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")]

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

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

3.1.1 Salmon expressionsets

Currently, I have these disabled.

hs_expt <- sm(create_expt("sample_sheets/tmrc3_samples_20201105.xlsx",
                          file_column="hg3891salmonfile",
                          gene_info=hs_annot, tx_gene_map=tx_gene_map))

libsizes <- plot_libsize(hs_expt)
libsizes$plot
nonzero <- plot_nonzero(hs_expt)
box <- plot_boxplot(hs_expt)
hs_write <- write_expt(hs_expt, excel=glue("excel/hs_written_salmon-v{ver}.xlsx"))

hs_valid <- subset_expt(hs_expt, coverage=100000)
valid_write <- write_expt(hs_valid, excel=glue("excel/hs_valid_salmon-v{ver}.xlsx"))

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("sample_sheets/tmrc3_samples_20201105.xlsx",
                       file_column="hg3891hisatfile", savefile="hs_expt_all.rda",
                       gene_info=hs_annot)
## Reading the sample metadata.
## Dropped 164 rows from the sample metadata because they were blank.
## The sample definitions comprises: 70 rows(samples) and 79 columns(metadata fields).
## Reading count tables.
## Reading count files with read.table().
## /mnt/sshfs/cbcbsub01/fs/cbcb-lab/nelsayed/scratch/atb/rnaseq/lpanamensis_tmrc_2019/preprocessing/tmrc30001/outputs/hisat2_hg38_91/forward.count.xz contains 58307 rows.
## /mnt/sshfs/cbcbsub01/fs/cbcb-lab/nelsayed/scratch/atb/rnaseq/lpanamensis_tmrc_2019/preprocessing/tmrc30002/outputs/hisat2_hg38_91/forward.count.xz contains 58307 rows and merges to 58307 rows.
## /mnt/sshfs/cbcbsub01/fs/cbcb-lab/nelsayed/scratch/atb/rnaseq/lpanamensis_tmrc_2019/preprocessing/tmrc30003/outputs/hisat2_hg38_91/forward.count.xz contains 58307 rows and merges to 58307 rows.
## /mnt/sshfs/cbcbsub01/fs/cbcb-lab/nelsayed/scratch/atb/rnaseq/lpanamensis_tmrc_2019/preprocessing/tmrc30004/outputs/hisat2_hg38_91/forward.count.xz contains 58307 rows and merges to 58307 rows.
## /mnt/sshfs/cbcbsub01/fs/cbcb-lab/nelsayed/scratch/atb/rnaseq/lpanamensis_tmrc_2019/preprocessing/tmrc30005/outputs/hisat2_hg38_91/forward.count.xz contains 58307 rows and merges to 58307 rows.
## /mnt/sshfs/cbcbsub01/fs/cbcb-lab/nelsayed/scratch/atb/rnaseq/lpanamensis_tmrc_2019/preprocessing/tmrc30006/outputs/hisat2_hg38_91/forward.count.xz contains 58307 rows and merges to 58307 rows.
## /mnt/sshfs/cbcbsub01/fs/cbcb-lab/nelsayed/scratch/atb/rnaseq/lpanamensis_tmrc_2019/preprocessing/tmrc30007/outputs/hisat2_hg38_91/forward.count.xz contains 58307 rows and merges to 58307 rows.
## /mnt/sshfs/cbcbsub01/fs/cbcb-lab/nelsayed/scratch/atb/rnaseq/lpanamensis_tmrc_2019/preprocessing/tmrc30009/outputs/hisat2_hg38_91/forward.count.xz contains 58307 rows and merges to 58307 rows.
## /mnt/sshfs/cbcbsub01/fs/cbcb-lab/nelsayed/scratch/atb/rnaseq/lpanamensis_tmrc_2019/preprocessing/tmrc30010/outputs/hisat2_hg38_91/forward.count.xz contains 58307 rows and merges to 58307 rows.
## /mnt/sshfs/cbcbsub01/fs/cbcb-lab/nelsayed/scratch/atb/rnaseq/lpanamensis_tmrc_2019/preprocessing/tmrc30015/outputs/hisat2_hg38_91/concatenated.count.xz contains 58307 rows and merges to 58307 rows.
## /mnt/sshfs/cbcbsub01/fs/cbcb-lab/nelsayed/scratch/atb/rnaseq/lpanamensis_tmrc_2019/preprocessing/tmrc30011/outputs/hisat2_hg38_91/forward.count.xz contains 58307 rows and merges to 58307 rows.
## /mnt/sshfs/cbcbsub01/fs/cbcb-lab/nelsayed/scratch/atb/rnaseq/lpanamensis_tmrc_2019/preprocessing/tmrc30012/outputs/hisat2_hg38_91/forward.count.xz contains 58307 rows and merges to 58307 rows.
## /mnt/sshfs/cbcbsub01/fs/cbcb-lab/nelsayed/scratch/atb/rnaseq/lpanamensis_tmrc_2019/preprocessing/tmrc30013/outputs/hisat2_hg38_91/forward.count.xz contains 58307 rows and merges to 58307 rows.
## /mnt/sshfs/cbcbsub01/fs/cbcb-lab/nelsayed/scratch/atb/rnaseq/lpanamensis_tmrc_2019/preprocessing/tmrc30016/outputs/hisat2_hg38_91/concatenated.count.xz contains 58307 rows and merges to 58307 rows.
## /mnt/sshfs/cbcbsub01/fs/cbcb-lab/nelsayed/scratch/atb/rnaseq/lpanamensis_tmrc_2019/preprocessing/tmrc30017/outputs/hisat2_hg38_91/concatenated.count.xz contains 58307 rows and merges to 58307 rows.
## /mnt/sshfs/cbcbsub01/fs/cbcb-lab/nelsayed/scratch/atb/rnaseq/lpanamensis_tmrc_2019/preprocessing/tmrc30050/outputs/hisat2_hg38_91/r1_trimmed.count_hg38_91_sno_gene_gene_id.count.xz contains 58307 rows and merges to 58307 rows.
## /mnt/sshfs/cbcbsub01/fs/cbcb-lab/nelsayed/scratch/atb/rnaseq/lpanamensis_tmrc_2019/preprocessing/tmrc30052/outputs/hisat2_hg38_91/r1_trimmed.count_hg38_91_sno_gene_gene_id.count.xz contains 58307 rows and merges to 58307 rows.
## /mnt/sshfs/cbcbsub01/fs/cbcb-lab/nelsayed/scratch/atb/rnaseq/lpanamensis_tmrc_2019/preprocessing/tmrc30071/outputs/hisat2_hg38_91/r1_trimmed.count_hg38_91_sno_gene_gene_id.count.xz contains 58307 rows and merges to 58307 rows.
## /mnt/sshfs/cbcbsub01/fs/cbcb-lab/nelsayed/scratch/atb/rnaseq/lpanamensis_tmrc_2019/preprocessing/tmrc30056/outputs/hisat2_hg38_91/r1_trimmed.count_hg38_91_sno_gene_gene_id.count.xz contains 58307 rows and merges to 58307 rows.
## /mnt/sshfs/cbcbsub01/fs/cbcb-lab/nelsayed/scratch/atb/rnaseq/lpanamensis_tmrc_2019/preprocessing/tmrc30058/outputs/hisat2_hg38_91/r1_trimmed.count_hg38_91_sno_gene_gene_id.count.xz contains 58307 rows and merges to 58307 rows.
## /mnt/sshfs/cbcbsub01/fs/cbcb-lab/nelsayed/scratch/atb/rnaseq/lpanamensis_tmrc_2019/preprocessing/tmrc30018/outputs/hisat2_hg38_91/concatenated.count.xz contains 58307 rows and merges to 58307 rows.
## /mnt/sshfs/cbcbsub01/fs/cbcb-lab/nelsayed/scratch/atb/rnaseq/lpanamensis_tmrc_2019/preprocessing/tmrc30019/outputs/hisat2_hg38_91/concatenated.count.xz contains 58307 rows and merges to 58307 rows.
## /mnt/sshfs/cbcbsub01/fs/cbcb-lab/nelsayed/scratch/atb/rnaseq/lpanamensis_tmrc_2019/preprocessing/tmrc30014/outputs/hisat2_hg38_91/concatenated.count.xz contains 58307 rows and merges to 58307 rows.
## /mnt/sshfs/cbcbsub01/fs/cbcb-lab/nelsayed/scratch/atb/rnaseq/lpanamensis_tmrc_2019/preprocessing/tmrc30021/outputs/hisat2_hg38_91/concatenated.count.xz contains 58307 rows and merges to 58307 rows.
## /mnt/sshfs/cbcbsub01/fs/cbcb-lab/nelsayed/scratch/atb/rnaseq/lpanamensis_tmrc_2019/preprocessing/tmrc30029/outputs/hisat2_hg38_91/concatenated.count.xz contains 58307 rows and merges to 58307 rows.
## /mnt/sshfs/cbcbsub01/fs/cbcb-lab/nelsayed/scratch/atb/rnaseq/lpanamensis_tmrc_2019/preprocessing/tmrc30020/outputs/hisat2_hg38_91/concatenated.count.xz contains 58307 rows and merges to 58307 rows.
## /mnt/sshfs/cbcbsub01/fs/cbcb-lab/nelsayed/scratch/atb/rnaseq/lpanamensis_tmrc_2019/preprocessing/tmrc30038/outputs/hisat2_hg38_91/concatenated.count.xz contains 58307 rows and merges to 58307 rows.
## /mnt/sshfs/cbcbsub01/fs/cbcb-lab/nelsayed/scratch/atb/rnaseq/lpanamensis_tmrc_2019/preprocessing/tmrc30039/outputs/hisat2_hg38_91/concatenated.count.xz contains 58307 rows and merges to 58307 rows.
## /mnt/sshfs/cbcbsub01/fs/cbcb-lab/nelsayed/scratch/atb/rnaseq/lpanamensis_tmrc_2019/preprocessing/tmrc30023/outputs/hisat2_hg38_91/concatenated.count.xz contains 58307 rows and merges to 58307 rows.
## /mnt/sshfs/cbcbsub01/fs/cbcb-lab/nelsayed/scratch/atb/rnaseq/lpanamensis_tmrc_2019/preprocessing/tmrc30025/outputs/hisat2_hg38_91/concatenated.count.xz contains 58307 rows and merges to 58307 rows.
## /mnt/sshfs/cbcbsub01/fs/cbcb-lab/nelsayed/scratch/atb/rnaseq/lpanamensis_tmrc_2019/preprocessing/tmrc30022/outputs/hisat2_hg38_91/concatenated.count.xz contains 58307 rows and merges to 58307 rows.
## /mnt/sshfs/cbcbsub01/fs/cbcb-lab/nelsayed/scratch/atb/rnaseq/lpanamensis_tmrc_2019/preprocessing/tmrc30046/outputs/hisat2_hg38_91/r1_trimmed.count_hg38_91_sno_gene_gene_id.count.xz contains 58307 rows and merges to 58307 rows.
## /mnt/sshfs/cbcbsub01/fs/cbcb-lab/nelsayed/scratch/atb/rnaseq/lpanamensis_tmrc_2019/preprocessing/tmrc30047/outputs/hisat2_hg38_91/r1_trimmed.count_hg38_91_sno_gene_gene_id.count.xz contains 58307 rows and merges to 58307 rows.
## /mnt/sshfs/cbcbsub01/fs/cbcb-lab/nelsayed/scratch/atb/rnaseq/lpanamensis_tmrc_2019/preprocessing/tmrc30048/outputs/hisat2_hg38_91/r1_trimmed.count_hg38_91_sno_gene_gene_id.count.xz contains 58307 rows and merges to 58307 rows.
## /mnt/sshfs/cbcbsub01/fs/cbcb-lab/nelsayed/scratch/atb/rnaseq/lpanamensis_tmrc_2019/preprocessing/tmrc30026/outputs/hisat2_hg38_91/concatenated.count.xz contains 58307 rows and merges to 58307 rows.
## /mnt/sshfs/cbcbsub01/fs/cbcb-lab/nelsayed/scratch/atb/rnaseq/lpanamensis_tmrc_2019/preprocessing/tmrc30030/outputs/hisat2_hg38_91/concatenated.count.xz contains 58307 rows and merges to 58307 rows.
## /mnt/sshfs/cbcbsub01/fs/cbcb-lab/nelsayed/scratch/atb/rnaseq/lpanamensis_tmrc_2019/preprocessing/tmrc30031/outputs/hisat2_hg38_91/concatenated.count.xz contains 58307 rows and merges to 58307 rows.
## /mnt/sshfs/cbcbsub01/fs/cbcb-lab/nelsayed/scratch/atb/rnaseq/lpanamensis_tmrc_2019/preprocessing/tmrc30032/outputs/hisat2_hg38_91/concatenated.count.xz contains 58307 rows and merges to 58307 rows.
## /mnt/sshfs/cbcbsub01/fs/cbcb-lab/nelsayed/scratch/atb/rnaseq/lpanamensis_tmrc_2019/preprocessing/tmrc30024/outputs/hisat2_hg38_91/concatenated.count.xz contains 58307 rows and merges to 58307 rows.
## /mnt/sshfs/cbcbsub01/fs/cbcb-lab/nelsayed/scratch/atb/rnaseq/lpanamensis_tmrc_2019/preprocessing/tmrc30040/outputs/hisat2_hg38_91/concatenated.count.xz contains 58307 rows and merges to 58307 rows.
## /mnt/sshfs/cbcbsub01/fs/cbcb-lab/nelsayed/scratch/atb/rnaseq/lpanamensis_tmrc_2019/preprocessing/tmrc30033/outputs/hisat2_hg38_91/concatenated.count.xz contains 58307 rows and merges to 58307 rows.
## /mnt/sshfs/cbcbsub01/fs/cbcb-lab/nelsayed/scratch/atb/rnaseq/lpanamensis_tmrc_2019/preprocessing/tmrc30049/outputs/hisat2_hg38_91/r1_trimmed.count_hg38_91_sno_gene_gene_id.count.xz contains 58307 rows and merges to 58307 rows.
## /mnt/sshfs/cbcbsub01/fs/cbcb-lab/nelsayed/scratch/atb/rnaseq/lpanamensis_tmrc_2019/preprocessing/tmrc30053/outputs/hisat2_hg38_91/r1_trimmed.count_hg38_91_sno_gene_gene_id.count.xz contains 58307 rows and merges to 58307 rows.
## /mnt/sshfs/cbcbsub01/fs/cbcb-lab/nelsayed/scratch/atb/rnaseq/lpanamensis_tmrc_2019/preprocessing/tmrc30054/outputs/hisat2_hg38_91/r1_trimmed.count_hg38_91_sno_gene_gene_id.count.xz contains 58307 rows and merges to 58307 rows.
## /mnt/sshfs/cbcbsub01/fs/cbcb-lab/nelsayed/scratch/atb/rnaseq/lpanamensis_tmrc_2019/preprocessing/tmrc30037/outputs/hisat2_hg38_91/concatenated.count.xz contains 58307 rows and merges to 58307 rows.
## /mnt/sshfs/cbcbsub01/fs/cbcb-lab/nelsayed/scratch/atb/rnaseq/lpanamensis_tmrc_2019/preprocessing/tmrc30027/outputs/hisat2_hg38_91/concatenated.count.xz contains 58307 rows and merges to 58307 rows.
## /mnt/sshfs/cbcbsub01/fs/cbcb-lab/nelsayed/scratch/atb/rnaseq/lpanamensis_tmrc_2019/preprocessing/tmrc30028/outputs/hisat2_hg38_91/concatenated.count.xz contains 58307 rows and merges to 58307 rows.
## /mnt/sshfs/cbcbsub01/fs/cbcb-lab/nelsayed/scratch/atb/rnaseq/lpanamensis_tmrc_2019/preprocessing/tmrc30034/outputs/hisat2_hg38_91/concatenated.count.xz contains 58307 rows and merges to 58307 rows.
## /mnt/sshfs/cbcbsub01/fs/cbcb-lab/nelsayed/scratch/atb/rnaseq/lpanamensis_tmrc_2019/preprocessing/tmrc30035/outputs/hisat2_hg38_91/concatenated.count.xz contains 58307 rows and merges to 58307 rows.
## /mnt/sshfs/cbcbsub01/fs/cbcb-lab/nelsayed/scratch/atb/rnaseq/lpanamensis_tmrc_2019/preprocessing/tmrc30036/outputs/hisat2_hg38_91/concatenated.count.xz contains 58307 rows and merges to 58307 rows.
## /mnt/sshfs/cbcbsub01/fs/cbcb-lab/nelsayed/scratch/atb/rnaseq/lpanamensis_tmrc_2019/preprocessing/tmrc30044/outputs/hisat2_hg38_91/concatenated.count.xz contains 58307 rows and merges to 58307 rows.
## /mnt/sshfs/cbcbsub01/fs/cbcb-lab/nelsayed/scratch/atb/rnaseq/lpanamensis_tmrc_2019/preprocessing/tmrc30055/outputs/hisat2_hg38_91/r1_trimmed.count_hg38_91_sno_gene_gene_id.count.xz contains 58307 rows and merges to 58307 rows.
## /mnt/sshfs/cbcbsub01/fs/cbcb-lab/nelsayed/scratch/atb/rnaseq/lpanamensis_tmrc_2019/preprocessing/tmrc30068/outputs/hisat2_hg38_91/r1_trimmed.count_hg38_91_sno_gene_gene_id.count.xz contains 58307 rows and merges to 58307 rows.
## /mnt/sshfs/cbcbsub01/fs/cbcb-lab/nelsayed/scratch/atb/rnaseq/lpanamensis_tmrc_2019/preprocessing/tmrc30070/outputs/hisat2_hg38_91/r1_trimmed.count_hg38_91_sno_gene_gene_id.count.xz contains 58307 rows and merges to 58307 rows.
## /mnt/sshfs/cbcbsub01/fs/cbcb-lab/nelsayed/scratch/atb/rnaseq/lpanamensis_tmrc_2019/preprocessing/tmrc30041/outputs/hisat2_hg38_91/concatenated.count.xz contains 58307 rows and merges to 58307 rows.
## /mnt/sshfs/cbcbsub01/fs/cbcb-lab/nelsayed/scratch/atb/rnaseq/lpanamensis_tmrc_2019/preprocessing/tmrc30042/outputs/hisat2_hg38_91/concatenated.count.xz contains 58307 rows and merges to 58307 rows.
## /mnt/sshfs/cbcbsub01/fs/cbcb-lab/nelsayed/scratch/atb/rnaseq/lpanamensis_tmrc_2019/preprocessing/tmrc30043/outputs/hisat2_hg38_91/concatenated.count.xz contains 58307 rows and merges to 58307 rows.
## /mnt/sshfs/cbcbsub01/fs/cbcb-lab/nelsayed/scratch/atb/rnaseq/lpanamensis_tmrc_2019/preprocessing/tmrc30045/outputs/hisat2_hg38_91/concatenated.count.xz contains 58307 rows and merges to 58307 rows.
## /mnt/sshfs/cbcbsub01/fs/cbcb-lab/nelsayed/scratch/atb/rnaseq/lpanamensis_tmrc_2019/preprocessing/tmrc30059/outputs/hisat2_hg38_91/r1_trimmed.count_hg38_91_sno_gene_gene_id.count.xz contains 58307 rows and merges to 58307 rows.
## /mnt/sshfs/cbcbsub01/fs/cbcb-lab/nelsayed/scratch/atb/rnaseq/lpanamensis_tmrc_2019/preprocessing/tmrc30060/outputs/hisat2_hg38_91/r1_trimmed.count_hg38_91_sno_gene_gene_id.count.xz contains 58307 rows and merges to 58307 rows.
## /mnt/sshfs/cbcbsub01/fs/cbcb-lab/nelsayed/scratch/atb/rnaseq/lpanamensis_tmrc_2019/preprocessing/tmrc30061/outputs/hisat2_hg38_91/r1_trimmed.count_hg38_91_sno_gene_gene_id.count.xz contains 58307 rows and merges to 58307 rows.
## /mnt/sshfs/cbcbsub01/fs/cbcb-lab/nelsayed/scratch/atb/rnaseq/lpanamensis_tmrc_2019/preprocessing/tmrc30062/outputs/hisat2_hg38_91/r1_trimmed.count_hg38_91_sno_gene_gene_id.count.xz contains 58307 rows and merges to 58307 rows.
## /mnt/sshfs/cbcbsub01/fs/cbcb-lab/nelsayed/scratch/atb/rnaseq/lpanamensis_tmrc_2019/preprocessing/tmrc30063/outputs/hisat2_hg38_91/r1_trimmed.count_hg38_91_sno_gene_gene_id.count.xz contains 58307 rows and merges to 58307 rows.
## /mnt/sshfs/cbcbsub01/fs/cbcb-lab/nelsayed/scratch/atb/rnaseq/lpanamensis_tmrc_2019/preprocessing/tmrc30051/outputs/hisat2_hg38_91/r1_trimmed.count_hg38_91_sno_gene_gene_id.count.xz contains 58307 rows and merges to 58307 rows.
## /mnt/sshfs/cbcbsub01/fs/cbcb-lab/nelsayed/scratch/atb/rnaseq/lpanamensis_tmrc_2019/preprocessing/tmrc30064/outputs/hisat2_hg38_91/r1_trimmed.count_hg38_91_sno_gene_gene_id.count.xz contains 58307 rows and merges to 58307 rows.
## /mnt/sshfs/cbcbsub01/fs/cbcb-lab/nelsayed/scratch/atb/rnaseq/lpanamensis_tmrc_2019/preprocessing/tmrc30065/outputs/hisat2_hg38_91/r1_trimmed.count_hg38_91_sno_gene_gene_id.count.xz contains 58307 rows and merges to 58307 rows.
## /mnt/sshfs/cbcbsub01/fs/cbcb-lab/nelsayed/scratch/atb/rnaseq/lpanamensis_tmrc_2019/preprocessing/tmrc30066/outputs/hisat2_hg38_91/r1_trimmed.count_hg38_91_sno_gene_gene_id.count.xz contains 58307 rows and merges to 58307 rows.
## /mnt/sshfs/cbcbsub01/fs/cbcb-lab/nelsayed/scratch/atb/rnaseq/lpanamensis_tmrc_2019/preprocessing/tmrc30067/outputs/hisat2_hg38_91/r1_trimmed.count_hg38_91_sno_gene_gene_id.count.xz contains 58307 rows and merges to 58307 rows.
## /mnt/sshfs/cbcbsub01/fs/cbcb-lab/nelsayed/scratch/atb/rnaseq/lpanamensis_tmrc_2019/preprocessing/tmrc30057/outputs/hisat2_hg38_91/r1_trimmed.count_hg38_91_sno_gene_gene_id.count.xz contains 58307 rows and merges to 58307 rows.
## /mnt/sshfs/cbcbsub01/fs/cbcb-lab/nelsayed/scratch/atb/rnaseq/lpanamensis_tmrc_2019/preprocessing/tmrc30069/outputs/hisat2_hg38_91/r1_trimmed.count_hg38_91_sno_gene_gene_id.count.xz contains 58307 rows and merges to 58307 rows.
## Finished reading count data.
## Matched 57768 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 58302 rows and 70 columns.
hs_expt <- exclude_genes_expt(hs_expt, column="gene_biotype",
                              method="keep", pattern="protein_coding")
## Before removal, there were 58302 entries.
## Now there are 19754 entries.
## Percent kept: 95.178, 94.234, 94.315, 94.748, 94.024, 93.570, 96.754, 97.306, 96.776, 84.755, 97.443, 97.423, 97.265, 93.491, 88.446, 97.881, 97.278, 97.417, 97.693, 97.133, 96.366, 90.828, 97.594, 97.264, 96.787, 90.052, 97.111, 96.690, 96.935, 93.115, 91.153, 98.074, 97.532, 97.672, 94.754, 97.628, 97.255, 96.931, 97.374, 96.524, 96.219, 98.214, 97.534, 97.489, 97.492, 96.859, 96.661, 97.571, 97.178, 96.855, 86.749, 98.027, 97.371, 97.531, 97.507, 96.959, 97.297, 80.597, 97.823, 97.741, 97.930, 98.008, 97.976, 98.054, 97.572, 97.353, 97.547, 97.568, 97.741, 98.048
## Percent removed: 4.822, 5.766, 5.685, 5.252, 5.976, 6.430, 3.246, 2.694, 3.224, 15.245, 2.557, 2.577, 2.735, 6.509, 11.554, 2.119, 2.722, 2.583, 2.307, 2.867, 3.634, 9.172, 2.406, 2.736, 3.213, 9.948, 2.889, 3.310, 3.065, 6.885, 8.847, 1.926, 2.468, 2.328, 5.246, 2.372, 2.745, 3.069, 2.626, 3.476, 3.781, 1.786, 2.466, 2.511, 2.508, 3.141, 3.339, 2.429, 2.822, 3.145, 13.251, 1.973, 2.629, 2.469, 2.493, 3.041, 2.703, 19.403, 2.177, 2.259, 2.070, 1.992, 2.024, 1.946, 2.428, 2.647, 2.453, 2.432, 2.259, 1.952
libsizes <- 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

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 431739 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 70, now there are 64 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")))
## Error in checkModelStatus(fit, showWarnings = showWarnings, colinearityCutoff = colinearityCutoff) : 
##   The variables specified in this model are redundant,
## so the design matrix is not full rank
## Error in checkModelStatus(fit, showWarnings = showWarnings, colinearityCutoff = colinearityCutoff) : 
##   The variables specified in this model are redundant,
## so the design matrix is not full rank

4 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.

    1. 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.

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 <- sm(normalize_expt(hs_valid, transform="log2", convert="cpm", batch="svaseq",
                           filter=TRUE))
all_pca <- plot_pca(all_norm)
pp(file="tmrc3_pca_nolabels.png", image=all_pca$plot)
## Writing the image to: tmrc3_pca_nolabels.png 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 in MASS::cov.trob(data[, vars]): Probable convergence failure

## Warning in MASS::cov.trob(data[, vars]): Probable convergence failure

all_ts <- plot_tsne(all_norm)
all_ts$plot

knitr::kable(all_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
1010-2 1010-2 PBMC d1010 1 #1B9E77 1010-2 -0.1227 -0.0961 -0.1227 -0.0961 -0.0944 -0.1031 0.0847 -0.0002 0.0763 -0.0213 0.2173 -0.2426 0.0363 -0.1221 0.0356 -0.1485 -0.0733 0.0720 0.0590 0.1402 -0.1036 0.0691 -0.1720 0.0117 -0.1878 0.1237 -0.0428 0.1449 -0.3364 0.1007 0.0167 -0.2583 -0.1822 -0.1595 0.0492 0.1615 -0.2408 -0.0285 -0.1125 -0.0729 -0.1060 0.1320 0.0705 0.0060 0.1801 0.1084 0.0472 0.1790 -0.1822 -0.2747 -0.0580 -0.0033 -0.1217 -0.1278 -0.0117 -0.0081 -0.0164 -0.0018 -0.0659 -0.0255 -0.0166 -0.0716 -0.0890 -0.0258 -0.0081
1010-7 1010-7 PBMC d1010 1 #1B9E77 1010-7 -0.0844 -0.0354 -0.0844 -0.0354 -0.0161 -0.0950 -0.1357 0.0254 0.0284 0.0288 0.0864 -0.1428 0.1067 -0.0234 0.0486 -0.1494 0.0597 0.0447 0.0170 -0.0795 0.0481 -0.1070 -0.0383 -0.0597 -0.1322 -0.1488 -0.0409 -0.1457 0.0245 -0.0864 0.0544 -0.0884 0.2415 -0.0720 0.0610 -0.0930 0.0608 -0.1028 -0.0297 0.1508 -0.0605 0.0905 0.1667 0.0987 0.2584 0.1596 0.0483 -0.0635 0.2445 0.0007 0.1850 -0.3112 0.0985 0.1300 -0.2243 0.0819 0.1360 0.1694 0.2409 0.0876 -0.0001 0.3202 -0.1302 -0.0253 0.0055
1010-12 1010-12 PBMC d1010 1 #1B9E77 1010-12 -0.1947 -0.1273 -0.1947 -0.1273 -0.0244 -0.0740 -0.0715 0.0683 0.2154 0.0682 -0.0440 -0.5463 -0.0654 -0.2013 -0.0708 0.0580 -0.0131 -0.0703 -0.2248 0.0612 -0.0508 -0.4204 -0.1185 0.1216 -0.0438 -0.2091 0.0470 0.0959 0.0921 -0.0150 0.0399 0.1654 0.0233 -0.0173 -0.1769 -0.0159 0.0626 -0.0450 0.0574 -0.1107 0.0512 -0.0379 0.0102 0.0021 -0.1729 -0.0548 -0.0838 -0.0521 -0.0351 0.1242 -0.0060 0.0630 0.0107 0.0302 0.0679 0.0568 -0.0644 -0.0415 -0.0850 -0.0085 0.0040 -0.0417 0.0444 0.0213 -0.0081
1011-2 1011-2 PBMC d1011 2 #1B9E77 1011-2 0.0400 -0.0372 0.0400 -0.0372 -0.0792 -0.1370 -0.0689 0.0070 -0.0450 0.0093 0.0966 -0.0902 0.0894 -0.0669 -0.0135 0.0311 0.0985 0.1177 0.0909 -0.0783 -0.0098 0.0944 -0.0063 -0.0069 -0.0446 0.1312 -0.0786 -0.1371 -0.0328 0.1093 0.1573 -0.1109 0.0204 0.0040 0.1073 0.1889 -0.2023 -0.0626 -0.1560 0.0714 -0.2918 -0.1310 -0.0078 -0.2531 -0.2037 -0.1653 -0.0276 -0.2118 0.2245 0.3152 -0.0273 0.3086 0.0291 0.1151 0.0469 0.0480 -0.1911 0.2504 0.0809 -0.0184 0.0385 0.0027 0.0482 -0.0018 -0.0070
1011-7 1011-7 PBMC d1011 2 #1B9E77 1011-7 0.0520 -0.0416 0.0520 -0.0416 -0.1010 -0.1183 -0.0360 0.0404 -0.0504 -0.0092 0.0891 -0.1501 0.0815 -0.0863 0.0072 0.0518 0.1939 -0.0296 0.0716 -0.1812 0.0390 0.0467 0.0143 0.0207 0.0851 0.0758 -0.0420 -0.2433 -0.0558 -0.0575 0.1662 -0.0314 0.1984 0.0099 0.1223 0.0774 -0.0430 0.0467 0.0775 -0.0384 0.0101 -0.1388 -0.0414 -0.0353 -0.1140 -0.0593 0.0387 -0.0326 0.1241 -0.1353 0.0780 -0.1278 0.0215 -0.2156 0.2364 -0.2220 0.3411 -0.5073 -0.0658 -0.0489 -0.0886 -0.0329 0.0202 0.0434 -0.0049
1011-12 1011-12 PBMC d1011 2 #1B9E77 1011-12 0.0802 -0.0354 0.0802 -0.0354 -0.0711 -0.0836 -0.0512 0.0292 0.0087 -0.0373 0.0806 -0.1021 0.0669 -0.0579 0.0492 0.0935 0.1735 -0.1268 0.1066 -0.3072 0.0367 0.0556 0.0816 0.1069 0.1644 0.0071 0.0708 -0.1785 0.0087 -0.2323 0.0169 0.0221 0.2106 -0.0386 0.0699 -0.1584 -0.0236 0.2038 0.0408 -0.1116 0.1952 0.0071 -0.0215 -0.2015 0.0004 0.1625 -0.0132 0.2047 -0.2957 -0.2230 -0.0436 0.1587 0.0415 0.0187 -0.1241 0.1114 -0.2472 0.2425 -0.0938 -0.0100 0.1457 0.0184 -0.0257 -0.0702 0.0281
1034n1 1034n1 Neutrophil d1034 3 #D95F02 1034n1 0.1140 0.0257 0.1140 0.0257 -0.0436 -0.1767 -0.0402 0.0322 0.0381 0.0902 -0.0631 0.0476 0.0121 0.0244 0.0726 0.0318 -0.1957 0.1821 0.2462 0.2493 -0.0763 -0.1872 -0.0706 0.1687 0.0233 0.1140 -0.1454 -0.0175 0.2431 -0.0281 0.0092 -0.0194 0.0455 0.0796 0.0518 -0.2244 -0.0760 0.2656 0.0887 0.1483 -0.0909 -0.0948 0.1735 0.0000 -0.0907 0.1132 0.1771 0.0882 -0.0755 -0.0511 -0.1619 -0.1372 -0.2091 0.2451 0.3106 -0.0584 -0.0220 0.0337 0.0984 0.0053 -0.0529 -0.0271 -0.0176 0.0246 -0.0182
1034bp1 1034bp1 Biopsy d1034 3 #E7298A 1034bp1 0.0124 0.0311 0.0124 0.0311 -0.0888 -0.1064 0.0308 -0.0135 -0.0515 -0.0012 0.0559 -0.0246 -0.0051 0.0510 0.0341 0.0544 0.1050 0.0082 -0.0339 -0.0350 0.0801 -0.1243 -0.0352 -0.1299 0.0027 -0.0046 -0.0107 -0.0960 -0.0468 0.0277 -0.1867 0.0207 0.0101 -0.0445 -0.0618 -0.0475 0.0218 0.0696 -0.0232 0.0069 -0.0088 0.1511 -0.1885 0.0544 0.0669 -0.0364 -0.0578 0.0372 -0.0223 0.2184 -0.1121 -0.2832 -0.0850 -0.3170 0.0848 -0.3270 0.0263 0.3282 0.2056 0.0605 0.2040 -0.3291 0.1962 0.0481 0.2344
1034n2 1034n2 Neutrophil d1034 3 #D95F02 1034n2 0.0954 0.0550 0.0954 0.0550 -0.0485 -0.1404 -0.0340 0.0115 0.0018 0.0246 -0.0427 0.0393 -0.0043 0.0595 0.0276 0.0568 -0.1665 0.0865 0.1321 0.1537 -0.1779 -0.1516 -0.0551 0.1590 0.1191 0.1749 -0.1236 -0.0340 0.1712 0.0835 -0.0602 0.1417 0.0435 0.0579 0.1085 0.0117 -0.1196 -0.0406 0.0046 0.0244 0.0478 0.1914 -0.0191 -0.0201 -0.1512 -0.0885 -0.1117 0.1571 0.1347 0.0239 0.2147 0.0256 0.0174 -0.3801 -0.5249 0.0638 0.0155 -0.0419 -0.0974 -0.0625 -0.0876 0.0051 0.0197 -0.0466 -0.0131
1034m2 1034m2 Monocyte d1034 3 #7570B3 1034m2 -0.0872 -0.0316 -0.0872 -0.0316 0.0218 -0.0980 -0.2756 0.0303 0.0523 0.1177 -0.0369 0.0180 0.0721 0.1613 -0.0757 -0.0540 -0.0743 0.2031 0.0502 0.0888 0.1501 0.2675 -0.0751 0.0281 -0.2086 -0.0661 0.2069 -0.0361 -0.0924 -0.2255 0.0612 0.2673 -0.1171 -0.0507 0.0384 0.0744 0.0403 0.1141 0.0446 -0.0816 0.0528 0.0663 -0.0478 0.0138 -0.0188 -0.0979 -0.0413 0.1508 -0.0516 0.1030 0.1139 -0.0279 0.0717 0.0188 -0.0057 -0.0715 -0.3198 -0.2691 0.3473 -0.1368 0.0091 -0.0357 -0.0523 0.0032 -0.0071
1034m2- 1034m2- Monocyte d1034 3 #7570B3 1034m2- -0.0270 -0.0314 -0.0270 -0.0314 0.0203 -0.1203 -0.2588 0.0021 -0.0164 0.0429 -0.0044 0.0664 0.1146 0.1631 -0.0644 -0.1168 -0.1038 0.1593 0.0320 0.0236 0.1491 0.2009 -0.1192 0.0721 -0.1605 -0.0543 0.1556 0.0699 -0.0475 -0.1079 0.0598 0.2145 0.0032 0.0201 -0.0067 -0.0221 0.0027 -0.0394 0.1023 -0.0671 0.0409 -0.0835 0.0083 -0.0093 0.0109 0.0558 -0.0295 -0.0763 0.0602 0.0263 -0.0791 0.0457 -0.1315 -0.0893 0.0710 0.1349 0.3940 0.2863 -0.4792 0.1246 0.0322 -0.0184 0.1203 -0.0098 0.0081
2050bp1 2050bp1 Biopsy d2050 4 #E7298A 2050bp1 -0.0663 0.0719 -0.0663 0.0719 0.0013 -0.0314 0.1079 0.0094 -0.0636 0.0230 0.0851 0.0861 0.0034 -0.1052 -0.0288 -0.0156 0.0699 0.0314 -0.1244 0.2036 -0.3323 0.2126 -0.0850 -0.1412 0.2517 -0.2690 0.3043 0.0509 0.2061 -0.1727 0.3348 -0.1185 0.0179 0.0303 -0.0467 -0.1632 -0.2428 -0.1940 -0.0779 0.0486 0.0229 -0.0466 -0.1673 0.0162 -0.0682 -0.1121 -0.0079 0.1629 0.0211 0.0068 -0.1170 -0.1459 -0.0109 0.0058 -0.0114 -0.0551 0.0020 0.0575 -0.0075 -0.0308 -0.0073 0.0379 -0.0252 -0.0251 -0.0023
2052bp1 2052bp1 Biopsy d2052 5 #E7298A 2052bp1 0.0418 0.0481 0.0418 0.0481 -0.0384 -0.0792 -0.0282 -0.0326 -0.0705 -0.0626 0.0452 -0.0295 -0.0009 0.0265 0.0305 0.0818 0.0697 0.0155 -0.0606 -0.1034 -0.1235 0.0234 0.0778 0.0509 0.1199 0.0726 0.0018 0.0590 -0.0934 0.0710 0.0185 0.0915 -0.0465 0.1047 -0.0267 0.0745 -0.0239 -0.1196 0.0263 -0.0407 0.0382 0.0174 -0.1603 0.2616 -0.0269 0.0067 0.1801 0.0961 0.0445 -0.0919 0.3813 -0.0015 -0.1523 0.2589 0.1171 0.4660 0.1153 0.0379 0.1935 -0.0293 -0.0174 -0.3139 0.1001 -0.0414 0.2135
2052e1 2052e1 Eosinophil d2052 5 #66A61E 2052e1 0.1404 -0.0931 0.1404 -0.0931 -0.2372 0.2598 -0.0378 0.8734 -0.0506 -0.2101 0.0483 0.0726 -0.0283 0.0336 -0.0445 -0.0004 -0.0223 0.0134 -0.0465 0.0455 -0.0010 -0.0036 0.0124 0.0127 -0.0351 -0.0083 0.0064 0.0166 -0.0009 0.0228 -0.0101 0.0080 -0.0218 -0.0071 -0.0115 0.0041 0.0083 -0.0176 -0.0048 0.0070 0.0014 0.0059 0.0020 0.0157 0.0122 0.0017 0.0029 -0.0108 0.0004 0.0081 -0.0022 -0.0056 -0.0036 0.0118 -0.0019 0.0031 -0.0034 0.0118 0.0069 0.0038 -0.0011 0.0026 -0.0022 0.0006 -0.0011
2052n2 2052n2 Neutrophil d2052 5 #D95F02 2052n2 0.2816 -0.2333 0.2816 -0.2333 0.4925 0.0892 0.0893 0.0451 -0.2962 0.2470 0.2639 -0.1758 -0.4037 0.0720 -0.2499 -0.1452 -0.1189 0.1493 0.0654 -0.1223 -0.0278 -0.0372 -0.0023 -0.0443 0.0834 0.0159 0.0618 -0.0891 -0.0334 0.0395 -0.0116 0.0042 -0.0071 0.0083 -0.0273 -0.0365 0.0321 0.0089 0.0266 -0.0368 -0.0302 0.0096 0.0129 -0.0041 -0.0061 0.0139 -0.0026 0.0114 0.0049 0.0001 -0.0062 -0.0168 -0.0090 0.0032 -0.0121 0.0013 -0.0054 0.0025 0.0040 -0.0074 0.0012 0.0063 -0.0015 -0.0049 -0.0026
2065bp1 2065bp1 Biopsy d2065 6 #E7298A 2065bp1 0.0365 -0.0294 0.0365 -0.0294 -0.0975 -0.0066 -0.0724 -0.0605 0.0621 -0.0601 0.1795 -0.0760 0.0186 -0.1479 0.0661 0.0486 -0.0804 0.1244 0.0616 -0.2640 -0.0403 0.2314 0.0067 0.1661 0.1605 -0.1506 0.0136 0.2149 0.0422 0.1185 -0.0592 -0.1243 -0.1144 -0.0718 0.0491 0.0279 0.1755 0.1396 0.1351 0.2307 0.2073 -0.0747 0.1204 0.2853 0.0181 -0.0969 -0.0822 -0.0939 0.1188 0.0849 -0.2793 0.0906 -0.1110 0.0484 -0.2231 -0.0872 -0.0492 -0.1088 0.0361 0.1631 -0.1170 -0.0533 0.0057 -0.0253 0.1720
2066bp1 2066bp1 Biopsy d2066 7 #E7298A 2066bp1 -0.0027 0.0751 -0.0027 0.0751 -0.0383 -0.0883 -0.0083 0.0001 -0.0769 0.0635 0.0175 0.0478 0.0029 0.0667 -0.0047 0.0759 0.1164 0.0578 -0.0404 0.0174 0.0698 -0.0579 -0.0351 -0.0545 -0.0672 0.0514 -0.0088 -0.1445 -0.0250 0.0483 -0.1351 0.0775 0.0063 -0.0196 -0.1008 0.1462 -0.0352 -0.0924 -0.0995 0.0580 -0.1452 0.0279 -0.3180 0.0080 -0.1362 0.0805 -0.0440 -0.0374 -0.1694 -0.0781 -0.0713 -0.2254 -0.0643 0.3600 -0.2118 -0.0919 -0.1815 -0.2093 -0.2663 0.4391 -0.1280 0.0836 0.0630 -0.0652 0.0614
2068m1 2068m1 Monocyte d2068 8 #7570B3 2068m1 -0.3049 -0.2106 -0.3049 -0.2106 -0.0016 0.0272 0.3279 0.0168 0.1371 0.0182 0.1893 0.2030 -0.2160 0.0143 0.3617 -0.0483 0.2788 0.2081 0.1964 0.0973 -0.0297 -0.1333 0.1730 0.1092 0.0523 -0.1602 0.0746 0.0283 -0.1273 -0.0997 -0.1094 0.1941 -0.0714 0.1408 0.1098 0.0969 0.0726 0.0322 0.1068 0.0028 -0.0312 -0.0292 -0.0187 -0.1790 0.0456 -0.0313 -0.0131 -0.0079 0.0536 0.0583 0.0294 0.0333 -0.0213 0.0366 -0.0089 0.0097 0.0822 0.0046 -0.0193 -0.0121 -0.0092 0.0404 -0.0368 0.0021 -0.0059
2068n1 2068n1 Neutrophil d2068 8 #D95F02 2068n1 0.0924 0.0804 0.0924 0.0804 0.0271 -0.1217 0.0366 0.0313 0.0225 -0.0145 -0.1282 0.0282 -0.1045 -0.1683 0.0645 -0.0934 0.0689 -0.0275 -0.0284 0.0618 0.1557 -0.0204 0.1564 -0.0717 0.0021 0.0812 0.1054 0.0986 0.1767 -0.0031 0.1802 -0.1159 -0.1008 -0.0970 -0.0590 0.1054 0.1245 0.3527 0.0422 -0.2185 -0.2924 0.1520 0.0240 0.0725 0.0525 -0.1567 0.1791 0.1233 0.1548 0.0091 -0.1141 -0.0319 0.1747 0.0413 -0.1438 0.1658 0.0803 -0.1183 -0.1099 0.1109 0.3669 -0.0345 0.0144 0.0226 0.0063
2068e1 2068e1 Eosinophil d2068 8 #66A61E 2068e1 0.0444 0.0311 0.0444 0.0311 -0.1554 0.0045 0.0992 -0.1011 0.0589 -0.0805 0.1374 -0.0764 -0.1093 -0.0166 -0.0429 0.0039 -0.1701 -0.0278 -0.1338 0.1136 0.3056 0.0226 0.4013 -0.1210 -0.0049 -0.1263 -0.0998 -0.1796 0.0066 -0.0937 0.0712 0.1519 -0.2059 0.1603 0.1019 -0.1695 -0.2107 0.1132 -0.3176 -0.0397 -0.0176 -0.0956 0.1414 0.2563 -0.0515 -0.0249 -0.1824 -0.1332 0.0139 -0.1511 0.0628 -0.0034 -0.0922 -0.0574 -0.0152 -0.0005 -0.0469 0.0395 -0.0382 -0.0021 -0.0418 0.0273 0.0048 0.0002 -0.0134
2068bp1 2068bp1 Biopsy d2068 8 #E7298A 2068bp1 0.0152 0.0461 0.0152 0.0461 -0.0635 -0.1771 0.1316 0.0359 -0.1715 0.1023 -0.1173 0.0544 0.0758 0.0866 -0.1104 -0.0137 0.0721 -0.0345 -0.1845 0.0558 0.0652 -0.0832 -0.0845 -0.2143 0.0544 -0.1595 0.0226 -0.1143 -0.0491 -0.0077 -0.1815 -0.1807 0.0128 -0.0414 0.1318 0.1745 0.0127 0.1238 0.2637 0.2826 -0.0765 -0.0920 0.2041 0.0384 0.0377 -0.2421 -0.2827 0.0836 -0.3158 0.0774 0.0838 0.0488 -0.1049 0.0260 -0.0041 0.2038 0.0881 -0.0279 -0.0506 -0.1560 0.0188 0.0534 -0.0210 -0.0203 -0.0333
2072n1 2072n1 Neutrophil d2072 10 #D95F02 2072n1 0.1019 0.0543 0.1019 0.0543 0.0222 -0.1673 -0.0320 0.0430 0.0566 0.0101 -0.0843 0.0182 0.0119 -0.1161 0.0918 -0.0814 0.0524 0.1421 0.0481 -0.0295 0.1997 -0.0662 0.2448 -0.1537 -0.0080 0.1846 0.1441 0.1335 0.0577 0.0033 0.1147 -0.1932 -0.0525 0.1416 -0.3366 0.0012 0.1291 -0.1180 0.1210 -0.0004 -0.0269 -0.0634 -0.0233 -0.0746 0.0341 0.1647 -0.1695 0.0910 -0.0874 0.0137 0.0041 0.0377 0.1475 -0.1887 0.0683 0.0521 -0.0358 0.0859 0.0950 0.0212 -0.5185 0.0020 0.0120 -0.0237 0.0226
2072e1 2072e1 Eosinophil d2072 10 #66A61E 2072e1 -0.0058 -0.0609 -0.0058 -0.0609 -0.0845 -0.0870 0.0494 -0.0838 -0.0282 -0.1680 0.2249 0.0060 0.0230 0.1176 0.0898 -0.4001 -0.3004 -0.3758 -0.2231 0.0479 0.0789 0.0678 -0.0862 0.2163 0.1933 0.0845 0.0245 -0.0281 0.1187 0.1097 0.0564 0.1388 0.0125 0.1102 -0.0471 0.1564 0.1726 0.0371 -0.0505 0.2037 -0.0596 -0.0528 -0.1266 -0.2296 0.1112 0.0656 -0.0700 0.0979 -0.0025 0.0239 0.0253 -0.0119 0.1055 0.0693 0.0788 -0.0540 0.0091 -0.0190 0.0488 0.0061 0.0220 -0.0251 -0.0162 0.0126 -0.0039
2072bp1 2072bp1 Biopsy d2072 10 #E7298A 2072bp1 -0.2163 -0.1378 -0.2163 -0.1378 -0.0539 -0.1273 0.5860 0.1245 -0.0815 0.2397 -0.2987 -0.0554 0.2664 0.0653 -0.1726 0.0673 -0.2574 0.0418 0.0123 -0.3189 -0.0111 0.1570 0.0935 0.0745 -0.0844 0.0149 -0.0517 0.1036 0.0938 -0.0085 0.0108 0.0625 0.0189 0.0292 -0.0116 -0.0557 0.0259 -0.0298 -0.0715 -0.0300 -0.0394 0.0388 -0.0448 -0.0141 0.0435 0.0266 0.0636 -0.0091 0.0578 -0.0144 0.0056 -0.0319 0.0420 -0.0026 0.0075 -0.0281 -0.0111 0.0059 0.0056 0.0196 -0.0073 -0.0074 0.0132 -0.0037 0.0011
2071bp1 2071bp1 Biopsy d2071 9 #E7298A 2071bp1 0.0458 0.0646 0.0458 0.0646 -0.0023 -0.1553 0.0086 0.0086 -0.1778 0.0211 -0.0957 0.0357 0.0249 0.1225 -0.0603 -0.0258 0.1381 -0.0450 -0.1049 0.0649 0.0032 -0.2468 0.0351 -0.1020 0.0010 0.0125 -0.0770 0.0710 -0.2640 0.0097 -0.0108 0.1095 -0.1123 -0.2252 0.0517 -0.0308 0.1126 0.0481 -0.2114 0.1290 0.3482 -0.1619 -0.0567 -0.0420 0.0159 -0.1196 0.1726 0.2907 0.3869 -0.1078 -0.1954 0.0997 0.0441 0.0033 0.0772 0.0147 -0.0800 0.0585 -0.0335 0.0273 -0.1258 0.0853 -0.0426 0.0043 -0.1032
2073m1 2073m1 Monocyte d2073 11 #7570B3 2073m1 -0.0864 -0.3058 -0.0864 -0.3058 0.0630 0.0942 -0.0303 -0.1418 -0.3233 -0.2897 -0.0529 0.1374 0.1270 -0.2535 0.0156 0.0170 -0.1141 0.1318 -0.0090 0.1856 0.0637 -0.0119 0.0633 -0.0740 -0.0435 -0.0377 -0.1032 0.0462 -0.0189 0.0263 0.0153 0.1170 0.5165 0.0490 -0.0655 0.1818 0.1098 -0.0760 -0.0644 -0.1037 -0.0021 -0.0123 0.0776 0.1322 -0.0233 -0.1029 0.0584 0.0235 -0.0872 -0.0733 -0.1659 0.0062 -0.0526 -0.0544 -0.0219 0.0402 -0.1239 0.0051 0.0269 -0.0462 -0.0121 -0.0589 0.0102 0.0225 -0.0048
2073n1 2073n1 Neutrophil d2073 11 #D95F02 2073n1 0.2269 -0.1522 0.2269 -0.1522 0.2155 0.0841 0.0309 -0.0131 -0.0084 0.0943 0.0003 -0.0593 0.1415 0.0789 0.2801 0.1694 -0.0063 -0.1068 -0.1734 0.1747 0.0562 0.0078 0.1566 0.1262 -0.0599 0.1220 -0.0507 0.2498 0.1235 -0.3502 0.0746 -0.1759 -0.0216 -0.3421 0.1913 0.1260 0.0942 -0.0341 -0.0770 0.0261 0.0900 0.0269 -0.1243 -0.0630 -0.0433 -0.0199 -0.0587 -0.2036 -0.0655 0.0166 0.1510 -0.0617 -0.1549 0.0358 -0.0187 -0.0794 0.0516 0.0579 0.0097 -0.0412 -0.0393 -0.0101 0.0312 0.0035 -0.0096
2073e1 2073e1 Eosinophil d2073 11 #66A61E 2073e1 0.1400 -0.1090 0.1400 -0.1090 -0.1687 0.2585 -0.0005 -0.1756 0.0094 0.1334 -0.0777 0.0326 0.0667 -0.0753 -0.0342 -0.1334 0.0809 0.1502 -0.3128 0.0908 0.0470 0.0409 0.2807 0.2650 -0.1719 -0.0192 0.1420 -0.2138 0.0970 0.2932 -0.1644 -0.0664 0.0070 -0.1671 0.0618 -0.1371 -0.2179 -0.1508 0.2230 -0.0305 0.1004 0.0378 0.0220 -0.1190 0.0014 0.0417 0.1286 0.1543 0.0133 0.0678 -0.0372 0.0515 0.0555 0.0267 0.0176 -0.0186 0.0542 -0.0403 0.0355 0.0420 0.0137 -0.0254 -0.0038 -0.0152 0.0196
2073bp1 2073bp1 Biopsy d2073 11 #E7298A 2073bp1 -0.0057 0.0975 -0.0057 0.0975 0.0508 -0.0339 -0.0372 -0.0145 -0.0758 -0.0699 -0.0290 0.0797 0.0050 0.0748 -0.0123 -0.0775 0.0922 0.0376 -0.0861 -0.0880 -0.0900 -0.0567 -0.0099 -0.0561 -0.1026 -0.1234 -0.1379 0.0650 0.0667 0.0670 -0.0066 0.0767 0.0006 -0.0933 0.0037 -0.0731 -0.1235 0.1704 -0.1560 -0.1047 -0.0088 0.0529 -0.1492 -0.1736 0.0263 0.3326 -0.1652 -0.2138 -0.0184 0.0730 -0.3530 -0.0157 -0.0096 0.0803 -0.1579 0.2122 0.1813 -0.1963 0.0896 -0.4049 -0.0980 -0.1208 -0.0839 0.0358 0.1425
2068m2 2068m2 Monocyte d2068 8 #7570B3 2068m2 -0.2448 -0.1807 -0.2448 -0.1807 0.0395 -0.0263 0.0454 0.0337 0.1087 0.0705 -0.1101 0.1598 -0.1830 0.2474 0.1051 -0.0855 0.1760 -0.0801 -0.0608 -0.0166 0.1459 0.0355 -0.2435 0.0657 0.0440 0.2480 -0.0889 -0.2368 0.1242 0.1593 0.2704 -0.1114 -0.0849 -0.1098 -0.0335 -0.0026 0.0612 -0.1741 0.0026 -0.1179 0.1835 -0.0838 0.1744 0.2407 -0.1999 -0.0194 -0.0192 -0.1081 -0.0992 -0.1216 -0.1589 -0.0429 0.0117 -0.0221 -0.0807 0.0563 -0.0329 0.0705 0.1039 -0.0481 0.0393 -0.0124 0.0698 0.0072 0.0071
2068n2 2068n2 Neutrophil d2068 8 #D95F02 2068n2 0.0788 0.0993 0.0788 0.0993 0.0106 -0.0622 0.0385 -0.0137 -0.0413 -0.0503 -0.1104 0.0042 -0.1108 -0.0353 -0.0050 0.0210 0.1196 -0.1573 -0.0655 0.0373 -0.1525 0.1131 -0.0821 0.0489 -0.0239 -0.0019 -0.0410 -0.0406 -0.0472 0.1565 0.1394 0.1876 0.0273 -0.0381 0.0242 -0.1084 0.0327 0.1492 0.0418 -0.3242 -0.0364 0.0326 0.1815 -0.0906 0.3739 -0.2805 0.0968 -0.1233 -0.0991 0.0810 0.1478 -0.0017 -0.1017 0.1590 -0.0832 -0.2465 -0.0139 0.1133 -0.0650 -0.0240 -0.3932 0.0091 0.0599 -0.0473 0.0767
2068e2 2068e2 Eosinophil d2068 8 #66A61E 2068e2 0.0200 0.0509 0.0200 0.0509 -0.1504 0.0801 0.1308 -0.1333 0.0980 -0.0956 0.1645 0.0507 -0.1584 0.1354 -0.3368 0.1143 0.0424 -0.0901 -0.0476 0.0805 -0.0213 0.0197 0.0530 -0.0629 -0.1904 0.0699 -0.3694 0.1155 -0.0693 -0.2152 0.2684 0.0093 0.0820 0.0394 -0.0801 0.0300 -0.0076 -0.0433 0.5076 0.0569 -0.0133 0.0680 -0.1006 0.0204 0.0034 0.1118 -0.0397 0.0537 0.1320 0.0214 -0.0485 0.0699 -0.0115 0.0507 -0.0493 -0.0802 -0.0612 0.0354 -0.0070 -0.0001 0.0545 0.0007 -0.0477 -0.0311 0.0020
2072m2 2072m2 Monocyte d2072 10 #7570B3 2072m2 -0.2558 -0.1820 -0.2558 -0.1820 0.0846 0.0198 -0.1586 0.0274 0.1839 -0.0237 -0.2229 -0.0582 -0.1690 0.0650 -0.0788 0.0562 -0.0150 0.0204 0.0184 0.1730 -0.1784 0.2526 0.2260 -0.1029 0.2253 0.0813 -0.1244 -0.0850 -0.1490 0.1202 -0.0118 -0.1106 0.1573 -0.1888 -0.0301 -0.1338 0.0842 0.1869 -0.1440 0.1936 -0.0067 0.0680 -0.0701 0.0617 0.0640 0.1968 -0.0970 0.0699 -0.0126 0.2401 0.1683 0.0417 0.0907 0.0115 0.1151 -0.0006 0.0025 -0.0450 -0.2013 0.0366 0.0061 -0.0406 -0.0084 0.0125 0.0092
2072n2 2072n2 Neutrophil d2072 10 #D95F02 2072n2 0.0853 0.0810 0.0853 0.0810 0.0435 -0.1289 -0.0386 0.0196 -0.0067 -0.0097 -0.0953 0.0330 -0.0223 -0.0913 -0.0104 -0.0782 0.0822 0.0793 0.0828 0.0112 -0.0348 -0.0016 0.1115 -0.0434 -0.0986 0.0667 -0.0609 0.1865 -0.0389 0.1595 0.1243 0.0382 -0.0104 0.0837 -0.1414 -0.2080 0.0667 -0.2443 -0.0626 0.1320 0.1748 -0.2716 0.1176 -0.2695 0.1791 -0.0381 -0.1349 -0.1186 -0.0955 -0.0667 0.1643 -0.0379 -0.2069 0.0057 -0.1215 -0.0663 -0.0710 -0.1956 0.0165 0.0590 0.4607 -0.0911 0.0096 0.0925 -0.0044
2072e2 2072e2 Eosinophil d2072 10 #66A61E 2072e2 0.0315 0.0298 0.0315 0.0298 -0.1430 -0.0006 0.0533 -0.0855 0.0652 -0.0500 0.1685 -0.0224 -0.0945 0.0006 -0.0830 0.0912 -0.0383 0.0483 0.0196 0.0673 0.0384 0.1325 -0.0920 -0.0255 -0.0241 -0.0343 -0.1145 0.0133 0.0084 -0.0601 -0.1590 0.0072 -0.0577 -0.1448 -0.0637 -0.2059 0.1703 -0.0353 -0.0366 0.1135 -0.0770 -0.2227 -0.1162 -0.1120 -0.0422 -0.1657 0.4745 -0.2263 -0.2673 0.0559 0.0121 -0.1358 0.2405 -0.2528 -0.0546 0.2752 -0.0181 -0.0207 -0.0040 -0.0302 -0.0779 0.0487 -0.0225 0.0619 -0.0255
2073m2 2073m2 Monocyte d2073 11 #7570B3 2073m2 -0.0548 -0.2539 -0.0548 -0.2539 0.0098 0.1207 -0.0589 -0.1119 -0.2029 -0.1409 -0.0930 0.0746 -0.0448 -0.2001 -0.0585 0.2122 0.1119 0.0818 -0.0591 -0.0514 0.2008 -0.0501 -0.3539 0.0533 -0.1191 -0.0062 -0.0975 0.0324 0.1727 -0.0022 0.0642 -0.1879 -0.2934 0.3258 0.1193 -0.0851 -0.0448 0.1555 -0.1118 0.0822 0.1575 0.0506 -0.1618 -0.0570 0.1637 0.0707 -0.0176 0.0979 0.0090 0.0996 0.1944 0.0182 0.1002 0.0121 0.0481 -0.0127 0.0241 -0.0176 -0.0820 0.0306 -0.0016 0.0494 -0.0202 -0.0178 -0.0071
2073n2 2073n2 Neutrophil d2073 11 #D95F02 2073n2 0.1750 -0.0859 0.1750 -0.0859 -0.0216 0.1578 -0.0133 -0.0537 -0.0860 0.1189 -0.1554 -0.0545 -0.0085 -0.0220 0.3407 0.1086 0.0282 -0.1687 -0.1941 -0.0581 -0.1625 0.1671 -0.1308 0.0357 -0.0869 0.0817 -0.0519 -0.0531 -0.3423 -0.2193 -0.0525 0.0838 -0.0580 0.2469 -0.2109 -0.2079 0.0534 -0.0318 -0.0391 0.1077 -0.2468 0.0364 0.2568 -0.0080 -0.1535 0.0357 -0.0444 0.1306 0.0792 0.0768 -0.1550 -0.0346 0.0036 -0.0349 -0.0872 0.0640 0.0506 -0.0671 0.0307 0.0354 0.0127 -0.0301 0.0002 -0.0090 -0.0339
2073e2 2073e2 Eosinophil d2073 11 #66A61E 2073e2 0.1369 -0.0996 0.1369 -0.0996 -0.2142 0.2400 -0.0164 -0.1504 0.1529 0.1418 -0.0605 0.0969 0.1026 0.1208 -0.3147 -0.2320 0.1638 -0.1123 0.1750 -0.0200 -0.2449 -0.2186 0.0664 0.1299 -0.1035 0.0991 0.2541 0.0508 -0.0786 -0.0967 0.0781 -0.1303 0.0836 0.2249 -0.0163 0.1350 0.1137 0.1708 -0.2639 0.0357 0.0358 -0.0335 -0.0329 0.1564 0.0150 -0.0579 0.0229 -0.1238 -0.0984 0.0780 -0.0125 -0.0495 -0.0774 -0.0628 -0.0372 0.0272 -0.0149 0.0035 -0.0216 -0.0212 -0.0196 0.0219 0.0079 0.0082 -0.0003
2068m3 2068m3 Monocyte d2068 8 #7570B3 2068m3 -0.1347 -0.0978 -0.1347 -0.0978 0.0392 -0.0541 -0.1237 0.0217 0.0180 -0.0196 -0.0070 0.0777 -0.0206 0.1810 0.1039 -0.2632 -0.0672 -0.2028 0.0143 -0.0949 0.1115 -0.0606 -0.0333 -0.0862 -0.0136 -0.0584 -0.0156 0.1810 -0.0110 -0.0807 -0.2979 -0.3458 0.1666 0.1119 0.0691 -0.3437 -0.1719 -0.0332 0.1277 -0.2181 -0.1010 -0.0760 -0.1545 0.0429 -0.0289 -0.1704 0.0449 -0.0521 0.2068 -0.0103 0.0732 0.0733 -0.1198 0.0073 -0.0161 -0.0121 -0.2123 -0.1236 -0.0905 -0.0136 -0.0610 -0.1005 -0.0107 -0.0073 -0.0140
2068n3 2068n3 Neutrophil d2068 8 #D95F02 2068n3 0.1136 0.0971 0.1136 0.0971 0.0891 -0.1010 0.0780 0.0547 0.0514 0.0061 -0.2196 0.0238 -0.2937 -0.3420 0.0002 -0.2153 0.1356 -0.3412 0.2395 0.0056 0.0323 0.2515 -0.0375 0.0333 -0.2913 -0.1602 -0.0524 0.0223 0.1392 -0.0112 -0.1940 0.1100 0.0476 -0.0482 0.0276 0.1698 -0.0981 -0.0575 -0.0333 0.1762 0.1103 0.0513 -0.0596 0.1068 -0.1989 0.0873 0.0438 0.0193 0.0194 -0.0032 0.0536 0.0142 -0.0719 -0.0540 0.0895 -0.0134 0.0026 0.0836 0.0246 -0.0336 -0.0427 0.0286 -0.0172 -0.0163 -0.0398
2068e3 2068e3 Eosinophil d2068 8 #66A61E 2068e3 0.0712 0.0465 0.0712 0.0465 -0.1530 -0.0040 0.0190 -0.0578 0.1205 -0.0289 0.0443 -0.0273 -0.1004 0.0603 -0.0290 0.0629 -0.1672 -0.0636 0.1617 -0.0615 0.1658 -0.0285 0.0069 -0.2281 0.1129 0.0050 -0.0124 0.0040 0.0337 -0.1128 -0.0504 -0.0902 0.0176 0.0475 0.1261 0.0721 0.0814 -0.3528 -0.1859 -0.1912 0.1979 0.2318 0.2137 -0.1411 -0.0091 0.0102 0.1498 0.2198 -0.1161 0.3865 -0.1169 0.0476 -0.1033 0.1885 0.0114 -0.0042 0.1717 -0.1491 -0.0282 -0.0057 0.0030 0.0169 -0.0004 -0.0163 0.0149
2072m3 2072m3 Monocyte d2072 10 #7570B3 2072m3 -0.1764 -0.0993 -0.1764 -0.0993 0.1061 0.0295 -0.3364 0.0264 0.1437 0.0490 -0.2043 -0.0505 -0.1412 0.0737 -0.1592 0.1775 -0.0228 -0.0739 -0.1330 -0.0905 -0.0434 0.0494 0.1982 -0.0543 0.1332 0.0179 -0.0325 0.0910 0.0920 -0.0138 -0.1390 0.0180 -0.0858 0.2385 0.0868 0.2555 -0.1174 -0.0513 0.0234 0.0907 -0.1132 -0.0198 -0.0126 -0.2303 0.0925 -0.1358 0.1704 -0.0046 -0.0286 -0.3155 -0.2040 -0.0519 -0.0917 -0.0473 -0.0844 -0.0755 0.1464 0.1013 0.2050 0.0198 -0.0123 0.0792 -0.0073 -0.0200 0.0005
2072n3 2072n3 Neutrophil d2072 10 #D95F02 2072n3 0.1057 0.0502 0.1057 0.0502 0.0037 -0.1395 -0.0257 0.0158 -0.0338 0.0462 -0.0690 0.0382 -0.0812 0.0035 0.0467 0.0449 -0.0382 -0.0126 0.0607 0.0708 -0.1424 -0.0819 -0.0188 0.1540 0.0402 0.0977 -0.0379 -0.0238 0.0012 -0.2029 -0.1242 0.0615 0.0386 0.0221 0.1166 0.0165 -0.1956 -0.2351 -0.0030 -0.0305 0.0409 0.0192 -0.0372 0.2368 0.3974 -0.0735 -0.1856 -0.1838 -0.0755 -0.0140 -0.1924 0.1889 0.4750 0.0137 0.1900 0.0559 0.0071 -0.0834 0.0412 0.1752 0.0832 -0.0463 -0.0031 0.0384 0.0137
2072e3 2072e3 Eosinophil d2072 10 #66A61E 2072e3 0.0407 0.0619 0.0407 0.0619 -0.1190 0.0344 0.0001 -0.0967 0.0999 -0.0203 0.1184 0.1408 -0.1772 0.1109 -0.1301 0.2535 0.0341 0.0642 0.0139 -0.0466 -0.0282 0.0541 -0.1184 0.1266 -0.1519 -0.1468 0.0795 0.0891 0.1641 0.0793 -0.1400 -0.1001 0.1367 -0.1713 -0.0146 -0.0663 0.2881 -0.0824 -0.2407 -0.0744 -0.2438 0.0393 0.1314 -0.1172 -0.0606 -0.0693 -0.3234 0.1655 0.1502 -0.3225 0.0880 0.0384 0.0831 0.0361 0.1646 -0.0038 0.0302 0.0188 0.0193 -0.0248 -0.0230 -0.0668 0.0691 -0.0032 0.0218
2159bp1 2159bp1 Biopsy d2159 12 #E7298A 2159bp1 0.0449 0.0728 0.0449 0.0728 -0.0674 -0.1592 0.0552 0.0075 -0.1228 0.0867 -0.0403 0.0645 0.0097 0.0279 -0.0973 0.0569 0.0241 -0.0162 -0.1576 0.2656 -0.0399 0.0488 -0.1067 -0.0536 0.1798 -0.1804 0.1324 0.0287 -0.0890 0.0044 -0.1104 -0.1272 0.0007 0.1413 0.0864 0.1420 0.1145 0.0631 0.0481 -0.1846 0.0799 0.0043 0.2228 -0.0731 -0.1345 0.4658 0.1851 -0.2965 0.1081 -0.0828 0.1325 0.1666 0.0668 -0.0969 -0.0449 -0.1150 -0.0918 -0.0193 0.0872 0.1714 0.0461 -0.0689 0.0168 0.0339 0.0380
2073m3 2073m3 Monocyte d2073 11 #7570B3 2073m3 -0.0644 -0.2495 -0.0644 -0.2495 0.0218 -0.0527 -0.0244 -0.1030 -0.1478 -0.3393 -0.0550 0.0743 0.1393 -0.0795 -0.1640 0.1274 -0.1082 -0.2788 0.3153 0.0282 -0.1131 -0.1318 0.0841 -0.0449 0.0235 0.0650 0.2877 -0.1551 -0.0508 -0.0369 -0.0789 0.0456 -0.3624 -0.2623 -0.0858 -0.1323 -0.0039 -0.0924 0.1816 -0.0162 -0.1000 0.0067 0.0566 -0.0420 -0.0838 0.0460 -0.0769 -0.1154 0.0677 -0.0266 0.0054 -0.0375 -0.0134 0.0764 -0.0405 -0.0241 0.0738 -0.0109 0.0664 -0.0095 0.0078 0.0307 -0.0079 -0.0074 -0.0006
2073n3 2073n3 Neutrophil d2073 11 #D95F02 2073n3 0.2411 -0.1420 0.2411 -0.1420 0.3123 0.0079 0.0843 0.0317 0.2980 -0.0120 0.1247 -0.0738 0.3741 0.2511 0.0805 0.2171 0.1098 -0.2010 0.1702 0.1614 0.0451 0.0590 -0.0144 -0.1497 -0.0984 -0.2575 -0.0320 -0.0284 0.0521 0.3536 0.0357 0.0421 0.0170 0.1794 -0.0381 0.0153 -0.0486 0.0127 0.0248 0.0051 0.0143 -0.0172 -0.0273 0.0731 0.0640 -0.0097 0.0462 0.0981 -0.0033 -0.0110 -0.0713 0.0574 0.1172 -0.0334 0.0104 0.0498 -0.0172 -0.0376 -0.0120 0.0300 0.0241 -0.0040 -0.0121 0.0132 0.0094
2073e3 2073e3 Eosinophil d2073 11 #66A61E 2073e3 0.1595 -0.1229 0.1595 -0.1229 -0.2086 0.3264 -0.0153 -0.1123 0.0808 0.1942 -0.1409 -0.0617 0.1532 -0.0870 0.0147 -0.2358 0.1101 0.1048 0.1504 0.0019 0.1064 0.0312 -0.2227 -0.3019 0.3983 0.0001 -0.2017 0.1554 0.0869 -0.0109 -0.0708 0.2519 -0.0976 -0.1760 -0.0485 0.0220 -0.0514 -0.0230 0.0587 -0.0617 -0.0914 0.0058 -0.0237 -0.0257 0.0285 0.0181 -0.1047 -0.0499 0.0672 -0.1618 0.0844 0.0127 0.0379 0.0536 0.0608 -0.0247 -0.0260 0.0429 0.0092 -0.0143 0.0093 0.0087 -0.0014 -0.0119 -0.0080
2162m1 2162m1 Monocyte d2162 13 #7570B3 2162m1 -0.0559 0.0081 -0.0559 0.0081 0.0934 0.0139 -0.1260 -0.0406 -0.0859 -0.1043 0.0705 0.2313 0.0290 0.1773 0.0633 -0.0840 0.0325 0.0076 -0.0776 -0.3450 -0.1201 -0.1892 0.0868 0.0038 -0.0282 -0.1953 -0.1229 0.2422 0.0744 -0.0044 0.1325 0.0961 -0.0648 -0.1704 -0.1131 0.0836 -0.2798 0.0384 -0.0656 0.0962 -0.0914 0.0697 0.2987 0.0490 -0.1547 0.0573 0.1555 0.0683 -0.2114 0.0942 0.1930 0.0332 0.1688 -0.1177 0.1139 -0.1458 -0.0347 -0.0385 -0.0602 0.1534 0.0006 0.0032 0.0063 0.0023 0.0074
2162n1 2162n1 Neutrophil d2162 13 #D95F02 2162n1 0.1377 0.0906 0.1377 0.0906 0.3617 -0.0417 0.1719 0.0247 0.4764 -0.3369 -0.0671 0.1906 0.1400 -0.1930 -0.2014 -0.1170 -0.0072 0.2338 -0.2574 -0.1146 0.0258 -0.0478 -0.1668 0.0805 0.1316 0.1110 -0.0033 -0.1181 -0.1566 -0.1636 -0.1284 0.0083 0.0070 -0.0005 0.0369 -0.0201 -0.0191 -0.0176 -0.0347 0.0029 -0.0228 0.0150 0.0340 -0.0021 -0.0577 -0.0240 0.0102 -0.0132 0.0499 0.0036 0.0155 -0.0309 -0.0525 0.0275 0.0171 -0.0375 -0.0100 0.0054 0.0022 -0.0081 0.0071 0.0084 0.0011 -0.0124 -0.0100
2162e1 2162e1 Eosinophil d2162 13 #66A61E 2162e1 0.1220 0.0556 0.1220 0.0556 -0.0841 0.1634 0.0341 -0.0809 0.1515 -0.0306 -0.2403 0.0631 -0.2204 0.0227 0.2809 0.0825 -0.4575 0.0751 0.0450 -0.1703 -0.0683 -0.1610 -0.1167 -0.2004 -0.1250 -0.1985 0.1559 -0.1923 -0.1640 0.0751 0.2365 -0.0853 0.0879 -0.0868 0.0563 0.1343 -0.0469 0.0865 0.0923 0.0476 0.0857 -0.1153 -0.1927 -0.0816 0.0415 0.0102 0.0233 -0.0759 0.0216 -0.1060 0.0566 0.0279 0.0216 -0.0054 -0.0301 0.0111 -0.0161 0.0371 0.0151 0.0418 0.0031 -0.0818 0.0410 0.0462 -0.0367
2162bp1 2162bp1 Biopsy d2162 13 #E7298A 2162bp1 0.0930 0.0383 0.0930 0.0383 -0.0548 -0.0702 -0.0326 -0.0334 -0.0207 -0.0012 -0.0397 0.0248 -0.0456 0.0337 0.1191 0.0939 -0.0850 0.0437 -0.0582 -0.0406 -0.0561 0.0007 0.0270 0.0297 0.0297 0.0729 0.0361 0.0545 -0.0553 0.1291 -0.0742 0.0068 -0.0384 0.0587 -0.0843 0.0785 0.0501 -0.0666 0.0382 -0.1268 0.0055 -0.0294 -0.0750 0.1106 -0.0522 0.0026 0.0101 -0.0515 0.0285 0.0220 0.0271 -0.0866 -0.0162 -0.0718 0.2144 -0.0884 -0.1265 0.0349 -0.1675 -0.2954 0.1968 0.5003 -0.2934 -0.2407 0.4236
macrofagos Macrofagos Macrophage unknown 14 #E6AB02 Macrofagos -0.1294 0.0584 -0.1294 0.0584 0.0200 0.0315 -0.0073 0.0190 0.0045 0.1224 0.2121 0.0977 0.0978 -0.0502 0.0744 -0.0030 -0.0634 0.0160 -0.0662 0.0078 -0.1381 0.0304 -0.0240 -0.1784 -0.1031 0.1085 -0.0382 -0.2295 0.1741 -0.0705 -0.1063 -0.0258 0.0022 -0.0099 -0.4142 -0.0093 0.0059 0.1631 -0.0420 0.0425 0.2421 0.3576 -0.0200 -0.0867 0.0374 -0.1914 0.0231 -0.1919 0.0546 -0.1103 0.0432 0.2897 -0.1907 -0.0389 0.1363 0.0684 0.0805 -0.0627 0.0611 0.1319 0.0211 0.0857 -0.1218 0.0374 -0.0295
macrofagos+sbv Macrofagos+SbV Macrophage unknown 14 #E6AB02 Mcrfgs+SbV -0.1265 0.2124 -0.1265 0.2124 0.0987 0.1910 0.0148 -0.0185 -0.0756 -0.1271 -0.0043 -0.1723 0.0714 0.0564 0.0773 -0.1141 0.0304 0.0507 0.0278 -0.0038 -0.1505 0.0111 0.0472 -0.0856 -0.0824 0.0333 -0.0069 -0.1032 0.1495 -0.0672 -0.0176 -0.0153 -0.1044 0.0500 0.1518 0.0230 0.1226 -0.0669 0.0645 0.0878 -0.0318 0.0691 0.0812 -0.0102 0.1208 0.1012 0.0647 0.0127 0.0663 -0.0563 -0.0554 0.0154 -0.0462 -0.0961 0.1044 0.0818 -0.2406 -0.1108 -0.1155 -0.1281 0.0047 0.1877 0.6493 0.0072 0.0918
macrofagos+10772 Macrofagos+10772 Macrophage unknown 14 #E6AB02 Mcrf+10772 -0.1676 0.1172 -0.1676 0.1172 0.0920 0.1297 -0.0778 0.0454 0.0287 0.2001 0.2103 0.1382 0.1342 -0.2319 -0.0023 0.0024 -0.1620 -0.0909 -0.0059 -0.0008 -0.2229 0.0282 0.0526 -0.1337 -0.1297 0.1223 -0.1786 -0.0386 0.0327 -0.0150 -0.1643 -0.0518 -0.0784 0.0319 0.0321 0.1182 0.0365 0.1180 0.0221 -0.3005 0.1432 -0.4369 0.0391 0.0082 -0.0978 0.0257 -0.1288 0.1707 -0.0111 0.0910 0.0672 -0.1527 0.2176 0.1142 -0.0670 -0.0428 0.1119 0.0985 0.0260 0.0200 0.0359 -0.0231 0.0697 -0.0342 -0.0099
macrofagos+10772+sbv Macrofagos+10772+SbV Macrophage unknown 14 #E6AB02 M+10772+SV -0.1229 0.2369 -0.1229 0.2369 0.1019 0.2124 -0.0007 -0.0108 -0.0698 -0.0656 -0.0381 -0.1498 0.0504 0.0800 0.0377 0.0178 0.0695 0.0276 0.0307 -0.0431 -0.0234 -0.0110 0.0157 -0.0350 -0.0330 0.0749 0.0770 -0.1649 0.2139 -0.0660 -0.0253 0.0337 -0.0706 0.0304 0.0936 0.1211 0.1124 -0.1161 0.0282 0.0775 -0.0563 -0.0610 0.1458 0.0168 0.1027 0.0357 0.0562 -0.0211 0.1108 0.0135 -0.0091 -0.1111 -0.0297 -0.0644 0.0360 0.0015 -0.1980 0.0481 -0.2558 -0.0338 -0.0492 -0.4230 -0.5328 0.0074 -0.0303
macrofagos+2169 Macrofagos+2169 Macrophage unknown 14 #E6AB02 Mcrfg+2169 -0.1317 0.1120 -0.1317 0.1120 0.0501 0.0844 -0.0164 0.0464 0.0407 0.2408 0.1494 0.1598 -0.0007 -0.1664 -0.0110 0.2034 -0.0114 -0.1288 -0.0471 -0.0210 0.2087 -0.1318 -0.0983 -0.0373 0.0256 0.1740 0.2271 0.0398 0.0179 0.0340 0.0178 0.1582 0.1218 -0.1113 0.0067 -0.0902 -0.0340 -0.0562 -0.0543 0.1775 -0.2423 -0.1596 -0.0687 0.2278 0.1876 0.1964 0.1743 0.0774 -0.0263 0.0525 0.0226 0.3482 -0.1466 -0.0264 -0.1327 -0.1647 0.0604 -0.0644 0.0132 -0.1871 0.0174 0.0259 0.0452 0.0106 -0.0075
macrofagos+2169+sbv Macrofagos+2169+SbV Macrophage unknown 14 #E6AB02 Mc+2169+SV -0.1195 0.2094 -0.1195 0.2094 0.0439 0.2108 0.1606 -0.0430 -0.0485 -0.1297 0.0213 -0.1705 -0.0815 0.1222 -0.0112 0.1491 0.0214 -0.0385 0.0663 0.0699 0.2153 0.0651 -0.0971 0.0232 0.0431 0.2685 0.2845 0.2613 -0.0781 0.0745 -0.0929 0.0392 0.2304 -0.0085 -0.1055 0.0309 -0.3649 0.2019 0.0150 0.0714 0.1989 -0.0172 0.1687 -0.1052 -0.0671 -0.0568 -0.0859 -0.0807 0.0866 0.0422 0.0167 -0.2002 0.1238 0.0653 0.0137 0.0974 0.0314 -0.0005 0.0707 0.0516 0.0004 0.0821 0.0536 0.0119 0.0085
macrofagos+12309 Macrofagos+12309 Macrophage unknown 14 #E6AB02 Mcrf+12309 0.0593 0.0377 0.0593 0.0377 -0.0633 -0.0606 -0.0355 -0.0417 -0.0249 -0.0387 0.0378 0.0059 -0.0191 0.0243 0.0459 0.0998 0.0334 0.0235 -0.0609 -0.1345 -0.0762 0.0659 0.0244 0.0590 0.0818 0.0310 0.0160 0.0417 0.0072 0.1104 -0.0746 0.0225 -0.0430 0.0209 -0.0084 0.0770 0.0014 -0.0215 0.0675 -0.1706 -0.0235 0.0354 -0.0914 0.1347 0.0354 0.0847 -0.0607 0.0384 0.0011 0.0831 0.0047 -0.1150 -0.1429 -0.0145 0.0556 0.0335 -0.0931 0.0739 -0.0266 -0.0647 0.0119 0.2297 -0.0610 0.7307 -0.3843
macrofagos+12309+sbv Macrofagos+12309+SbV Macrophage unknown 14 #E6AB02 M+12309+SV -0.0803 0.2160 -0.0803 0.2160 0.0749 0.1428 0.0557 -0.0333 -0.1069 -0.1929 -0.0961 -0.1813 0.0207 0.1071 0.0303 -0.1008 0.0485 0.0534 0.0187 0.0103 -0.0757 0.0033 -0.0257 -0.0208 -0.0369 0.0424 -0.0112 0.0658 0.0158 0.0937 0.0485 0.0586 0.0067 -0.0011 0.2536 -0.1641 0.1601 0.0319 0.0016 -0.1550 -0.1245 -0.0709 -0.1881 0.0690 -0.1350 0.0194 -0.1014 0.0701 -0.1912 0.0641 -0.0295 0.2367 -0.1489 -0.1530 0.0535 0.0114 0.1899 0.0014 0.3014 0.3611 0.0373 0.1993 -0.1842 -0.1593 -0.1098
macrofagos+12367+sbv Macrofagos+12367+SbV Macrophage unknown 14 #E6AB02 M+12367+SV -0.0527 0.1577 -0.0527 0.1577 0.0338 0.0333 -0.0213 0.0008 -0.0830 -0.0564 -0.0337 -0.1460 0.0777 0.0326 0.0427 -0.1257 -0.0031 0.1189 0.0683 0.0535 0.0296 -0.0104 0.0599 -0.0151 -0.0430 0.0052 0.0054 0.0370 -0.1166 0.0324 -0.0152 -0.0793 -0.0233 0.1486 0.0115 -0.1230 0.1322 -0.0405 0.0124 0.1374 0.0434 0.1895 -0.0791 0.0874 -0.2606 -0.1380 0.0811 -0.0467 -0.1476 -0.1925 0.0596 0.1684 0.3260 0.2989 -0.1383 -0.3484 0.2090 0.1449 -0.0679 -0.2731 0.0390 -0.0603 -0.0087 0.2128 0.0341
macrofagos+1126 Macrofagos+1126 Macrophage unknown 14 #E6AB02 Mcrfg+1126 0.0431 0.0429 0.0431 0.0429 -0.0623 -0.0865 -0.0469 -0.0300 -0.0198 -0.0238 0.0610 -0.0011 -0.0025 0.0150 0.0601 0.0672 0.0212 0.0719 -0.0307 -0.0853 -0.0307 0.0217 0.0489 0.0190 0.0522 -0.0104 0.0235 0.0438 -0.0162 0.1235 -0.0630 -0.0068 -0.0158 0.0432 -0.0863 0.0225 0.0445 -0.0072 0.0228 -0.0206 0.0423 0.1009 -0.0569 0.1038 0.0387 0.0442 0.0234 -0.1417 -0.0026 0.0316 -0.0226 -0.1193 0.0236 0.0524 0.0415 -0.0756 -0.0137 0.0272 -0.0180 -0.2050 0.0936 -0.0686 0.1020 -0.5357 -0.6831
macrofagos+12251 Macrofagos+12251 Macrophage unknown 14 #E6AB02 Mcrf+12251 -0.1158 0.1537 -0.1158 0.1537 0.0959 0.0908 -0.1122 0.0624 -0.0078 0.2665 0.0914 0.2215 0.0980 -0.2637 -0.0681 0.0354 -0.0833 -0.1765 0.0270 -0.0126 0.1128 -0.0842 0.0508 0.0754 0.0873 -0.0388 -0.0345 0.0826 -0.2505 0.1937 0.2162 0.0335 -0.0116 0.0428 0.3055 -0.1524 0.1040 -0.0587 0.0832 0.0552 -0.0015 0.3528 -0.0084 -0.1162 -0.1151 -0.0818 -0.0926 -0.1347 -0.0385 -0.0322 -0.0734 -0.1968 0.0333 -0.0727 0.1694 0.1807 -0.1263 0.0088 -0.0318 0.0682 -0.0478 -0.0180 -0.0340 0.0092 0.0127
macrofagos+12251+sbv Macrofagos+12251+SbV Macrophage unknown 14 #E6AB02 M+12251+SV -0.0832 0.2643 -0.0832 0.2643 0.1091 0.1773 -0.0178 0.0154 -0.1187 0.0441 -0.1138 -0.0042 0.0680 0.0365 -0.0524 0.0218 -0.0384 -0.0206 0.2135 0.0983 0.2110 -0.0229 0.0193 0.4412 0.2158 -0.3167 -0.2186 -0.1600 -0.1518 -0.0920 0.0364 -0.2243 -0.0657 -0.0586 -0.3606 0.1243 -0.0510 -0.0448 -0.0555 -0.1006 -0.0507 -0.0825 -0.0635 -0.0207 0.0977 -0.0322 0.0033 0.0620 0.0158 0.0459 0.0141 0.0284 -0.0959 -0.0399 -0.0408 0.0042 0.0470 0.0009 0.0492 -0.0357 0.0247 0.0402 0.0373 -0.0140 -0.0051 
write.csv(all_pca$table, file="hs_donor_pca_coords.csv")
plot_corheat(all_norm)$plot

plot_topn(hs_valid)$plot
## `geom_smooth()` using formula 'y ~ x'

5 Cell types

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

5.1 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.

5.2 Monocytes

mono <- subset_expt(hs_valid, subset="typeofcells=='Monocytes'")
## Using a subset expression.
## There were 64, now there are 11 samples.
mono <- set_expt_conditions(mono, fact="clinicaloutcome")
mono <- set_expt_batches(mono, fact="donor")
save_result <- save(mono, file = "monocyte_expt.rda")
mono_norm <- sm(normalize_expt(mono, transform="log2", convert="cpm", batch="svaseq", filter=TRUE))
plot_pca(mono_norm)$plot

mono_de <- sm(all_pairwise(mono, model_batch="svaseq", filter=TRUE))

keepers <- list(
  "fail_vs_cure" = c("failure", "cure"))
mono_tables <- combine_de_tables(mono_de, keepers=keepers,
                                 excel=glue::glue("excel/mono_clinical_de-v{ver}.xlsx"))
## Deleting the file excel/mono_clinical_de-v202011.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: 0.908; equation: y = 1.08x - 0.714
## Deseq expression coefficients for fail_vs_cure; R^2: 0.932; equation: y = 1.01x - 0.122
## Edger expression coefficients for fail_vs_cure; R^2: 0.931; equation: y = 1.01x - 0.12
## Writing summary information, compare_plot is: TRUE.
## Performing save of excel/mono_clinical_de-v202011.xlsx.

mono_sig <- extract_significant_genes(mono_tables, according_to="deseq",
                                      excel=glue::glue("excel/mono_clinical_sig-v{ver}.xlsx"))
## Writing a legend of columns.
## Printing significant genes to the file: excel/mono_clinical_sig-v202011.xlsx
## 1/1: Creating significant table up_deseq_fail_vs_cure
## Adding significance bar plots.
mono_pct_sig <- extract_significant_genes(mono_tables, top_percent=20, according_to="deseq",
                                          excel=glue::glue("excel/mono_clinical_sig_pct-v{ver}.xlsx"))
## Setting n to 2130
## Writing a legend of columns.
## Getting the top and bottom 2130 genes.
## Printing significant genes to the file: excel/mono_clinical_sig_pct-v202011.xlsx
## 1/1: Creating significant table up_deseq_fail_vs_cure
## Adding significance bar plots.
mono_sig$summary_df
##              deseq_change_counts_up deseq_change_counts_down
## fail_vs_cure                    624                     1516

5.3 Neutrophils

neut <- subset_expt(hs_valid, subset="typeofcells=='Neutrophils'")
## Using a subset expression.
## There were 64, now there are 13 samples.
neut <- set_expt_conditions(neut, fact="clinicaloutcome")
neut <- set_expt_batches(neut, fact="donor")
save_result <- save(neut, file = "neutrophil_expt.rda")
neut_norm <- sm(normalize_expt(neut, transform="log2", convert="cpm", batch="svaseq", filter=TRUE))
plot_pca(neut_norm)$plot

neut_de <- sm(all_pairwise(neut, model_batch="svaseq", filter=TRUE))

neut_tables <- combine_de_tables(neut_de, keepers=keepers,
                                 excel=glue::glue("excel/neut_clinical_de-v{ver}.xlsx"))
## Deleting the file excel/neut_clinical_de-v202011.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: 0.863; equation: y = 0.998x - 0.256
## Deseq expression coefficients for fail_vs_cure; R^2: 0.888; equation: y = 0.917x + 0.744
## Edger expression coefficients for fail_vs_cure; R^2: 0.887; equation: y = 0.918x + 0.891
## Writing summary information, compare_plot is: TRUE.
## Performing save of excel/neut_clinical_de-v202011.xlsx.

neut_sig <- extract_significant_genes(neut_tables, according_to="deseq",
                                      excel=glue::glue("excel/neut_clinical_sig-v{ver}.xlsx"))
## Writing a legend of columns.
## Printing significant genes to the file: excel/neut_clinical_sig-v202011.xlsx
## 1/1: Creating significant table up_deseq_fail_vs_cure
## Adding significance bar plots.
neut_pct_sig <- extract_significant_genes(neut_tables, top_percent=20, according_to="deseq",
                                          excel=glue::glue("excel/neut_clinical_sig_pct-v{ver}.xlsx"))
## Setting n to 1758
## Writing a legend of columns.
## Getting the top and bottom 1758 genes.
## Printing significant genes to the file: excel/neut_clinical_sig_pct-v202011.xlsx
## 1/1: Creating significant table up_deseq_fail_vs_cure
## Adding significance bar plots.

5.4 Eosinophils

eo <- subset_expt(hs_valid, subset="typeofcells=='Eosinophils'")
## Using a subset expression.
## There were 64, now there are 11 samples.
eo <- set_expt_conditions(eo, fact="clinicaloutcome")
eo <- set_expt_batches(eo, fact="donor")
save_result <- save(eo, file = "eosinophil_expt.rda")
eo_norm <- sm(normalize_expt(eo, transform="log2", convert="cpm", batch="svaseq", filter=TRUE))
plot_pca(eo_norm)$plot

eo_de <- sm(all_pairwise(eo, model_batch="svaseq", filter=TRUE))

eo_tables <- combine_de_tables(eo_de, keepers=keepers,
                               excel=glue::glue("excel/eo_clinical_de-v{ver}.xlsx"))
## Deleting the file excel/eo_clinical_de-v202011.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: 0.914; equation: y = 1.08x - 0.747
## Deseq expression coefficients for fail_vs_cure; R^2: 0.931; equation: y = 1x - 0.0128
## Edger expression coefficients for fail_vs_cure; R^2: 0.931; equation: y = 1x - 0.0567
## Writing summary information, compare_plot is: TRUE.
## Performing save of excel/eo_clinical_de-v202011.xlsx.

eo_sig <- extract_significant_genes(eo_tables, according_to="deseq",
                                    excel=glue::glue("excel/eo_clinical_sig-v{ver}.xlsx"))
## Writing a legend of columns.
## Printing significant genes to the file: excel/eo_clinical_sig-v202011.xlsx
## 1/1: Creating significant table up_deseq_fail_vs_cure
## Adding significance bar plots.
eo_pct_sig <- extract_significant_genes(eo_tables, top_percent=20, according_to="deseq",
                                        excel=glue::glue("excel/eo_clinical_sig_pct-v{ver}.xlsx"))
## Setting n to 2095
## Writing a legend of columns.
## Getting the top and bottom 2095 genes.
## Printing significant genes to the file: excel/eo_clinical_sig_pct-v202011.xlsx
## 1/1: Creating significant table up_deseq_fail_vs_cure
## Adding significance bar plots.

5.5 Biopsies

biop <- subset_expt(hs_valid, subset="typeofcells=='Biopsy'")
## Using a subset expression.
## There were 64, now there are 11 samples.
biop <- set_expt_conditions(biop, fact="visitnumber")
biop <- set_expt_batches(biop, fact="donor")
save_result <- save(biop, file = "biopsy_expt.rda")
biop_norm <- sm(normalize_expt(biop, transform="log2", convert="cpm", batch="svaseq", filter=TRUE))
## Error in `contrasts<-`(`*tmp*`, value = contr.funs[1 + isOF[nn]]) : 
##   contrasts can be applied only to factors with 2 or more levels
plot_pca(biop_norm)$plot

biop_de <- sm(all_pairwise(biop, model_batch="svaseq", filter=TRUE))
## Error in `contrasts<-`(`*tmp*`, value = contr.funs[1 + isOF[nn]]): contrasts can be applied only to factors with 2 or more levels
biop_tables <- combine_de_tables(biop_de, keepers=keepers,
                                 excel=glue::glue("excel/biop_clinical_de-v{ver}.xlsx"))
## Error in combine_de_tables(biop_de, keepers = keepers, excel = glue::glue("excel/biop_clinical_de-v{ver}.xlsx")): object 'biop_de' not found
biop_sig <- extract_significant_genes(biop_tables, according_to="deseq",
                                      excel=glue::glue("excel/biop_clinical_sig-v{ver}.xlsx"))
## Error in extract_significant_genes(biop_tables, according_to = "deseq", : object 'biop_tables' not found
biop_sig_pct <- extract_significant_genes(biop_tables, according_to="deseq", top_percent=20,
                                          excel=glue::glue("excel/biop_clinical_sig_pct-v{ver}.xlsx"))
## Error in extract_significant_genes(biop_tables, according_to = "deseq", : object 'biop_tables' not found

5.6 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_expt, subset="typeofcells=='Macrophages'")
## Using a subset expression.
## There were 70, now there are 12 samples.
macr <- set_expt_conditions(macr, fact="zymodeme")
macr <- set_expt_batches(macr, fact="macrdrug")

macr_norm <- normalize_expt(macr, transform="log2", norm="quant", convert="cpm", 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$best_libsize
## Not correcting the count-data for batch effects.  If batch is
##  included in EdgerR/limma's model, then this is probably wise; but in extreme
##  batch effects this is a good parameter to play with.
## Step 1: performing count filter with option: cbcb
## Removing 9365 low-count genes (10389 remaining).
## Step 2: normalizing the data with quant.
## Step 3: converting the data with cpm.
## Step 4: transforming the data with log2.
## Step 5: not doing batch correction.
norm_pca <- plot_pca(macr_norm)
pp(file="macrophage_side_experiment/norm_pca.png", image=norm_pca$plot)
## Writing the image to: macrophage_side_experiment/norm_pca.png and calling dev.off().

macr_nb <- normalize_expt(macr, batch="svaseq", filter=TRUE)
## This function will replace the expt$expressionset slot with:
## svaseq(cbcb(data))
## It will save copies of each step along the way
##  in expt$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$best_libsize
## Leaving the data in its current base format, keep in mind that
##  some metrics are easier to see when the data is log2 transformed, but
##  EdgeR/DESeq do not accept transformed data.
## Leaving the data unconverted.  It is often advisable to cpm/rpkm
##  the data to normalize for sampling differences, keep in mind though that rpkm
##  has some annoying biases, and voom() by default does a cpm (though hpgl_voom()
##  will try to detect this).
## Leaving the data unnormalized.  This is necessary for DESeq, but
##  EdgeR/limma might benefit from normalization.  Good choices include quantile,
##  size-factor, tmm, etc.
## Step 1: performing count filter with option: cbcb
## Removing 9365 low-count genes (10389 remaining).
## Step 2: not normalizing the data.
## Step 3: not converting the data.
## Step 4: not transforming the data.
## Step 5: doing batch correction with svaseq.
## Using the current state of normalization.
## Passing the data to all_adjusters using the svaseq estimate type.
## batch_counts: Before batch/surrogate estimation, 124526 entries are x>1: 100%.
## batch_counts: Before batch/surrogate estimation, 66 entries are x==0: 0%.
## The be method chose 2 surrogate variables.
## Attempting svaseq estimation with 2 surrogates.
## There are 20 (0%) elements which are < 0 after batch correction.
## Setting low elements to zero.
macr_nb <- normalize_expt(macr_nb, norm="quant", convert="cpm", transform="log2")
## This function will replace the expt$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.
## Step 4: transforming the data with log2.
## Step 5: not doing batch correction.
nb_pca <- plot_pca(macr_nb)$plot
pp(file="macrophage_side_experiment/normbatch_pca.png", image=norm_pca$plot)
## Writing the image to: macrophage_side_experiment/normbatch_pca.png and calling dev.off().

macr_written <- write_expt(macr, excel="macrophage_side_experiment/macrophage_expt.xlsx")
## 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:101 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:70 s
## Placing factor: condition at the beginning of the model.
## Writing the median reads by factor.

zymo_de <- all_pairwise(macr, model_batch="svaseq", filter=TRUE)
## batch_counts: Before batch/surrogate estimation, 124526 entries are x>1: 100%.
## batch_counts: Before batch/surrogate estimation, 66 entries are x==0: 0%.
## The be method chose 2 surrogate variables.
## Attempting svaseq estimation with 2 surrogates.
## Plotting a PCA before surrogate/batch inclusion.
## Not putting labels on the PC plot.
## Using svaseq to visualize before/after batch inclusion.
## Performing a test normalization with: raw
## This function will replace the expt$expressionset slot with:
## log2(svaseq(cpm(cbcb(data))))
## It will save copies of each step along the way
##  in expt$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 unnormalized.  This is necessary for DESeq, but
##  EdgeR/limma might benefit from normalization.  Good choices include quantile,
##  size-factor, tmm, etc.
## Step 1: performing count filter with option: cbcb
## Removing 0 low-count genes (10389 remaining).
## Step 2: not normalizing the data.
## Step 3: converting the data with cpm.
## Step 4: transforming the data with log2.
## transform_counts: Found 66 values equal to 0, adding 1 to the matrix.
## Step 5: doing batch correction with svaseq.
## Using the current state of normalization.
## Passing the data to all_adjusters using the svaseq estimate type.
## batch_counts: Before batch/surrogate estimation, 121056 entries are x>1: 97%.
## batch_counts: Before batch/surrogate estimation, 66 entries are x==0: 0%.
## batch_counts: Before batch/surrogate estimation, 3546 entries are 0<x<1: 3%.
## The be method chose 2 surrogate variables.
## Attempting svaseq estimation with 2 surrogates.
## There are 175 (0%) elements which are < 0 after batch correction.
## Setting low elements to zero.
## Not putting labels on the PC plot.
## Finished running DE analyses, collecting outputs.
## Comparing analyses.

zymo_table <- combine_de_tables(zymo_de, excel="macrophage_side_experiment/macrophage_de.xlsx")
## Deleting the file 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: 0.96; equation: y = 0.971x + 0.159
## Deseq expression coefficients for z22_vs_undef; R^2: 0.959; equation: y = 1.02x - 0.176
## Edger expression coefficients for z22_vs_undef; R^2: 0.959; equation: y = 1.02x - 0.153
## Adding venn plots for z23_vs_undef.

## Limma expression coefficients for z23_vs_undef; R^2: 0.955; equation: y = 0.976x + 0.157
## Deseq expression coefficients for z23_vs_undef; R^2: 0.952; equation: y = 0.971x + 0.32
## Edger expression coefficients for z23_vs_undef; R^2: 0.952; equation: y = 0.971x + 0.296
## Adding venn plots for z23_vs_z22.

## Limma expression coefficients for z23_vs_z22; R^2: 0.968; equation: y = 0.988x + 0.0835
## Deseq expression coefficients for z23_vs_z22; R^2: 0.965; equation: y = 0.943x + 0.632
## Edger expression coefficients for z23_vs_z22; R^2: 0.965; equation: y = 0.943x + 0.432
## Writing summary information, compare_plot is: TRUE.
## Performing save of macrophage_side_experiment/macrophage_de.xlsx.

5.7 Donor effect

donor <- set_expt_conditions(hs_expt, fact="donor")
donor <- set_expt_batches(donor, fact="time")
save_result <- save(donor, file = "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$best_libsize
## Not correcting the count-data for batch effects.  If batch is
##  included in EdgerR/limma's model, then this is probably wise; but in extreme
##  batch effects this is a good parameter to play with.
## Step 1: performing count filter with option: cbcb
## Removing 4756 low-count genes (14998 remaining).
## Step 2: normalizing the data with quant.
## Step 3: converting the data with cpm.
## Step 4: transforming the data with log2.
## transform_counts: Found 4080 values equal to 0, adding 1 to the matrix.
## Step 5: not doing batch correction.
plot_pca(test)$plot
## Warning in MASS::cov.trob(data[, vars]): Probable convergence failure

## Warning in MASS::cov.trob(data[, vars]): Probable convergence failure

5.8 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 64, 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$table)
sampleid condition batch batch_int colors labels PC1 PC2 pc_1 pc_2 pc_3 pc_4 pc_5
1010-2 1010-2 PBMC d1010 1 #1B9E77 1010-2 -0.5597 0.2860 -0.5597 0.2860 0.4198 -0.1032 0.5014
1010-7 1010-7 PBMC d1010 1 #1B9E77 1010-7 0.0137 0.4819 0.0137 0.4819 -0.6300 0.4508 0.0283
1010-12 1010-12 PBMC d1010 1 #1B9E77 1010-12 0.4174 0.4406 0.4174 0.4406 0.3520 -0.3314 -0.4809
1011-2 1011-2 PBMC d1011 2 #1B9E77 1011-2 -0.4953 -0.4061 -0.4953 -0.4061 -0.3185 -0.3369 -0.4563
1011-7 1011-7 PBMC d1011 2 #1B9E77 1011-7 0.1215 -0.4505 0.1215 -0.4505 0.3933 0.6660 -0.1319
1011-12 1011-12 PBMC d1011 2 #1B9E77 1011-12 0.5023 -0.3520 0.5023 -0.3520 -0.2166 -0.3452 0.5394 
write.csv(q1_pca$table, file="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, transform="log2",
                               batch="svaseq", filter=TRUE))
## 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,] "17.79:ENSG00000038427" "43.5:ENSG00000090382" 
##  [2,] "15.99:ENSG00000075624" "27.89:ENSG00000198804"
##  [3,] "13.32:ENSG00000100201" "27.24:ENSG00000121966"
##  [4,] "12.45:ENSG00000090382" "26.44:ENSG00000128016"
##  [5,] "10.25:ENSG00000136167" "26.18:ENSG00000124942"
##  [6,] "10.03:ENSG00000134954" "23.29:ENSG00000075624"
##  [7,] "9.368:ENSG00000197629" "18.88:ENSG00000265972"
##  [8,] "7.881:ENSG00000111913" "16.19:ENSG00000019582"
##  [9,] "7.639:ENSG00000165168" "15.31:ENSG00000196924"
## [10,] "6.955:ENSG00000163131" "14.5:ENSG00000159388" 
## [11,] "6.087:ENSG00000188404" "14.06:ENSG00000125538"
## [12,] "6.001:ENSG00000168685" "13.82:ENSG00000171223"
## [13,] "5.939:ENSG00000125538" "12.33:ENSG00000100345"
## [14,] "5.547:ENSG00000127951" "12.21:ENSG00000152518"
## [15,] "5.428:ENSG00000140575" "11.99:ENSG00000130066"
## [16,] "4.94:ENSG00000118515"  "11.9:ENSG00000163220" 
## [17,] "4.795:ENSG00000081237" "11.65:ENSG00000118515"
## [18,] "4.699:ENSG00000122566" "11.44:ENSG00000134954"
## [19,] "4.45:ENSG00000196924"  "10.91:ENSG00000197746"
## [20,] "4.436:ENSG00000182578" "10.29:ENSG00000120129"
## [21,] "4.268:ENSG00000184009" "9.83:ENSG00000081237" 
## [22,] "4.213:ENSG00000261371" "9.598:ENSG00000116741"
## [23,] "3.803:ENSG00000110876" "9.316:ENSG00000038427"
## [24,] "3.792:ENSG00000160593" "9.273:ENSG00000087086"
## [25,] "3.76:ENSG00000130066"  "8.505:ENSG00000110324"
## [26,] "3.754:ENSG00000101347" "7.749:ENSG00000167978"
## [27,] "3.747:ENSG00000028137" "7.527:ENSG00000135821"
## [28,] "3.734:ENSG00000185811" "6.935:ENSG00000185215"
## [29,] "3.694:ENSG00000109971" "6.814:ENSG00000165168"
## [30,] "3.673:ENSG00000115232" "6.438:ENSG00000101347"
## [31,] "3.66:ENSG00000082074"  "6.419:ENSG00000123146"
## [32,] "3.648:ENSG00000081059" "6.332:ENSG00000140575"
## [33,] "3.647:ENSG00000128340" "6.105:ENSG00000073756"
## [34,] "3.632:ENSG00000100345" "6.033:ENSG00000119535"
## [35,] "3.575:ENSG00000116741" "6.022:ENSG00000075426"
## [36,] "3.487:ENSG00000143546" "5.996:ENSG00000185650"
## [37,] "3.417:ENSG00000110934" "5.83:ENSG00000163131" 
## [38,] "3.403:ENSG00000197043" "5.774:ENSG00000000938"
## [39,] "3.067:ENSG00000073756" "5.706:ENSG00000179094"
## [40,] "3.027:ENSG00000110324" "5.689:ENSG00000136167"
## 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] "ENSG00000038427" "ENSG00000075624" "ENSG00000100201" "ENSG00000090382"
## [5] "ENSG00000136167" "ENSG00000134954"
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", transform="log2",
                                batch="svaseq", filter=TRUE))
## 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,] "129.2:ENSG00000090382" "17.88:ENSG00000118503"
##  [2,] "70.72:ENSG00000198804" "15.81:ENSG00000157514"
##  [3,] "45.8:ENSG00000198938"  "12.64:ENSG00000100316"
##  [4,] "41.14:ENSG00000198886" "12.42:ENSG00000156508"
##  [5,] "34.72:ENSG00000198899" "11.39:ENSG00000137154"
##  [6,] "34.57:ENSG00000198763" "10.67:ENSG00000167658"
##  [7,] "31.24:ENSG00000163220" "10.56:ENSG00000149273"
##  [8,] "30.82:ENSG00000121966" "10.54:ENSG00000112306"
##  [9,] "30.06:ENSG00000198888" "9.841:ENSG00000142541"
## [10,] "29.13:ENSG00000265972" "9.482:ENSG00000164587"
## [11,] "23.4:ENSG00000118503"  "8.087:ENSG00000167526"
## [12,] "18.5:ENSG00000143384"  "8.003:ENSG00000142676"
## [13,] "18.38:ENSG00000157514" "7.707:ENSG00000174444"
## [14,] "17.07:ENSG00000038427" "7.462:ENSG00000121966"
## [15,] "15.59:ENSG00000129824" "7.026:ENSG00000107742"
## [16,] "15.07:ENSG00000067048" "6.987:ENSG00000108298"
## [17,] "14.72:ENSG00000152518" "6.907:ENSG00000142937"
## [18,] "13.8:ENSG00000026025"  "6.747:ENSG00000144713"
## [19,] "12.73:ENSG00000135046" "6.595:ENSG00000197756"
## [20,] "11.83:ENSG00000119535" "6.592:ENSG00000177600"
## [21,] "11.55:ENSG00000198712" "6.522:ENSG00000063177"
## [22,] "10.14:ENSG00000012817" "6.215:ENSG00000142534"
## [23,] "9.721:ENSG00000163131" "6.169:ENSG00000105372"
## [24,] "9.051:ENSG00000133112" "6.064:ENSG00000161016"
## [25,] "8.792:ENSG00000128016" "5.733:ENSG00000156482"
## [26,] "8.441:ENSG00000124942" "5.663:ENSG00000067082"
## [27,] "8.416:ENSG00000087086" "5.503:ENSG00000147403"
## [28,] "8.258:ENSG00000139318" "5.463:ENSG00000071082"
## [29,] "7.632:ENSG00000173821" "5.251:ENSG00000177954"
## [30,] "7.494:ENSG00000118515" "5.157:ENSG00000145592"
## [31,] "7.092:ENSG00000137154" "5.014:ENSG00000105193"
## [32,] "7.019:ENSG00000197629" "4.78:ENSG00000166441" 
## [33,] "6.754:ENSG00000142102" "4.77:ENSG00000213741" 
## [34,] "6.363:ENSG00000179344" "4.697:ENSG00000087074"
## [35,] "6.332:ENSG00000170776" "4.639:ENSG00000109475"
## [36,] "6.28:ENSG00000206503"  "4.639:ENSG00000198034"
## [37,] "6.249:ENSG00000170345" "4.62:ENSG00000008988" 
## [38,] "6.144:ENSG00000136167" "4.529:ENSG00000137818"
## [39,] "6.088:ENSG00000134954" "4.402:ENSG00000168685"
## [40,] "6.085:ENSG00000173193" "4.376:ENSG00000171858"
## 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] "ENSG00000090382" "ENSG00000198804" "ENSG00000198938" "ENSG00000198886"
## [5] "ENSG00000198899" "ENSG00000198763"
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 = "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 = "donor_tables.rda")
hs_q2 <- subset_expt(hs_valid, subset="donor!='d1010'&donor!='d1011'")
## Using a subset expression.
## There were 64, now there are 58 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
1034n1 1034n1 Neutrophil d1034 1 #D95F02 1034n1 -0.1732 -0.0652 -0.1732 -0.0652 -0.1739 -0.0278 -0.1152 -0.0301 0.3016 -0.1039 0.0725 -0.0426 0.0010 -0.0902 0.0592 -0.2204 0.2706 -0.0473 0.0435 -0.1543 0.0304 -0.0518 0.1216 -0.0918 0.1039 0.1429 -0.3789 0.1433 0.1095 0.4072 -0.3134 -0.1268 -0.0691 -0.2514 0.0893 0.0546 0.0820 -0.0505 -0.1490 -0.0167 -0.0018 0.0013 0.0360 0.0609 0.0135 0.0380 -0.0341 -0.0107 -0.0032 -0.0009 -0.0023 -0.0005 -0.0061 -0.0015 -0.0019 -0.0129 -0.0038 -0.0013 -0.0035
1034bp1 1034bp1 Biopsy d1034 1 #E7298A 1034bp1 0.1762 -0.1888 0.1762 -0.1888 -0.0237 0.0923 0.0544 0.2045 0.1656 0.0443 -0.0950 0.1514 0.0101 -0.0174 0.0537 0.0705 0.0156 0.0877 -0.1220 -0.0184 -0.0904 0.1402 0.0894 -0.0356 -0.0363 0.0116 0.1017 0.0136 0.2713 -0.3070 -0.2344 -0.1409 0.5649 -0.2757 0.0181 0.1077 0.1672 0.0282 0.1369 -0.1128 0.0022 0.0826 0.0070 0.0102 0.0115 -0.0100 0.0244 -0.0216 0.0336 -0.0192 0.0100 -0.0148 0.0079 -0.0049 -0.0103 0.0036 -0.0125 0.0040 0.0078
1034n2 1034n2 Neutrophil d1034 1 #D95F02 1034n2 -0.1732 -0.0685 -0.1732 -0.0685 -0.2007 -0.0245 -0.1192 0.0139 0.3133 -0.1842 0.0757 -0.0046 0.0074 -0.0383 0.0289 -0.3516 0.2551 -0.2494 0.0248 -0.1618 -0.0104 0.0983 -0.1980 -0.0745 0.0042 -0.0901 0.2311 0.0598 0.0529 -0.2845 0.4046 -0.1543 0.0201 0.2216 -0.1433 0.0372 -0.0302 0.0686 0.0313 0.0255 0.0019 0.0445 0.0334 0.0202 -0.0252 -0.0065 0.0239 0.0184 0.0085 -0.0096 0.0073 -0.0034 -0.0009 -0.0041 -0.0003 0.0036 -0.0038 0.0007 -0.0008
1034m2 1034m2 Monocyte d1034 1 #7570B3 1034m2 -0.0026 -0.0366 -0.0026 -0.0366 0.0441 -0.2625 0.0345 -0.1574 0.3522 0.0227 0.1218 0.0299 -0.0334 0.0069 0.0977 0.2175 0.1543 0.1613 -0.1777 0.1187 0.1068 -0.0182 0.0026 0.0956 0.0062 -0.0235 0.0678 -0.1457 -0.0447 -0.0719 -0.0973 0.1860 -0.0625 0.0892 -0.0301 0.0015 0.0090 -0.0322 -0.0595 0.0061 -0.0307 -0.0548 -0.0146 0.0376 0.0280 -0.0501 0.0526 0.3455 0.4693 0.2443 -0.1891 0.1586 -0.0377 0.0226 0.0252 0.0139 0.0118 -0.0047 0.0054
1034m2- 1034m2- Monocyte d1034 1 #7570B3 1034m2- -0.0087 -0.0382 -0.0087 -0.0382 0.0512 -0.2531 0.0278 -0.1560 0.3592 0.0446 0.1269 0.0225 -0.1167 0.0546 0.0606 0.1766 0.1334 0.1533 -0.1852 0.1693 0.1120 -0.0542 0.0080 0.0241 0.0278 -0.0524 0.0950 -0.1207 -0.0364 -0.0170 -0.0096 0.1541 -0.0555 0.0287 0.0524 -0.0063 -0.0108 -0.0109 0.0685 -0.0418 0.0310 -0.0374 -0.0131 -0.0432 -0.0642 -0.0354 -0.0716 -0.3326 -0.5125 -0.2223 0.1560 -0.2012 0.0279 -0.0204 -0.0023 -0.0130 0.0017 -0.0138 -0.0073
2050bp1 2050bp1 Biopsy d2050 2 #E7298A 2050bp1 0.1642 -0.1684 0.1642 -0.1684 -0.0071 0.0628 0.0352 0.0689 -0.1245 -0.3370 0.2811 -0.3254 -0.2397 0.1066 -0.1360 0.3616 0.1130 -0.2199 0.0587 -0.1000 0.0364 0.0699 -0.0581 -0.0689 -0.0748 -0.1800 -0.1509 -0.1414 -0.0269 -0.0859 -0.0986 -0.1156 -0.0400 -0.0942 0.0173 0.2229 -0.3200 0.0218 0.0601 0.0503 0.0117 -0.0404 -0.0516 0.0441 -0.0107 0.0704 0.0228 -0.0147 0.0195 -0.0023 -0.0008 -0.0019 0.0134 -0.0195 0.0232 -0.0363 -0.0050 -0.0031 0.0067
2052bp1 2052bp1 Biopsy d2052 3 #E7298A 2052bp1 0.1733 -0.1944 0.1733 -0.1944 -0.0204 0.1107 0.0626 0.2373 0.1161 0.0895 -0.1535 0.2038 0.0128 0.0056 0.0178 0.1007 -0.0271 -0.0242 -0.0335 -0.0968 -0.1946 0.1301 0.0140 -0.0178 0.2467 -0.3689 -0.1855 0.0261 -0.2049 -0.1566 0.0063 -0.0670 -0.5152 -0.0793 -0.0546 -0.1367 0.2697 -0.0480 0.0254 -0.0429 0.0219 -0.0220 0.0114 0.0141 0.0027 -0.0347 0.0155 -0.0097 -0.0059 -0.0085 -0.0111 -0.0099 -0.0158 -0.0034 -0.0039 -0.0078 0.0074 0.0093 -0.0001
2052e1 2052e1 Eosinophil d2052 3 #66A61E 2052e1 -0.0956 0.1103 -0.0956 0.1103 0.1745 0.2235 0.1420 -0.0572 -0.0363 0.0127 0.0271 0.0100 0.3013 -0.1252 0.1920 0.1848 0.1446 -0.2729 0.0388 0.1760 0.0726 0.2438 -0.1947 -0.2125 0.2942 0.2643 -0.2621 0.0166 -0.0244 -0.1357 0.0671 0.3190 0.0911 -0.0165 -0.0525 0.0049 -0.0106 0.0980 -0.0218 -0.0836 0.0751 -0.0236 -0.0245 -0.0162 -0.0808 0.0044 -0.0301 -0.0325 -0.0416 0.0180 -0.0081 0.0575 0.0227 0.0068 0.0111 0.0198 -0.0170 -0.0245 -0.0061
2052n2 2052n2 Neutrophil d2052 3 #D95F02 2052n2 -0.1354 0.0861 -0.1354 0.0861 -0.2232 0.1496 0.1831 -0.1019 -0.0400 0.1529 -0.0883 -0.0630 0.0647 0.6311 0.3107 0.0527 -0.0694 -0.3422 -0.3212 -0.0395 -0.0090 -0.1798 0.1552 0.0247 -0.0411 -0.0114 0.0220 -0.0141 0.0366 0.0228 -0.0196 -0.0873 -0.0006 0.0266 -0.0083 0.0046 0.0174 -0.0028 -0.0451 0.0400 -0.0057 -0.0020 0.0085 -0.0024 0.0244 -0.0183 -0.0160 0.0065 0.0104 -0.0010 -0.0037 -0.0044 -0.0057 -0.0003 0.0044 0.0035 0.0046 0.0002 0.0029
2065bp1 2065bp1 Biopsy d2065 4 #E7298A 2065bp1 0.1781 -0.1922 0.1781 -0.1922 -0.0426 0.0959 0.0790 0.3802 0.1598 0.1509 -0.1483 0.0717 -0.0232 0.0271 -0.0006 0.0476 -0.0438 -0.1456 0.3489 0.0064 0.5252 -0.3238 -0.0814 0.1286 0.1151 -0.0074 0.0714 0.0396 -0.0119 0.1699 0.0174 0.0991 0.1068 0.1991 0.0460 0.0565 -0.0652 -0.0248 -0.0469 -0.0041 0.0039 -0.0101 0.0104 -0.0202 -0.0269 0.0075 0.0053 0.0083 0.0021 0.0163 0.0108 0.0027 0.0004 0.0031 0.0072 -0.0032 -0.0081 -0.0011 -0.0022
2066bp1 2066bp1 Biopsy d2066 5 #E7298A 2066bp1 0.1731 -0.1954 0.1731 -0.1954 -0.0385 0.1033 0.0456 0.0986 0.0949 0.0993 -0.1617 0.1681 0.0758 -0.0617 0.0649 -0.0497 0.0004 0.0730 -0.1384 0.0370 -0.1938 0.0607 -0.1091 -0.2162 -0.4334 0.3161 -0.1170 -0.2396 0.0042 0.1334 0.1984 -0.0075 -0.1706 0.1325 0.3312 0.2016 -0.1816 -0.0801 0.0577 0.1159 0.0033 -0.0219 -0.0321 -0.0077 0.0285 0.0492 -0.0020 0.0030 0.0040 0.0084 -0.0018 0.0291 -0.0115 -0.0097 -0.0016 0.0027 0.0081 -0.0106 -0.0016
2068m1 2068m1 Monocyte d2068 6 #7570B3 2068m1 0.0018 -0.0496 0.0018 -0.0496 0.0854 -0.2698 0.1071 0.1350 -0.2366 -0.0503 -0.1160 -0.0342 0.0627 0.1020 0.0342 -0.2312 0.2202 -0.0199 0.1336 0.0369 0.0894 0.1987 0.2635 -0.0212 -0.0605 -0.0644 -0.1086 -0.2195 0.1468 -0.1908 -0.0460 0.1622 0.0294 0.0653 -0.0105 -0.1889 -0.0560 -0.3804 -0.2167 0.1319 -0.2559 -0.0405 0.1106 0.0391 0.0602 -0.0308 0.0936 -0.0619 -0.0371 -0.0511 0.0781 0.0433 0.0373 0.1367 -0.0221 -0.0664 -0.0233 0.0026 0.0117
2068n1 2068n1 Neutrophil d2068 6 #D95F02 2068n1 -0.1653 -0.0725 -0.1653 -0.0725 -0.1361 -0.0365 -0.0734 0.1103 -0.2210 0.0024 -0.0494 -0.0091 0.0160 -0.1549 -0.0471 0.0903 0.1200 0.1056 -0.1939 0.0812 0.1610 -0.0418 0.2283 -0.1532 -0.0802 0.0066 -0.0946 0.3095 0.0890 -0.0825 -0.1090 -0.0199 -0.1200 0.3104 -0.1355 0.1308 0.0143 0.1364 0.2113 -0.0536 -0.0917 -0.1048 -0.1039 -0.4658 0.1729 -0.0661 0.0148 -0.1128 0.0681 0.0070 -0.0470 -0.0119 0.0000 0.0364 0.0066 -0.0101 0.0013 -0.0076 -0.0065
2068e1 2068e1 Eosinophil d2068 6 #66A61E 2068e1 -0.1251 -0.0549 -0.1251 -0.0549 0.2402 0.0559 -0.1248 0.0511 -0.1172 0.0485 -0.0999 -0.0498 -0.0627 0.0772 -0.0572 -0.1008 0.1229 0.1167 -0.1589 -0.1393 0.2578 0.0939 0.1064 -0.0965 -0.0210 0.0141 0.1325 0.0215 -0.1875 -0.1197 -0.0241 -0.0813 -0.0566 -0.0565 0.2807 -0.1394 -0.0942 0.0997 -0.2613 -0.1587 0.2853 0.2331 -0.2966 -0.0053 -0.1607 -0.0300 -0.1660 0.0079 0.1553 -0.0273 0.1881 0.0170 0.1508 -0.0940 -0.0172 0.0336 0.0031 0.0043 0.0069
2068bp1 2068bp1 Biopsy d2068 6 #E7298A 2068bp1 0.1695 -0.1829 0.1695 -0.1829 -0.0222 0.0745 -0.0026 -0.2783 -0.1798 -0.2938 0.0341 -0.0065 -0.0384 0.0316 -0.0356 -0.0173 -0.0044 -0.0174 -0.0370 0.0194 -0.0053 -0.0119 -0.1250 -0.1874 0.0764 -0.1439 0.0078 -0.1895 0.0352 0.2836 -0.0040 -0.0009 0.2023 0.3741 0.1755 -0.1713 0.4681 -0.0495 0.0101 -0.1988 0.0218 0.0811 -0.0070 -0.0432 0.0174 -0.0247 0.0371 -0.0069 0.0201 0.0199 0.0006 0.0019 -0.0195 -0.0076 -0.0260 0.0093 -0.0170 0.0008 -0.0166
2072n1 2072n1 Neutrophil d2072 8 #D95F02 2072n1 -0.1727 -0.0670 -0.1727 -0.0670 -0.1501 -0.0338 -0.1051 -0.0662 -0.1421 0.1806 -0.0384 0.0312 -0.0411 -0.2156 -0.0213 0.2129 0.0458 -0.0083 0.0928 0.0129 -0.0253 -0.1262 0.3460 -0.1152 0.0575 0.0717 -0.0100 -0.1460 -0.1327 -0.0975 0.0434 -0.1545 0.0576 0.1771 0.0274 -0.0509 -0.0080 0.1686 0.1012 0.0041 0.0632 -0.0006 0.3879 0.4853 -0.0539 0.0397 -0.1682 -0.1066 0.0337 0.0601 -0.0016 0.0287 -0.0046 -0.0144 0.0117 0.0041 0.0047 -0.0061 0.0078
2072e1 2072e1 Eosinophil d2072 8 #66A61E 2072e1 -0.1071 -0.0611 -0.1071 -0.0611 0.2041 0.0342 -0.1324 -0.0882 -0.0707 0.2913 -0.1837 0.0596 -0.6336 0.1340 -0.2685 -0.1781 0.0791 -0.1591 -0.0757 0.1535 -0.0936 0.0053 -0.2120 -0.0253 0.1297 0.1138 -0.1209 0.0301 0.0157 -0.0174 -0.0220 0.1133 0.0638 -0.0170 -0.0908 0.0849 0.0573 -0.0204 0.0817 0.0605 -0.0566 -0.1082 -0.0110 0.0703 0.1030 0.0062 0.0189 0.0447 0.0559 -0.0097 -0.0071 -0.0664 -0.0013 0.0006 0.0013 0.0037 -0.0023 0.0000 -0.0110
2072bp1 2072bp1 Biopsy d2072 8 #E7298A 2072bp1 0.1683 -0.1774 0.1683 -0.1774 -0.0366 0.0650 -0.0178 -0.3460 -0.1186 -0.1229 0.0744 -0.0689 0.0035 0.0033 0.0168 -0.0590 -0.0415 -0.0122 0.1358 0.0203 0.1688 -0.1203 0.1390 0.2208 -0.0205 0.2783 0.0650 0.2784 -0.0039 -0.3052 0.1707 0.0945 -0.2099 -0.2417 0.2449 0.1897 0.2728 -0.1774 0.1116 0.0515 0.0040 -0.0300 0.0608 0.0280 -0.0016 -0.0654 0.0219 0.0154 0.0163 -0.0215 0.0172 0.0033 -0.0095 0.0062 0.0073 0.0113 0.0148 -0.0077 0.0126
2071bp1 2071bp1 Biopsy d2071 7 #E7298A 2071bp1 0.1588 -0.1814 0.1588 -0.1814 -0.0312 0.0722 -0.0236 -0.4326 -0.0847 -0.0890 -0.2399 0.2592 0.1192 -0.0666 0.0751 -0.1642 -0.0792 0.0914 -0.0361 -0.0274 -0.0399 0.0214 0.0313 0.0626 0.3171 -0.2412 0.0552 0.0628 0.0465 0.0342 -0.1553 0.0304 0.0477 0.0627 -0.0357 0.1870 -0.4883 0.0799 -0.0789 0.1494 -0.0004 -0.0204 -0.0263 0.0178 0.0171 -0.0141 -0.0452 0.0133 -0.0311 -0.0154 -0.0042 -0.0027 0.0019 0.0027 -0.0193 0.0175 0.0115 -0.0050 0.0021
2073m1 2073m1 Monocyte d2073 9 #7570B3 2073m1 0.0162 0.1191 0.0162 0.1191 0.0391 -0.0986 0.3085 -0.0591 -0.0423 0.0549 -0.0411 0.0040 -0.0384 -0.2096 -0.1181 -0.1005 -0.0560 -0.1105 -0.1295 -0.2965 0.1135 0.0049 0.0903 0.0568 -0.0284 0.0122 -0.0372 -0.0726 0.0150 -0.0402 0.0091 0.0244 0.0154 0.0005 0.0096 -0.0470 -0.0052 -0.0397 0.0164 -0.0914 -0.0919 -0.0592 -0.0672 -0.0106 -0.0858 0.1716 0.0867 -0.0193 -0.0889 -0.0035 -0.3395 -0.0830 -0.1737 -0.5905 0.0795 0.1764 0.0850 -0.0175 0.0052
2073n1 2073n1 Neutrophil d2073 9 #D95F02 2073n1 -0.1310 0.0947 -0.1310 0.0947 -0.1839 0.1349 0.2066 -0.0449 -0.0406 0.0410 -0.0423 -0.0200 -0.0858 0.0682 -0.0399 0.0672 0.1146 0.3159 0.1446 -0.0676 0.0120 0.0439 -0.0167 -0.0362 -0.0078 0.0073 -0.0557 0.0618 -0.0014 -0.0373 -0.0197 0.0150 -0.0117 0.0731 0.0182 -0.0414 -0.0220 0.0681 0.1209 -0.0220 -0.0231 0.1195 0.0425 0.0161 -0.0357 0.1287 0.2401 0.6201 -0.3304 -0.1206 0.1772 0.0527 0.1450 -0.0230 -0.0156 -0.0169 -0.0107 0.0233 -0.0040
2073e1 2073e1 Eosinophil d2073 9 #66A61E 2073e1 -0.0894 0.1058 -0.0894 0.1058 0.1825 0.2105 0.1554 0.0209 0.0195 -0.0892 0.0064 0.0552 -0.0720 -0.1109 -0.0543 -0.1074 0.0889 -0.0819 0.0040 0.2536 -0.0291 -0.0688 0.1528 0.0493 -0.1384 -0.1084 0.1241 0.0529 -0.0431 -0.0101 -0.0720 -0.1194 -0.0400 -0.0004 0.1106 -0.0184 -0.0373 0.0181 -0.0716 -0.0968 0.1029 0.0357 -0.0789 0.0383 -0.1346 0.0459 0.0499 0.0683 -0.2046 0.0334 -0.3791 0.0002 -0.4138 0.4364 0.0397 -0.0911 -0.0333 0.0038 0.0221
2073bp1 2073bp1 Biopsy d2073 9 #E7298A 2073bp1 0.1592 -0.1766 0.1592 -0.1766 -0.0372 0.0531 0.0030 -0.1695 0.0037 -0.1734 -0.1636 0.0709 -0.0403 -0.0144 0.0087 0.0260 -0.0367 0.0204 0.0106 0.0528 0.1571 -0.1311 -0.1038 -0.0906 -0.3568 0.1975 -0.0519 -0.0191 -0.0696 -0.0261 -0.0170 -0.0572 -0.0500 -0.2211 -0.5403 -0.4268 -0.0274 0.1840 -0.1293 0.0020 -0.0300 0.0055 0.0188 -0.0085 -0.0031 0.0136 -0.0574 -0.0063 -0.0386 -0.0036 -0.0182 -0.0084 0.0232 0.0227 -0.0013 0.0139 0.0036 0.0072 0.0012
2068m2 2068m2 Monocyte d2068 6 #7570B3 2068m2 -0.0045 -0.0436 -0.0045 -0.0436 0.0766 -0.2755 0.1129 0.1425 -0.0380 -0.1599 0.0631 0.0203 0.0474 0.1611 0.0356 -0.1273 -0.1191 0.1090 0.0158 0.1692 -0.1148 -0.1219 0.0073 -0.1796 0.1015 0.0986 -0.0708 0.1693 -0.3036 0.0327 -0.0045 -0.0312 0.1003 0.0480 -0.2489 0.1438 -0.0393 -0.3180 0.1861 0.0838 0.3214 0.3549 0.0209 0.0363 0.0061 0.0985 -0.0461 -0.0048 0.0334 -0.0960 -0.1275 0.0649 -0.0196 -0.1085 0.0130 0.0058 0.0164 0.0080 0.0032
2068n2 2068n2 Neutrophil d2068 6 #D95F02 2068n2 -0.1757 -0.0735 -0.1757 -0.0735 -0.1694 -0.0309 -0.0802 0.1808 -0.0712 -0.1863 0.1034 0.0500 0.0289 -0.0436 -0.0382 -0.1362 -0.2962 0.0266 -0.2992 0.2505 0.1713 0.1421 -0.1763 0.4944 0.0645 0.0471 -0.3208 -0.0980 0.0145 -0.0581 0.0171 -0.0985 0.0410 0.0273 0.1577 -0.1887 -0.0793 0.1012 0.0903 0.0244 0.0264 0.0471 0.0764 0.0692 0.0129 -0.0052 0.0264 0.0199 -0.0211 0.0026 0.0106 -0.0016 0.0096 0.0027 0.0005 -0.0039 -0.0014 -0.0009 -0.0028
2068e2 2068e2 Eosinophil d2068 6 #66A61E 2068e2 -0.1229 -0.0559 -0.1229 -0.0559 0.2477 0.0561 -0.1005 0.2041 0.0062 -0.2655 0.0603 -0.0144 0.0938 0.1751 0.0456 -0.1582 -0.1851 0.2332 -0.0966 -0.1226 -0.0079 -0.1800 0.1075 -0.1922 0.1022 0.0000 0.0052 -0.0495 -0.1620 0.0404 0.1536 0.1553 0.0819 -0.1099 -0.0201 0.1077 0.0232 0.1644 0.0911 -0.0936 -0.3325 -0.4138 -0.0066 0.1246 -0.1313 0.0004 -0.0718 0.1208 0.0113 -0.0581 0.0053 -0.0815 -0.0520 0.0362 -0.0157 0.0001 -0.0202 -0.0136 -0.0002
2072m2 2072m2 Monocyte d2072 8 #7570B3 2072m2 -0.0136 -0.0439 -0.0136 -0.0439 0.0737 -0.2714 0.0784 -0.0387 0.0301 0.0375 0.0426 0.0231 -0.0098 0.0595 0.0135 0.0928 -0.1778 -0.0257 0.1406 0.0858 -0.1401 -0.0104 -0.0830 -0.0499 0.0293 0.0377 -0.0636 0.2230 -0.0711 -0.1128 0.0444 -0.2163 0.0577 0.0526 0.1441 0.0258 0.0241 0.1130 -0.3002 -0.0364 -0.3115 0.0912 -0.0565 -0.1121 0.0753 0.3230 -0.1253 -0.0084 -0.1469 0.4661 0.1860 -0.0452 -0.0764 0.0162 -0.0099 0.0690 0.0008 -0.0115 -0.0059
2072n2 2072n2 Neutrophil d2072 8 #D95F02 2072n2 -0.1823 -0.0676 -0.1823 -0.0676 -0.1833 -0.0267 -0.1201 -0.0585 -0.0099 0.0805 0.0563 0.0625 -0.0157 -0.1357 -0.0097 0.0557 -0.2060 -0.0970 0.1597 0.0281 -0.0807 -0.0022 0.1319 0.0209 0.0950 0.0912 0.0571 -0.3594 -0.2307 -0.0353 0.0839 -0.1490 0.1225 -0.1334 -0.0880 0.1095 0.0003 -0.1998 -0.2507 -0.1852 0.2174 -0.2822 -0.1305 -0.3252 0.1794 -0.0654 0.1083 0.1362 -0.0610 -0.0985 0.0212 -0.0100 0.0174 0.0263 0.0005 0.0115 0.0155 0.0044 0.0045
2072e2 2072e2 Eosinophil d2072 8 #66A61E 2072e2 -0.1365 -0.0551 -0.1365 -0.0551 0.2266 0.0690 -0.1506 -0.0747 0.1045 0.0635 0.0244 -0.0345 -0.0638 0.0486 -0.0273 0.0756 -0.2212 0.0308 0.1077 -0.1378 -0.1028 -0.0510 0.0626 0.0381 -0.0499 -0.0511 -0.1725 -0.0851 0.2546 -0.0216 0.0805 0.0324 -0.0238 0.0453 -0.0540 0.0380 -0.0363 -0.0288 -0.0603 0.0173 -0.0104 0.2575 0.3705 -0.3994 -0.4685 -0.1582 -0.0383 0.0102 0.0259 -0.0094 -0.0584 0.0732 -0.0117 -0.0177 0.0126 -0.0077 -0.0053 -0.0245 0.0030
2073m2 2073m2 Monocyte d2073 9 #7570B3 2073m2 0.0163 0.1175 0.0163 0.1175 0.0288 -0.0949 0.3189 -0.0041 0.0368 -0.0361 -0.0120 -0.0300 -0.0152 -0.1782 -0.1489 -0.0470 -0.1909 -0.0704 -0.1799 -0.2607 -0.0175 -0.0717 -0.0529 -0.0667 -0.0446 0.0647 -0.0110 0.1097 -0.0588 0.0097 -0.0805 0.0603 0.0128 0.0554 -0.0567 0.0476 -0.0440 -0.0362 -0.0295 -0.0411 0.1792 -0.0545 0.1093 0.1136 -0.0394 -0.3636 0.3121 -0.0705 0.0627 0.2392 0.3786 -0.1816 -0.0454 0.1902 0.0449 -0.1224 -0.0854 0.0121 -0.0009
2073n2 2073n2 Neutrophil d2073 9 #D95F02 2073n2 -0.1356 0.0939 -0.1356 0.0939 -0.2185 0.1421 0.1960 -0.0260 0.0407 -0.0253 -0.0259 -0.0553 -0.0608 0.2016 -0.0695 -0.0777 -0.0534 0.2857 0.2205 -0.0763 -0.0066 0.2303 -0.2206 0.0085 -0.1119 -0.0365 0.0963 0.0672 -0.0756 -0.0499 -0.1437 0.1891 -0.0338 -0.0065 0.0853 -0.0110 -0.0827 -0.0332 -0.0416 -0.2673 0.1174 -0.1541 0.3302 -0.0788 0.1621 0.1114 -0.2448 -0.2523 0.1734 0.0055 -0.1136 -0.0499 -0.0574 0.0253 0.0014 0.0027 0.0048 -0.0068 -0.0017
2073e2 2073e2 Eosinophil d2073 9 #66A61E 2073e2 -0.0919 0.1074 -0.0919 0.1074 0.1771 0.2161 0.1510 0.0068 0.1245 -0.1371 0.0415 0.0529 -0.0518 -0.0736 -0.0369 -0.1103 -0.0126 -0.0593 0.0572 0.2871 -0.1149 -0.1201 0.1381 0.0733 -0.1334 -0.1616 0.0824 0.0334 0.0359 0.0633 -0.0234 -0.0337 -0.0395 -0.0176 -0.0152 0.0159 -0.0102 -0.0199 0.0429 -0.0671 0.0006 -0.1404 0.0141 0.0300 0.0586 -0.0232 0.0969 -0.1804 -0.0246 0.1519 0.1650 0.5049 0.3777 -0.2483 -0.0780 0.0705 0.0450 0.0440 -0.0123
2068m3 2068m3 Monocyte d2068 6 #7570B3 2068m3 -0.0174 -0.0463 -0.0174 -0.0463 0.0674 -0.2701 0.0766 -0.0464 -0.0984 0.1065 -0.0583 -0.0047 0.0788 0.0542 0.0293 0.0381 0.0167 -0.0576 0.1081 0.1048 0.0842 0.1897 -0.0504 -0.0819 0.0004 -0.1173 0.0848 0.1189 0.2612 0.2399 0.1663 -0.2412 -0.0176 -0.1843 0.0507 -0.2051 -0.0763 -0.1040 0.3600 0.0006 0.2369 -0.3604 -0.0202 -0.0413 -0.1995 -0.0316 -0.1721 0.1462 0.0247 0.1390 0.0600 -0.0431 -0.0196 0.0081 0.0389 0.0336 0.0004 -0.0094 0.0032
2068n3 2068n3 Neutrophil d2068 6 #D95F02 2068n3 -0.1804 -0.0687 -0.1804 -0.0687 -0.1715 -0.0214 -0.0929 0.0887 -0.1441 0.0129 -0.0048 0.0216 0.0495 -0.1027 -0.0738 0.1115 -0.2101 -0.0463 -0.1408 0.1445 0.1351 0.2426 -0.1176 -0.0974 -0.1199 -0.0992 0.2995 0.0602 0.1766 0.1691 -0.0809 0.1471 -0.1668 -0.0736 -0.2035 0.3430 0.1986 -0.1116 -0.2729 0.0439 -0.0721 0.0667 -0.0190 0.2446 -0.2075 0.0948 -0.0206 0.0458 -0.0614 -0.0399 0.0227 0.0205 0.0111 -0.0447 -0.0050 0.0189 -0.0042 0.0106 0.0029
2068e3 2068e3 Eosinophil d2068 6 #66A61E 2068e3 -0.1365 -0.0487 -0.1365 -0.0487 0.2448 0.0714 -0.1386 -0.0020 -0.0123 0.0459 -0.0009 -0.0500 0.0815 0.0499 0.0128 0.1004 -0.0349 0.1017 -0.0791 -0.1443 0.2108 0.1002 -0.0348 -0.1238 0.1143 -0.0282 0.1918 -0.0655 0.0340 0.1679 0.1644 -0.0604 -0.0394 -0.2358 0.0476 -0.0630 -0.0181 0.0523 0.0712 0.1910 0.0158 0.1885 0.2323 -0.0469 0.4785 0.0554 0.3445 -0.1122 -0.1040 0.1497 -0.1264 0.0799 -0.0845 0.0148 0.0279 -0.0266 0.0253 0.0204 0.0040
2072m3 2072m3 Monocyte d2072 8 #7570B3 2072m3 -0.0179 -0.0438 -0.0179 -0.0438 0.0579 -0.2706 0.0761 -0.0426 0.0445 0.0710 -0.0192 -0.0514 0.0459 0.0445 -0.0058 0.1115 -0.1558 -0.0632 0.1593 0.0737 -0.1111 0.0497 -0.1050 -0.0881 -0.0583 -0.0012 -0.0117 0.2714 0.0020 0.0153 0.0041 -0.1375 0.0290 0.0443 0.2047 -0.0833 -0.0302 0.2549 -0.1882 -0.0164 -0.1057 -0.0625 -0.0222 0.1145 0.0338 -0.2239 0.2745 -0.0543 0.1382 -0.5181 -0.2360 0.0749 0.1229 -0.0109 -0.0501 -0.0436 -0.0096 0.0260 -0.0079
2072n3 2072n3 Neutrophil d2072 8 #D95F02 2072n3 -0.1843 -0.0697 -0.1843 -0.0697 -0.2022 -0.0213 -0.1140 -0.0240 0.0881 0.0064 -0.0087 -0.0319 0.0111 -0.0549 -0.0340 -0.0396 -0.1934 -0.2320 0.2028 -0.0295 -0.1265 0.0634 0.0747 -0.0928 -0.0860 -0.0382 0.1777 -0.0530 -0.2122 0.0159 -0.2517 0.3489 0.0283 -0.1084 0.1680 -0.2538 -0.0483 0.0447 0.3577 0.2006 -0.1753 0.1858 -0.2518 -0.0790 -0.0749 -0.0552 0.0109 -0.0263 0.0188 0.0978 0.0118 -0.0091 -0.0185 0.0097 0.0046 -0.0183 -0.0117 -0.0065 -0.0012
2072e3 2072e3 Eosinophil d2072 8 #66A61E 2072e3 -0.1316 -0.0537 -0.1316 -0.0537 0.2331 0.0608 -0.1458 -0.0535 0.1310 0.0082 0.0147 -0.0636 0.0915 0.0336 0.0487 0.0956 -0.2160 0.0275 0.1681 -0.2112 -0.1076 -0.0121 0.0982 0.1621 -0.1563 -0.0251 -0.1672 0.0507 0.3516 -0.0674 0.0020 0.1368 -0.0447 0.2521 -0.1420 -0.0456 0.0175 -0.0793 0.0459 -0.0869 0.2235 -0.0144 -0.3399 0.2738 0.2863 0.1398 -0.0741 0.0101 -0.0303 -0.0354 0.0213 -0.0999 -0.0274 0.0410 0.0080 -0.0105 0.0028 0.0217 0.0014
2159bp1 2159bp1 Biopsy d2159 10 #E7298A 2159bp1 0.1862 -0.1877 0.1862 -0.1877 -0.0181 0.0991 0.0496 0.1313 -0.0033 0.0233 0.1579 -0.0954 -0.1084 0.0208 -0.0261 0.0746 0.1125 0.0001 -0.0918 -0.0654 -0.2721 0.3162 0.3075 0.3120 0.1163 0.2564 0.2050 0.2221 -0.0900 0.3334 0.2009 0.1438 0.0861 0.1061 -0.1408 -0.1474 -0.0877 0.0496 -0.1076 -0.0220 -0.0305 0.0007 0.0207 -0.0439 -0.0373 0.0025 -0.0134 -0.0048 -0.0099 0.0078 0.0023 -0.0022 0.0078 0.0020 0.0089 -0.0026 -0.0067 0.0050 -0.0108
2073m3 2073m3 Monocyte d2073 9 #7570B3 2073m3 0.0117 0.1232 0.0117 0.1232 0.0448 -0.0983 0.3170 -0.0340 -0.0016 0.0241 0.0530 0.0386 -0.0597 -0.2101 -0.1116 0.0060 -0.1047 -0.0903 -0.1432 -0.2746 0.0593 -0.0598 -0.0290 0.0398 0.0961 0.0249 0.0425 -0.1161 0.0467 0.0595 0.0857 -0.0300 -0.0061 -0.0738 -0.0068 0.0314 0.0992 0.0328 0.0802 0.1453 -0.1010 0.0940 -0.0383 -0.1000 0.1297 0.1724 -0.4211 0.0326 0.0581 -0.2426 -0.0123 0.2307 0.2243 0.3750 -0.1231 -0.0570 -0.0013 -0.0056 -0.0029
2073n3 2073n3 Neutrophil d2073 9 #D95F02 2073n3 -0.1324 0.0991 -0.1324 0.0991 -0.1863 0.1367 0.1856 -0.0240 -0.0605 0.0229 0.0404 0.0442 -0.1028 0.0765 0.0029 0.0870 0.0691 0.3923 0.1850 -0.0151 0.0042 -0.0049 -0.0587 0.0324 0.1548 0.0618 -0.0852 -0.1294 0.0884 0.0245 0.2025 -0.1663 0.0440 -0.0348 -0.0958 0.0292 0.0806 -0.0136 -0.0328 0.2834 -0.0950 0.0456 -0.4131 0.0795 -0.1727 -0.2137 0.0641 -0.3176 0.1071 0.1202 -0.0737 0.0268 -0.0798 0.0124 0.0027 0.0092 0.0046 -0.0116 0.0059
2073e3 2073e3 Eosinophil d2073 9 #66A61E 2073e3 -0.0984 0.1119 -0.0984 0.1119 0.1787 0.2235 0.1452 -0.0068 0.0523 -0.0774 0.0728 0.0524 0.0571 -0.1334 0.0334 0.0384 0.0628 -0.0421 0.0851 0.2554 -0.0301 -0.0209 0.0585 0.0535 -0.0519 -0.0819 0.0895 -0.0361 -0.0096 0.0822 -0.0010 -0.1596 -0.0316 -0.0131 -0.0052 -0.0097 0.0216 -0.0633 -0.0129 0.1847 -0.1595 0.1286 0.0976 -0.0629 0.1673 -0.0347 -0.1335 0.1220 0.2904 -0.1955 0.2095 -0.5639 -0.0066 -0.1914 0.0319 0.0041 -0.0003 -0.0269 -0.0048
2162m1 2162m1 Monocyte d2162 11 #7570B3 2162m1 0.0053 -0.0464 0.0053 -0.0464 0.0792 -0.2706 0.0935 0.0582 -0.2036 0.0197 -0.0828 -0.0183 0.1394 0.0882 0.0552 -0.1699 0.2197 -0.0421 0.1962 0.0123 -0.1410 -0.0389 -0.0329 0.2799 -0.0738 -0.0068 0.0336 -0.2845 0.0205 0.0984 -0.0505 0.0926 -0.1199 -0.1123 -0.1011 0.2973 0.1552 0.5044 0.0860 -0.1346 0.1176 0.1413 -0.0094 -0.0478 0.0425 -0.0633 0.0220 -0.0087 0.0005 0.0491 0.0578 -0.0376 -0.0457 -0.0276 0.0125 -0.0036 0.0036 0.0036 0.0003
2162n1 2162n1 Neutrophil d2162 11 #D95F02 2162n1 -0.1551 -0.0752 -0.1551 -0.0752 -0.1245 -0.0431 -0.0529 0.1173 -0.2299 -0.0306 -0.0392 0.0536 0.0571 -0.1486 0.0019 0.1905 0.2561 0.0285 -0.1347 -0.0415 -0.2982 -0.4785 -0.2401 0.0882 -0.0052 -0.1571 0.0345 0.1310 0.1266 0.0033 0.1853 0.2534 0.0899 -0.2160 0.1268 -0.1655 -0.1166 -0.1651 -0.1233 -0.0951 0.0340 0.0404 0.0063 -0.0130 0.0222 0.0456 0.0040 -0.0135 0.0353 -0.0149 -0.0078 -0.0234 -0.0106 -0.0252 -0.0129 0.0023 -0.0012 0.0049 -0.0025
2162e1 2162e1 Eosinophil d2162 11 #66A61E 2162e1 -0.1244 -0.0542 -0.1244 -0.0542 0.2357 0.0626 -0.1266 -0.0346 -0.0916 0.0801 -0.0563 -0.0513 0.2054 -0.0275 0.0971 0.1002 0.1876 0.0163 -0.0112 -0.2331 -0.0561 0.0133 -0.3255 0.2950 -0.1026 0.1131 0.1218 0.0353 -0.2954 -0.0029 -0.3365 -0.2688 0.0544 0.1733 -0.0671 0.0306 0.0858 -0.2260 0.0980 0.1220 -0.1360 -0.1671 0.0335 0.0018 -0.1277 -0.0202 0.0030 -0.0439 -0.0976 -0.0157 -0.0202 0.0755 0.0420 0.0208 -0.0218 0.0068 0.0009 -0.0063 -0.0036
2162bp1 2162bp1 Biopsy d2162 11 #E7298A 2162bp1 0.1779 -0.1948 0.1779 -0.1948 -0.0342 0.1390 0.0428 -0.0297 -0.0533 0.4917 0.4540 -0.3747 0.2069 -0.0755 -0.0612 -0.3288 -0.0459 0.1192 -0.0690 0.1245 -0.0348 -0.1435 -0.0967 -0.1260 0.0318 -0.1115 -0.0064 -0.0508 0.0510 -0.0786 -0.0933 -0.0141 -0.0205 -0.0025 -0.0813 -0.0852 -0.0321 0.0343 -0.0397 0.0306 -0.0031 -0.0200 -0.0174 0.0142 0.0081 0.0090 -0.0176 0.0192 -0.0108 0.0068 -0.0060 0.0056 0.0006 0.0040 0.0091 -0.0008 0.0034 -0.0012 0.0006
macrofagos Macrofagos Macrophage unknown 12 #E6AB02 Macrofagos 0.1323 0.1849 0.1323 0.1849 -0.0069 -0.0252 -0.0942 0.1296 -0.0780 -0.1075 0.0693 0.0271 -0.1846 -0.0698 0.3001 -0.0687 -0.0627 0.0336 0.0209 -0.0274 -0.0871 -0.0769 -0.0178 -0.0546 0.0965 0.1017 0.1333 -0.0354 0.1214 -0.0835 -0.1647 0.0429 -0.0801 0.0793 0.0695 -0.0910 0.0938 0.1388 -0.0914 0.3029 0.2538 -0.1861 0.0561 -0.0951 -0.1506 0.3338 0.1921 -0.0663 0.0505 0.0309 -0.0843 -0.1764 0.2671 0.0793 0.1693 0.0137 0.1427 0.0898 0.1352
macrofagos+sbv Macrofagos+SbV Macrophage unknown 12 #E6AB02 Mcrfgs+SbV 0.1246 0.1910 0.1246 0.1910 -0.0122 -0.0174 -0.1228 0.0891 -0.0912 -0.0250 0.2206 0.2401 -0.0039 0.0857 -0.0362 -0.0019 0.0047 -0.0361 0.0411 -0.0452 -0.0057 -0.0319 -0.0182 -0.0600 0.0555 0.1499 0.1010 -0.0333 0.1208 -0.0458 -0.1093 -0.0372 -0.1265 0.0202 0.0125 -0.0386 -0.0806 -0.0312 0.0065 -0.0824 -0.0101 0.0412 0.0162 0.0399 0.0373 -0.2007 -0.0538 0.0656 -0.1032 0.1419 -0.1982 -0.1485 0.1351 -0.0282 -0.4241 0.0461 -0.0173 0.1099 -0.5621
macrofagos+10772 Macrofagos+10772 Macrophage unknown 12 #E6AB02 Mcrf+10772 0.1287 0.1845 0.1287 0.1845 -0.0286 -0.0179 -0.1087 0.0431 0.0117 -0.0368 -0.1053 -0.1609 -0.0967 -0.0763 0.1624 0.0059 -0.0550 0.0301 -0.0086 0.0428 -0.0389 -0.0347 -0.0344 -0.0076 0.1243 0.1055 0.1070 -0.0424 0.0964 -0.0394 -0.0686 -0.0477 -0.0942 0.0212 -0.0071 -0.0734 -0.0165 0.0227 0.0159 0.0232 0.0242 -0.0509 -0.0043 -0.0493 -0.0122 0.1567 0.0217 -0.0195 0.1000 -0.1692 0.2783 0.2537 -0.4810 -0.2060 -0.3112 -0.3133 -0.2286 -0.1297 -0.0627
macrofagos+10772+sbv Macrofagos+10772+SbV Macrophage unknown 12 #E6AB02 M+10772+SV 0.1220 0.1913 0.1220 0.1913 -0.0205 -0.0132 -0.1234 0.0941 -0.0359 -0.0412 0.1644 0.1765 0.0288 0.1001 -0.1429 0.0266 0.0241 -0.0289 0.0231 -0.0349 -0.0326 -0.0219 -0.0240 -0.0488 0.0095 0.0694 0.0469 0.0156 0.0732 -0.0196 -0.1021 0.0113 -0.0481 0.0424 0.0204 -0.0511 -0.0156 0.0261 -0.0414 0.0784 0.0367 -0.0046 0.0395 0.0190 0.1140 -0.2256 -0.0839 0.0750 -0.0804 -0.0568 0.0777 0.1080 -0.1674 0.0225 -0.0711 0.5431 0.0012 -0.3445 0.4602
macrofagos+2169 Macrofagos+2169 Macrophage unknown 12 #E6AB02 Mcrfg+2169 0.1233 0.1765 0.1233 0.1765 -0.0475 -0.0091 -0.0924 0.0704 0.0809 -0.0969 -0.2807 -0.3484 0.0366 -0.0406 -0.0688 0.0309 -0.0364 0.0308 -0.0323 0.0854 -0.0842 -0.0397 -0.0412 -0.0216 0.1531 0.1071 0.0762 -0.0585 0.0825 -0.0289 -0.0374 -0.0545 -0.0621 0.0234 0.0203 -0.0128 -0.0293 -0.0421 -0.0081 -0.0426 -0.0890 0.0508 -0.0023 0.0105 0.0800 -0.2097 -0.1723 0.0631 -0.0637 -0.0437 0.0580 0.1036 -0.1276 0.0214 0.3396 0.2200 0.2643 0.4774 -0.1253
macrofagos+2169+sbv Macrofagos+2169+SbV Macrophage unknown 12 #E6AB02 Mc+2169+SV 0.1162 0.1862 0.1162 0.1862 -0.0425 0.0003 -0.1314 -0.0119 0.0579 -0.0077 -0.0351 0.0510 0.1831 0.1080 -0.3328 0.0292 0.0631 0.0021 -0.0179 0.0056 -0.0136 -0.0024 0.0588 0.0280 -0.0920 -0.1071 -0.0847 0.0479 -0.0774 -0.0214 0.0396 0.0682 0.1341 0.0005 0.0386 0.0159 0.1315 0.0889 -0.1204 0.3108 0.1494 -0.1225 0.0153 -0.0493 0.0219 0.0600 -0.0703 0.0213 -0.0422 -0.0219 -0.0044 0.0789 0.0128 -0.0587 0.4101 -0.0523 -0.3353 -0.2184 -0.3897
macrofagos+12309 Macrofagos+12309 Macrophage unknown 12 #E6AB02 Mcrf+12309 0.1277 0.1869 0.1277 0.1869 -0.0192 -0.0094 -0.1151 0.0260 -0.0647 0.0614 0.0051 -0.0684 -0.1767 -0.1354 0.3551 -0.0416 -0.0326 0.0242 0.0115 -0.0435 0.0540 0.0919 -0.0248 0.0084 -0.1812 -0.2174 -0.1283 0.1142 -0.1436 0.0496 0.1023 0.0697 0.1548 -0.0324 0.0034 0.0671 0.0471 0.0064 -0.0472 0.0269 -0.0057 0.0247 0.0038 0.0408 0.0960 -0.2421 -0.1466 0.0811 -0.1045 0.0586 -0.0253 0.0425 -0.0148 -0.0315 0.0372 -0.2037 0.4921 -0.3411 -0.1053
macrofagos+12309+sbv Macrofagos+12309+SbV Macrophage unknown 12 #E6AB02 M+12309+SV 0.1243 0.1901 0.1243 0.1901 -0.0190 -0.0094 -0.1385 -0.0020 -0.0689 0.0384 0.1741 0.2570 0.0295 0.0745 -0.0551 -0.0011 0.0086 -0.0285 0.0268 -0.0294 0.0172 -0.0045 -0.0402 -0.0190 0.0212 0.0747 0.0382 -0.0053 0.0449 0.0109 -0.0134 -0.0546 -0.0562 -0.0016 -0.0068 -0.0095 -0.0644 -0.0506 0.0322 -0.1943 -0.1452 0.0700 -0.0388 0.0307 0.0582 -0.1341 -0.1302 0.0328 -0.0662 0.0638 -0.0949 0.0258 0.1109 -0.1498 0.2946 -0.5455 -0.1641 0.1922 0.4052
macrofagos+12367+sbv Macrofagos+12367+SbV Macrophage unknown 12 #E6AB02 M+12367+SV 0.1206 0.1911 0.1206 0.1911 -0.0289 -0.0048 -0.1552 -0.0971 -0.0276 0.1224 0.1479 0.2442 0.0721 0.0703 -0.0704 0.0025 0.0349 -0.0318 0.0155 -0.0208 0.0737 0.0177 0.0247 0.0336 -0.0315 -0.0196 -0.0463 -0.0115 -0.0402 0.0703 0.0537 -0.0504 -0.0240 -0.0443 -0.0727 0.0057 -0.1036 -0.0785 0.1647 -0.3127 -0.1683 0.1006 -0.0421 0.0300 -0.1400 0.3138 0.2890 -0.1562 0.2392 -0.1718 0.2286 0.0441 -0.0804 0.2039 0.1918 0.1189 0.2684 -0.0428 -0.1686
macrofagos+1126 Macrofagos+1126 Macrophage unknown 12 #E6AB02 Mcrfg+1126 0.1254 0.1878 0.1254 0.1878 -0.0220 -0.0086 -0.1267 -0.0386 -0.0490 0.1129 0.0222 -0.0389 -0.1465 -0.1287 0.3366 -0.0386 -0.0101 0.0236 0.0065 -0.0416 0.0882 0.0883 0.0150 0.0444 -0.1819 -0.2289 -0.1593 0.0720 -0.1733 0.0713 0.1397 0.0558 0.1250 -0.0584 -0.0208 0.0852 0.0111 -0.0216 0.0375 -0.0261 -0.0301 0.0441 0.0058 0.0076 0.0109 -0.0075 0.0152 -0.0250 0.0214 -0.0046 -0.0135 -0.0236 0.0087 0.0584 -0.0908 0.3100 -0.5101 0.3781 0.0643
macrofagos+12251 Macrofagos+12251 Macrophage unknown 12 #E6AB02 Mcrf+12251 0.1242 0.1759 0.1242 0.1759 -0.0555 -0.0039 -0.1083 -0.0094 0.0911 -0.0215 -0.3345 -0.3700 0.0667 -0.0548 -0.0485 -0.0029 -0.0378 0.0351 -0.0365 0.1067 -0.0446 -0.0271 0.0162 0.0347 0.1174 0.0462 0.0391 -0.0723 0.0209 0.0254 0.0593 -0.0867 -0.0712 -0.0282 -0.0339 0.0373 -0.0639 -0.1052 0.0857 -0.2488 -0.1160 0.1093 -0.0469 0.0449 -0.0378 0.0522 0.0906 -0.0388 0.0083 0.1164 -0.2020 -0.1902 0.3499 0.1025 -0.0658 0.0164 -0.1975 -0.4336 0.0908
macrofagos+12251+sbv Macrofagos+12251+SbV Macrophage unknown 12 #E6AB02 M+12251+SV 0.1153 0.1846 0.1153 0.1846 -0.0549 0.0054 -0.1355 -0.0568 0.1012 0.0337 -0.1230 -0.0661 0.2360 0.0939 -0.3743 0.0483 0.0774 -0.0134 -0.0374 0.0423 0.0201 0.0263 0.0735 0.0429 -0.0830 -0.1056 -0.1358 0.0369 -0.1151 0.0165 0.1009 0.0648 0.1471 -0.0118 -0.0011 0.0469 0.0842 0.0390 -0.0371 0.1688 0.1021 -0.0772 0.0107 -0.0407 -0.0778 0.1195 0.0439 -0.0139 0.0255 0.0540 -0.0220 -0.1121 -0.0023 -0.0051 -0.4753 -0.1500 0.2873 0.2732 0.2536 
write.csv(q2_pca$table, file="q2_pca_coords.csv")
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 d6114393775c44346befc5916efed02675b9bd7f
## This is hpgltools commit: Fri Nov 6 15:01:37 2020 -0500: d6114393775c44346befc5916efed02675b9bd7f
## Saving to 01_annotation_v202011.rda.xz
tmp <- loadme(filename=savefile)
---
title: "L. panamensis 202011: 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}
if (!isTRUE(get0("skip_load"))) {
  library(hpgltools)
  tt <- sm(devtools::load_all("/data/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 <- "202011"
  previous_file <- paste0("01_annotation_v", ver, ".Rmd")
  rundate <- format(Sys.Date(), format="%Y%m%d")

  tmp <- try(sm(loadme(filename=gsub(pattern="\\.Rmd", replace="\\.rda\\.xz", x=previous_file))))
  rmd_file <- paste0("01_annotation_v", ver, ".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")]
```

# 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

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

### Salmon expressionsets

Currently, I have these disabled.

```{r all_new_salmon, eval=FALSE}
hs_expt <- sm(create_expt("sample_sheets/tmrc3_samples_20201105.xlsx",
                          file_column="hg3891salmonfile",
                          gene_info=hs_annot, tx_gene_map=tx_gene_map))

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

```{r salmon_write, fig.show="hide", eval=FALSE}
hs_write <- write_expt(hs_expt, excel=glue("excel/hs_written_salmon-v{ver}.xlsx"))

hs_valid <- subset_expt(hs_expt, coverage=100000)
valid_write <- write_expt(hs_valid, excel=glue("excel/hs_valid_salmon-v{ver}.xlsx"))
```

### 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("sample_sheets/tmrc3_samples_20201105.xlsx",
                       file_column="hg3891hisatfile", savefile="hs_expt_all.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 <- sm(normalize_expt(hs_valid, transform="log2", convert="cpm", batch="svaseq",
                           filter=TRUE))
all_pca <- plot_pca(all_norm)
pp(file="tmrc3_pca_nolabels.png", image=all_pca$plot)
all_ts <- plot_tsne(all_norm)
all_ts$plot

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

# 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.


## 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")
save_result <- save(mono, file = "monocyte_expt.rda")
mono_norm <- sm(normalize_expt(mono, transform="log2", convert="cpm", batch="svaseq", filter=TRUE))
plot_pca(mono_norm)$plot
mono_de <- sm(all_pairwise(mono, model_batch="svaseq", filter=TRUE))
keepers <- list(
  "fail_vs_cure" = c("failure", "cure"))
mono_tables <- combine_de_tables(mono_de, keepers=keepers,
                                 excel=glue::glue("excel/mono_clinical_de-v{ver}.xlsx"))
mono_sig <- extract_significant_genes(mono_tables, according_to="deseq",
                                      excel=glue::glue("excel/mono_clinical_sig-v{ver}.xlsx"))
mono_pct_sig <- extract_significant_genes(mono_tables, top_percent=20, according_to="deseq",
                                          excel=glue::glue("excel/mono_clinical_sig_pct-v{ver}.xlsx"))
mono_sig$summary_df
```

## 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")
save_result <- save(neut, file = "neutrophil_expt.rda")
neut_norm <- sm(normalize_expt(neut, transform="log2", convert="cpm", batch="svaseq", filter=TRUE))
plot_pca(neut_norm)$plot
neut_de <- sm(all_pairwise(neut, model_batch="svaseq", filter=TRUE))
neut_tables <- combine_de_tables(neut_de, keepers=keepers,
                                 excel=glue::glue("excel/neut_clinical_de-v{ver}.xlsx"))
neut_sig <- extract_significant_genes(neut_tables, according_to="deseq",
                                      excel=glue::glue("excel/neut_clinical_sig-v{ver}.xlsx"))
neut_pct_sig <- extract_significant_genes(neut_tables, top_percent=20, according_to="deseq",
                                          excel=glue::glue("excel/neut_clinical_sig_pct-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")
save_result <- save(eo, file = "eosinophil_expt.rda")
eo_norm <- sm(normalize_expt(eo, transform="log2", convert="cpm", batch="svaseq", filter=TRUE))
plot_pca(eo_norm)$plot
eo_de <- sm(all_pairwise(eo, model_batch="svaseq", filter=TRUE))
eo_tables <- combine_de_tables(eo_de, keepers=keepers,
                               excel=glue::glue("excel/eo_clinical_de-v{ver}.xlsx"))
eo_sig <- extract_significant_genes(eo_tables, according_to="deseq",
                                    excel=glue::glue("excel/eo_clinical_sig-v{ver}.xlsx"))
eo_pct_sig <- extract_significant_genes(eo_tables, top_percent=20, according_to="deseq",
                                        excel=glue::glue("excel/eo_clinical_sig_pct-v{ver}.xlsx"))
```

## Biopsies

```{r biopsies}
biop <- subset_expt(hs_valid, subset="typeofcells=='Biopsy'")
biop <- set_expt_conditions(biop, fact="visitnumber")
biop <- set_expt_batches(biop, fact="donor")
save_result <- save(biop, file = "biopsy_expt.rda")
biop_norm <- sm(normalize_expt(biop, transform="log2", convert="cpm", batch="svaseq", filter=TRUE))
plot_pca(biop_norm)$plot
biop_de <- sm(all_pairwise(biop, model_batch="svaseq", filter=TRUE))
biop_tables <- combine_de_tables(biop_de, keepers=keepers,
                                 excel=glue::glue("excel/biop_clinical_de-v{ver}.xlsx"))
biop_sig <- extract_significant_genes(biop_tables, according_to="deseq",
                                      excel=glue::glue("excel/biop_clinical_sig-v{ver}.xlsx"))
biop_sig_pct <- extract_significant_genes(biop_tables, according_to="deseq", top_percent=20,
                                          excel=glue::glue("excel/biop_clinical_sig_pct-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_expt, subset="typeofcells=='Macrophages'")
macr <- set_expt_conditions(macr, fact="zymodeme")
macr <- set_expt_batches(macr, fact="macrdrug")

macr_norm <- normalize_expt(macr, transform="log2", norm="quant", convert="cpm", filter=TRUE)
norm_pca <- plot_pca(macr_norm)
pp(file="macrophage_side_experiment/norm_pca.png", image=norm_pca$plot)

macr_nb <- normalize_expt(macr, batch="svaseq", filter=TRUE)
macr_nb <- normalize_expt(macr_nb, norm="quant", convert="cpm", transform="log2")
nb_pca <- plot_pca(macr_nb)$plot
pp(file="macrophage_side_experiment/normbatch_pca.png", image=norm_pca$plot)

macr_written <- write_expt(macr, excel="macrophage_side_experiment/macrophage_expt.xlsx")

zymo_de <- all_pairwise(macr, model_batch="svaseq", filter=TRUE)
zymo_table <- combine_de_tables(zymo_de, excel="macrophage_side_experiment/macrophage_de.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 = "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="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, transform="log2",
                               batch="svaseq", filter=TRUE))
## 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", transform="log2",
                                batch="svaseq", filter=TRUE))
## 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 = "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 = "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="q2_pca_coords.csv")
```

```{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)
```
