "rda/biopsy_expt.rda")
biop_norm <- normalize_expt(biop, filter = TRUE, convert = "cpm")
biop_norm <- normalize_expt(biop_norm, transform = "log2")
plt <- plot_pca(biop_norm, plot_labels = FALSE)$plot
pp(file = glue("images/biop_pca_normalized-v{ver}.pdf"), image = plt)
plt
biop_de <- sm(all_pairwise(biop, model_batch = FALSE, filter = TRUE))
biop_tables <- combine_de_tables(biop_de, keepers = keepers,
                                 excel = glue::glue("excel/biopsy_clinical_all_tables-v{ver}.xlsx"))
written <- write_xlsx(data = biop_tables[["data"]][[1]],
                      excel = glue::glue("excel/biopsy_clinical_table-v{ver}.xlsx"))
biop_sig <- extract_significant_genes(biop_tables, according_to = "deseq")
##written <- write_xlsx(data = biop_sig[["deseq"]][["ups"]][[1]],
##                      excel = glue::glue("excel/biopsy_clinical_sigup-v{ver}.xlsx"))
written <- write_xlsx(data = biop_sig[["deseq"]][["downs"]][[1]],
                      excel = glue::glue("excel/biopsy_clinical_sigdown-v{ver}.xlsx"))
biop_pct_sig <- extract_significant_genes(biop_tables, n = 200, lfc = NULL, p = NULL, according_to = "deseq")
written <- write_xlsx(data = biop_pct_sig[["deseq"]][["ups"]][[1]],
                      excel = glue::glue("excel/biopsy_clinical_sigup_pct-v{ver}.xlsx"))
written <- write_xlsx(data = biop_pct_sig[["deseq"]][["downs"]][[1]],
                      excel = glue::glue("excel/biopsy_clinical_sigdown_pct-v{ver}.xlsx"))

biop_cpm <- sm(normalize_expt(biop, convert = "cpm"))
written <- write_xlsx(data = exprs(biop_cpm),
                      excel = glue::glue("excel/biopsy_cpm_before_batch-v{ver}.xlsx"))
biop_bcpm <- sm(normalize_expt(biop, filter = TRUE, convert = "cpm"))
written <- write_xlsx(data = exprs(biop_bcpm),
                      excel = glue::glue("excel/biopsy_cpm_after_batch-v{ver}.xlsx"))
```

## Macrophages

These samples are rather different from all of the others.  The following
section is therefore written primarily in response to a separate set of emails
from Olga and Maria Adelaida; here is a snippet:

 Dear all, about the samples corresponding to infected macrophages with three
 sensitive (2.2) and three resistant (2.3) clinical strains of L. (V.)
 panamensis, I send you the results of parasite burden by detection of 7SLRNA. I
 think these results are interesting, but the sample size is very small.
Doctor Najib or Trey could you please send me the quality data and PCA analysis
 of these samples?

and

 Hi Doctor, thank you.  These samples corresponding to primary macrophages
 infected with clinical strains 2.2 (n = 3) and 2.3 (n = 3). These information is
 in the file: TMRC project 3: excel Host TMRC3 v1.1, rows 137 to 150.

Thus I added 3 columns to the end of the sample sheet which seek to
include this information.  The first is 'drug' and encodes both the infection
state (no for the two controls and yes for everything else), the second is
zymodeme which I took from the tmrc2 sample sheet, the last is drug, which is
either no or sb.

```{r macrophage_zymodeme_experiment}
macr <- subset_expt(hs_valid, subset = "typeofcells=='Macrophages'")
macr <- set_expt_conditions(macr, fact = "zymodeme")
macr <- set_expt_batches(macr, fact = "macrdrug")

macr_norm <- normalize_expt(macr, norm = "quant", convert = "cpm", filter = TRUE)
macr_norm <- normalize_expt(macr_norm, transform = "log2")
plt <- plot_pca(macr_norm, plot_labels = FALSE)$plot
pp(file = glue("images/macrophage_side_experiment_norm_pca-v{ver}.pdf"), image = plt)
plt

macr_nb <- normalize_expt(macr, filter = TRUE)
macr_nb <- normalize_expt(macr_nb, norm = "quant", convert = "cpm", transform = "log2")
plt <- plot_pca(macr_nb)$plot
pp(file = glue("images/macrophage_side_experiment-v{ver}/normbatch_pca.pdf"), image = plt)
plt
macr_written <- write_expt(macr, excel = "excel/macrophage_side_experiment/macrophage_expt.xlsx")

zymo_de <- all_pairwise(macr, model_batch = FALSE, filter = TRUE)
zymo_table <- combine_de_tables(zymo_de, excel = "images/macrophage_side_experiment/macrophage_de.xlsx")
```

### Followup email from Olga

- Macrophages infected with 2.3 vs macrophagues infected with 2.3 + SbV
- Macrophages uninfected (M0) vs macrophages uninfected + SbV
- Macrophages uninfected vs macrophages infected with 2.3
- Macrophages uninfected + SbV vs Macrophagues infected with 2.3 + SbV

With this comparisons we can define the effect of infection with 2.3 and the
drug effect with and without infection. In this moment we can't evaluate the
conditions with 2.2 because the samples are incomplete. Mariana will complete the
samples in the next shipment.

```{r olga_followup}
new_factor <- paste0(pData(macr)[["macrdrug"]], "_", pData(macr)[["zymodeme"]])
new_factor <- gsub(x = new_factor, pattern = "undef", replacement = "uninf")
macr_v2 <- set_expt_conditions(macr, fact = new_factor)
macr_v2$conditions
macr_v2_norm <- normalize_expt(macr_v2, onvert = "cpm", filter = TRUE, norm = "quant")
macr_v2_norm <- normalize_expt(macr_v2_norm, transform = "log2")
plot_pca(macr_v2_norm)$plot
keepers <- list(
  "zymo_sbv" = c("sb_z22", "sb_z23"),
  "uninf_drug" = c("no_uninf", "sb_uninf"),
  "uninfnodrug_z23" = c("no_uninf", "no_z23"),
  "uninfdrug_z23" = c("sb_uninf", "sb_z23"),
  "z23_drug_nodrug" = c("sb_z23", "no_z23"),
  "z22_drug_nodrug" = c("sb_z22", "no_z22")
)
olga_pairwise <- all_pairwise(macr_v2, do_deseq = FALSE, do_edger = FALSE, do_ebseq = FALSE)

olga_zymo_sb <- olga_pairwise[["limma"]][["all_tables"]][["sb_z23_vs_sb_z22"]]
olga_zymo_sb <- merge(olga_zymo_sb, fData(macr_v2), by = "row.names")
rownames(olga_zymo_sb) <- olga_zymo_sb[["Row.names"]]
olga_zymo_sb[["Row.names"]] <- NULL
olga_zymo_sb[["transcript"]] <- NULL
written <- write_xlsx(data = olga_zymo_sb,
                      excel = glue::glue("excel/sb_23_vs22-v{ver}.xlsx"))

olga_uninf_drug <- olga_pairwise[["limma"]][["all_tables"]][["sb_uninf_vs_no_uninf"]]
olga_uninf_drug <- merge(olga_uninf_drug, fData(macr_v2), by = "row.names")
rownames(olga_uninf_drug) <- olga_uninf_drug[["Row.names"]]
olga_uninf_drug[["Row.names"]] <- NULL
written <- write_xlsx(data = olga_uninf_drug,
                      excel = glue::glue("excel/uninf_sb_vs_nosb-v{ver}.xlsx"))

olga_uninfnodrug_z23 <- olga_pairwise[["limma"]][["all_tables"]][["no_z23_vs_no_uninf"]]
olga_uninfnodrug_z23 <- merge(olga_uninfnodrug_z23, fData(macr_v2), by = "row.names")
rownames(olga_uninfnodrug_z23) <- olga_uninfnodrug_z23[["Row.names"]]
olga_uninfnodrug_z23[["Row.names"]] <- NULL
written <- write_xlsx(data = olga_uninfnodrug_z23,
                      excel = glue::glue("excel/no_z23_vs_nosb_uninf-v{ver}.xlsx"))

olga_uninfdrug_z23 <- olga_pairwise[["limma"]][["all_tables"]][["sb_z23_vs_sb_uninf"]]
olga_uninfdrug_z23 <- merge(olga_uninfdrug_z23, fData(macr_v2), by = "row.names")
rownames(olga_uninfdrug_z23) <- olga_uninfdrug_z23[["Row.names"]]
olga_uninfdrug_z23[["Row.names"]] <- NULL
written <- write_xlsx(data = olga_uninfdrug_z23,
                      excel = glue::glue("excel/sb_z23_vs_sb_uninf-v{ver}.xlsx"))

olga_z23_drugnodrug <- olga_pairwise[["limma"]][["all_tables"]][["sb_z23_vs_no_z23"]]
olga_z23_drugnodrug <- merge(olga_z23_drugnodrug, fData(macr_v2), by = "row.names")
rownames(olga_z23_drugnodrug) <- olga_z23_drugnodrug[["Row.names"]]
olga_z23_drugnodrug[["Row.names"]] <- NULL
olga_z23_drugnodrug[["transcript"]] <- NULL
written <- write_xlsx(data = olga_z23_drugnodrug,
                      excel = glue::glue("excel/z23_drug_vs_nodrug-v{ver}.xlsx"))

olga_z22_drugnodrug <- olga_pairwise[["limma"]][["all_tables"]][["sb_z22_vs_no_z22"]]
olga_z22_drugnodrug <- merge(olga_z22_drugnodrug, fData(macr_v2), by = "row.names")
rownames(olga_z22_drugnodrug) <- olga_z22_drugnodrug[["Row.names"]]
olga_z22_drugnodrug[["Row.names"]] <- NULL
olga_z22_drugnodrug[["transcript"]] <- NULL
written <- write_xlsx(data = olga_z22_drugnodrug,
                      excel = glue::glue("excel/z22_drug_vs_nodrug-v{ver}.xlsx"))
```

## Donor effect

```{r donor}
donor <- set_expt_conditions(hs_expt, fact = "donor")
donor <- set_expt_batches(donor, fact = "time")
save_result <- save(donor, file = "rda/donor_expt.rda")
test <- normalize_expt(donor, filter = TRUE, transform = "log2", convert = "cpm", norm = "quant")
plot_pca(test)$plot
```

## Question 1

I interpreted question 1 as: pull out tmrc3000[1-6] and look at them.

I am not entirely certain what is meant by the heirarchical clustering request.
I can see a couple of possibilities for what this means.  The most similar thing
I recall in the cruzi context was a post-DE visualization of some fairly
specific genes.

```{r question1}
hs_q1 <- subset_expt(hs_valid, subset = "donor=='d1010'|donor=='d1011'")
q1_norm <- sm(normalize_expt(hs_q1, norm = "quant", transform = "log2", convert = "cpm", batch = FALSE,
                           filter = TRUE))
q1_pca <- plot_pca(q1_norm)
q1_pca$plot

knitr::kable(q1_pca$table)
write.csv(q1_pca$table, file = "coords/q1_pca_coords.csv")

## Looks like PC1 == Time and PC2 is donor, and they have pretty much the same amount of variance

hs_time <- set_expt_conditions(hs_q1, fact = "time")
time_norm <- sm(normalize_expt(hs_time, filter = TRUE))
time_norm <- normalize_expt(time_norm, transform = "log2")
## Get a set of genes with high PC loads for PC1(time), and PC2(donor):
high_scores <- pca_highscores(time_norm, n = 40)
time_stuff <- high_scores$highest[, c(1, 2)]
time_stuff
## Column 1 should be winners vs. time and column 2 by donor.
time_genes <- gsub(x = time_stuff[, "Comp.1"], pattern = "^.*:(.*)$", replacement = "\\1")
head(time_genes)
time_subset <- exprs(time_norm)[time_genes, ]
plot_sample_heatmap(time_norm, row_label = rownames(time_norm))

hs_donor <- set_expt_conditions(hs_q1, fact = "donor")
donor_norm <- sm(normalize_expt(hs_donor, convert = "cpm", filter = TRUE))
donor_norm <- normalize_expt(donor_norm, transform = "log2")
## Get a set of genes with high PC loads for PC1(donor), and PC2(donor):
high_scores <- pca_highscores(donor_norm, n = 40)
donor_stuff <- high_scores$highest[, c(1, 2)]
donor_stuff
## Column 1 should be winners vs. donor and column 2 by donor.
donor_genes <- gsub(x = donor_stuff[, "Comp.1"], pattern = "^.*:(.*)$", replacement = "\\1")
head(donor_genes)
donor_subset <- exprs(donor_norm)[donor_genes, ]
plot_sample_heatmap(donor_norm, row_label = rownames(donor_norm))
```

```{r time_donor_de, fig.show = "hide"}
time_keepers <- list(
  "2hr_vs_7hr" = c("t2hr", "t7hr"),
  "2hr_vs_12hr" = c("t2hr", "t12hr"),
  "7hr_vs_12hr" = c("t7hr", "t12hr"))

q1_time <- set_expt_conditions(hs_q1, fact = "time")
time_de <- sm(all_pairwise(q1_time, model_batch = FALSE, parallel = FALSE))
time_all_tables <- sm(combine_de_tables(time_de,
                                        excel = glue::glue("excel/time_de_tables-v{ver}.xlsx")))
save_result <- save(time_all_tables, file = "rda/time_all_tables.rda")

time_all_tables_all <- sm(combine_de_tables(
  time_de,
  keepers = "all",
  excel = glue::glue("excel/time_de_all_tables-v{ver}.xlsx")))

time_all_tables <- sm(combine_de_tables(
  time_de,
  keepers = time_keepers,
  excel = glue::glue("excel/{rundate}-time_de_tables-v{ver}.xlsx")))

q1_donor <- set_expt_conditions(hs_q1, fact = "donor")
donor_de <- sm(all_pairwise(q1_donor, model_batch = FALSE))
donor_tables <- sm(combine_de_tables(
                     donor_de, excel = glue::glue("excel/donor_de_tables-v{ver}.xlsx")))
save_result <- save(donor_tables, file = "rda/donor_tables.rda")
```

```{r question2}
hs_q2 <- subset_expt(hs_valid, subset = "donor!='d1010'&donor!='d1011'")
q2_norm <- sm(normalize_expt(hs_q2, transform = "log2", convert = "cpm", norm = "quant", filter = TRUE))

q2_pca <- plot_pca(q2_norm)
knitr::kable(q2_pca$table)
write.csv(q2_pca$table, file = "coords/q2_pca_coords.csv")
```

# Request on 202101

Maria Adelaida sent an email which said: "I have been working on the gene lists
and the easiest and cleanest way I found was to get the GSEA hallmark gene sets
that may be of interest to us.  The idea is that we use some of these gene lists
(to star the xenobiotic list) to map the changes in gene expression over the
course of treatment and in the different cell populations."

Upon speaking with her, I think the goal is to have a plot with one line per
MSigDB Hallmark category gene.  The x-axis will therefore be the three time
points for those patients for whom we have 3 visits; the y-axis will be
normalized cpm.  Each dot will be a single Hallmark gene.

With that in mind, the most difficult challenge is picking out the samples for
which we have the three time points.

```{r three_timepoint_line}
meta <- hpgltools::extract_metadata(samplesheet)
na_idx <- is.na(meta)
meta[na_idx] <- ""

visit_3_samples <- meta[["visitnumber"]] == 3
threes <- meta[visit_3_samples, ]
visit_2_samples <- meta[["visitnumber"]] == 2
twos <- meta[visit_2_samples, ]
visit_1_samples <- meta[["visitnumber"]] == 1
ones <- meta[visit_1_samples, ]

ones[["tubelabelorigin"]] %in% twos[["tubelabelorigin"]]
ones_twos <- ones[["tubelabelorigin"]] %in% twos[["tubelabelorigin"]]
one_two <- meta[ones_twos, ]
all_three_idx <- threes[["tubelabelorigin"]] %in% one_two[["tubelabelorigin"]]
all_three <- threes[all_three_idx, ]
wanted_patients <- levels(as.factor(all_three[["tubelabelorigin"]]))

## So, it appears we have 3 visits for patients: su2068, su2072, and su2073.
plot_expt <- subset_expt(expt = all_norm, subset = "tubelabelorigin=='su2068'|tubelabelorigin=='su2072'|tubelabelorigin=='su2073'")
plot_pca(plot_expt)$plot

plot_monocyte <- subset_expt(plot_expt, subset = "typeofcells=='Monocytes'&tubelabelorigin!='su2072'")
up_monocyte_genes <- rownames(mono_sig[["deseq"]][["ups"]][["fail_vs_cure"]])
down_monocyte_genes <- rownames(mono_sig[["deseq"]][["downs"]][["fail_vs_cure"]])
plot_monocyte <- exclude_genes_expt(plot_monocyte, ids = down_monocyte_genes, method = "keep")

hallmark_table <- openxlsx::read.xlsx(xlsxFile = "reference/geneset_selection_GSEA_Hallmark_modified.xlsx")
first_category <- hallmark_table[[1]]

hallmark_genes_idx <- fData(plot_monocyte)[["hgnc_symbol"]] %in% first_category
hallmark_genes <- fData(plot_monocyte)[hallmark_genes_idx, ]
wanted <- rownames(hallmark_genes)

plotted <- exprs(plot_monocyte)[wanted, ]
plot_long <- reshape2::melt(plotted)
colnames(plot_long) <- c("gene", "samplename", "exprs")
dim(plot_long)
plot_long <- merge(plot_long, fData(plot_monocyte), by.x = "gene", by.y = "row.names")
dim(plot_long)
plot_long <- merge(plot_long, pData(plot_monocyte), by = "samplename")
dim(plot_long)
library(ggplot2)

plot_2068_idx <- plot_long[["tubelabelorigin"]] == "su2068"
plot_2068 <- plot_long[plot_2068_idx, ]
plt <- ggplot(data = plot_2068, mapping = aes_string(x = "visitnumber", y = "exprs",
                                                 colour = "gene")) +
  geom_line() +
  theme(legend.position = "none")
plt
probably_not <- ggplotly_url(plt, "probably_terrible_su2068.html")

plot_2073_idx <- plot_long[["tubelabelorigin"]] == "su2073"
plot_2073 <- plot_long[plot_2073_idx, ]
plt <- ggplot(data = plot_2073, mapping = aes_string(x = "visitnumber", y = "exprs",
                                                 colour = "gene")) +
  geom_line() +
  theme(legend.position = "none")
plt
probably_not <- ggplotly_url(plt, "images/probably_terrible_su2073.html")
```

```{r saveme}
if (!isTRUE(get0("skip_load"))) {
  pander::pander(sessionInfo())
  message(paste0("This is hpgltools commit: ", get_git_commit()))
  message(paste0("Saving to ", savefile))
  tmp <- sm(saveme(filename = savefile))
}
```

```{r loadme_after, eval = FALSE}
tmp <- loadme(filename = savefile)
```
