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_20191001.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_20200915.xlsx",
                       file_column="hg3891hisatfile",
                       gene_info=hs_annot)
## Reading the sample metadata.
## Dropped 129 rows from the sample metadata because they were blank.
## There are 1 duplicate rows in the sample ID column.
## The sample definitions comprises: 69 rows(samples) and 75 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/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/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 58243 annotations and counts.
## Bringing together the count matrix and gene information.
## Some annotations were lost in merging, setting them to 'undefined'.
## Saving the expressionset to 'expt.rda'.
## The final expressionset has 58302 rows and 69 columns.
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 2599328 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 69, now there are 63 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 <- write_expt(hs_valid, excel=glue("excel/hs_valid-v{ver}.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: The shape palette can deal with a maximum of 6 discrete values because
## more than 6 becomes difficult to discriminate; you have 14. Consider
## specifying shapes manually if you must have them.
## Warning: Removed 50 rows containing missing values (geom_dotplot).
## Warning: The shape palette can deal with a maximum of 6 discrete values because
## more than 6 becomes difficult to discriminate; you have 14. Consider
## specifying shapes manually if you must have them.
## Warning: Removed 50 rows containing missing values (geom_dotplot).
## Warning: The shape palette can deal with a maximum of 6 discrete values because
## more than 6 becomes difficult to discriminate; you have 14. Consider
## specifying shapes manually if you must have them.
## Warning: Removed 50 rows containing missing values (geom_dotplot).
## Warning: The shape palette can deal with a maximum of 6 discrete values because
## more than 6 becomes difficult to discriminate; you have 14. Consider
## specifying shapes manually if you must have them.
## Warning: Removed 50 rows containing missing values (geom_dotplot).
## Warning in MASS::cov.trob(data[, vars]): Probable convergence failure

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

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

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

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

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

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

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

## Warning 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
## Attempting mixed linear model with: ~  (1|condition) + (1|batch)
## Fitting the expressionset to the model, this is slow.
## Dividing work into 100 chunks...
## 
## Total:183 s
## Error in `.rowNamesDF<-`(x, value = value) : invalid 'row.names' length
## A couple of common errors:
## An error like 'vtv downdated' may be because there are too many 0s, filter the data and rerun.
## An error like 'number of levels of each grouping factor must be < number of observations' means
## that the factor used is not appropriate for the analysis - it really only works for factors
## which are shared among multiple samples.
## Retrying with only condition in the model.
## Loading required package: Matrix
## 
## Total:113 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: The shape palette can deal with a maximum of 6 discrete values because
## more than 6 becomes difficult to discriminate; you have 14. Consider
## specifying shapes manually if you must have them.
## Warning: Removed 50 rows containing missing values (geom_dotplot).
## Warning: The shape palette can deal with a maximum of 6 discrete values because
## more than 6 becomes difficult to discriminate; you have 14. Consider
## specifying shapes manually if you must have them.
## Warning: Removed 50 rows containing missing values (geom_dotplot).
## Warning: The shape palette can deal with a maximum of 6 discrete values because
## more than 6 becomes difficult to discriminate; you have 14. Consider
## specifying shapes manually if you must have them.
## Warning: Removed 50 rows containing missing values (geom_dotplot).
## Warning: The shape palette can deal with a maximum of 6 discrete values because
## more than 6 becomes difficult to discriminate; you have 14. Consider
## specifying shapes manually if you must have them.
## Warning: Removed 50 rows containing missing values (geom_dotplot).
## Attempting mixed linear model with: ~  (1|condition) + (1|batch)
## Fitting the expressionset to the model, this is slow.
## Dividing work into 100 chunks...
## 
## Total:242 s
## Placing factor: condition at the beginning of the model.
## Writing the median reads by factor.

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 <- normalize_expt(hs_valid, transform="log2", convert="cpm", batch="svaseq",
                           filter=TRUE)
## 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$best_libsize
## Leaving the data unnormalized.  This is necessary for DESeq, but
##  EdgeR/limma might benefit from normalization.  Good choices include quantile,
##  size-factor, tmm, etc.
## Step 1: performing count filter with option: cbcb
## Removing 39597 low-count genes (18705 remaining).
## Step 2: not normalizing the data.
## Step 3: converting the data with cpm.
## Step 4: transforming the data with log2.
## transform_counts: Found 183897 values equal to 0, adding 1 to the matrix.
## Step 5: doing batch correction with svaseq.
## Using the current state of normalization.
## Passing the data to all_adjusters using the svaseq estimate type.
## batch_counts: Before batch/surrogate estimation, 776401 entries are x>1: 66%.
## batch_counts: Before batch/surrogate estimation, 183897 entries are x==0: 16%.
## batch_counts: Before batch/surrogate estimation, 218117 entries are 0<x<1: 19%.
## The be method chose 10 surrogate variables.
## Attempting svaseq estimation with 10 surrogates.
## There are 53917 (5%) elements which are < 0 after batch correction.
## Setting low elements to zero.
all_pca <- plot_pca(all_norm)
## Potentially check over the experimental design, there appear to be missing values.
## Warning in plot_pca(all_norm): There are NA values in the component data. This
## can lead to weird plotting errors.
all_pca$plot
## Warning: Removed 1 rows containing missing values (geom_point).

all_ts <- plot_tsne(all_norm)
## Potentially check over the experimental design, there appear to be missing values.
## Warning in plot_pca(..., pc_method = "tsne"): There are NA values in the
## component data. This can lead to weird plotting errors.
all_ts$plot
## Warning: Removed 1 rows containing missing values (geom_point).

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
1010-2 1010-2 PBMCs d1010 1 #1B9E77 1010-2 -0.0815 -0.1544 -0.0815 -0.1544 -0.0748 0.1318 0.0973 -0.1087 0.2378 -0.0297 0.1310 -0.0522 0.0540 -0.1060 0.0624 -0.0768 -0.1116 -0.1929 -0.1450 0.5646 -0.1153 0.2196 -0.2106 0.0649 -0.0340 0.1597 -0.1476 0.1088 -0.0248 -0.1447 0.0277 0.0542 -0.0393 -0.0756 0.2347 0.0794 0.0163 0.1063 0.0041 0.1753 -0.0230 -0.1109 -0.0247 -0.1946 -0.0339 -0.0049 0.1652 0.0262 0.1242 0.0111 0.0608 0.0298 0.0242 0.0092 0.0500 0.0416 0.0327 0.0546 0.0012 0.0115 0.0108 0.0137 0.0461
1010-7 1010-7 PBMCs d1010 1 #1B9E77 1010-7 -0.0441 -0.1633 -0.0441 -0.1633 -0.0469 0.0388 0.0836 -0.0282 0.3063 -0.1100 0.1234 0.0401 -0.0323 0.0198 0.0235 -0.0628 0.0502 -0.0457 -0.0967 -0.0969 -0.0231 0.0309 0.1133 -0.2092 -0.2037 -0.0133 -0.1168 0.1333 -0.1249 0.0228 -0.1067 -0.0450 0.1901 0.0299 -0.0900 -0.1474 -0.0474 -0.0946 0.0277 0.0015 -0.0175 -0.1107 0.1529 0.0055 0.0340 0.0127 -0.1667 0.1469 -0.0591 0.2241 -0.4415 -0.2394 -0.0264 0.3245 -0.0368 -0.0585 -0.1405 -0.1406 0.0078 0.0911 -0.0345 -0.0038 -0.0043
1010-12 1010-12 PBMCs d1010 1 #1B9E77 1010-12 0.0029 -0.2256 0.0029 -0.2256 -0.0603 0.0572 -0.0338 -0.1090 0.3648 -0.0841 0.1395 0.0539 0.0816 -0.1512 -0.0264 -0.1028 0.2509 -0.0196 -0.0858 -0.0845 0.0098 0.0152 -0.0903 -0.0290 0.1014 -0.0102 0.1939 0.0650 0.1107 -0.0501 0.0442 0.0457 0.1061 0.2105 -0.0500 -0.3319 0.0461 -0.1387 -0.0205 -0.2138 0.1897 0.0746 0.0016 0.1041 -0.1439 -0.0449 -0.0255 0.0250 -0.1918 0.0096 0.3141 0.0485 0.0337 -0.2313 -0.0637 -0.0282 0.1150 0.0835 -0.0567 -0.1330 0.0246 0.0062 -0.0227
1011-2 1011-2 PBMCs d1011 2 #1B9E77 1011-2 0.0020 -0.0908 0.0020 -0.0908 -0.0849 0.0228 -0.0335 -0.0322 0.0943 -0.1369 -0.0010 0.0560 -0.0700 0.0058 -0.0324 -0.0293 -0.0223 0.0347 0.1653 0.1179 0.0488 0.0090 -0.0271 0.1260 -0.0218 0.0250 0.0723 -0.0187 0.0940 0.0991 0.0766 -0.0611 -0.0995 -0.1969 0.1620 0.1418 0.1783 0.1603 -0.0245 -0.0794 -0.0417 0.2942 0.0333 0.1742 0.2405 -0.0722 -0.0588 -0.0697 -0.2417 -0.1732 -0.0496 -0.2371 -0.0126 -0.0191 -0.2320 -0.2596 0.0532 -0.1025 -0.3387 0.2793 -0.0133 -0.0413 -0.0644
1011-7 1011-7 PBMCs d1011 2 #1B9E77 1011-7 -0.0111 -0.0791 -0.0111 -0.0791 -0.1119 0.0423 -0.0377 -0.0270 0.1733 -0.1302 0.0401 0.1277 -0.0694 0.0118 0.0238 -0.0272 0.0558 0.0867 0.1598 -0.0096 0.0377 -0.0473 0.0665 0.0994 -0.0361 -0.1164 0.0437 0.0032 0.1836 0.1424 0.0578 0.0217 -0.1053 -0.0073 0.0848 0.1268 -0.0099 0.1419 -0.0318 0.0428 -0.0982 0.0626 0.0156 0.0040 0.0558 0.0554 0.1457 -0.0131 -0.0714 -0.2134 -0.0528 -0.0356 -0.0745 0.0602 -0.0728 0.1595 -0.3595 -0.1153 0.3925 -0.5116 0.0439 0.0778 -0.0383
1011-12 1011-12 PBMCs d1011 2 #1B9E77 1011-12 -0.0148 -0.1066 -0.0148 -0.1066 -0.1148 0.0404 -0.0657 -0.0397 0.2810 -0.1550 0.1014 0.2349 -0.1175 -0.0154 0.0904 -0.0664 0.0917 0.1146 0.1462 -0.2523 0.0697 -0.0846 0.0714 0.0417 -0.0792 -0.1975 0.0444 0.0321 -0.0412 0.1714 0.0189 -0.0390 -0.1346 -0.0300 -0.0683 0.1717 -0.1532 0.1099 0.0167 0.2316 -0.0860 -0.1536 -0.1487 -0.0225 -0.0483 0.0161 0.1267 -0.0692 0.2237 0.0480 0.0366 0.2874 0.0312 -0.0440 0.1922 0.0182 0.1478 0.1172 -0.0942 0.3204 -0.0485 -0.0478 0.0472
1034n1 1034n1 Neutrophils d1034 3 #D95F02 1034n1 -0.3559 0.1381 -0.3559 0.1381 0.3661 0.0420 -0.0376 -0.2519 -0.0075 0.0226 0.0326 0.2228 -0.0091 -0.1689 -0.3867 0.0183 -0.0095 -0.1266 0.3581 0.0288 0.1035 -0.0737 -0.1673 -0.2038 0.0955 0.1673 -0.1792 0.0019 -0.0586 0.1088 0.0419 0.0357 0.1554 -0.0015 -0.0213 0.0136 -0.0939 -0.0841 -0.0491 0.0145 -0.0433 0.0331 -0.1430 0.0730 -0.0357 0.0588 0.0417 0.0147 -0.0048 -0.0434 -0.0686 0.0134 0.0055 -0.0442 0.0211 -0.0164 -0.0039 -0.0301 0.0079 -0.0182 -0.0039 -0.0005 -0.0023
1034bp1 1034bp1 Biopsy NA NA #E7298A 1034bp1 -0.0069 -0.0068 -0.0069 -0.0068 -0.1001 -0.0395 0.0378 -0.0693 -0.1596 0.0624 -0.0425 0.0190 -0.0223 0.0344 0.0900 0.0470 0.1525 0.1454 0.0506 0.0066 -0.0059 0.0090 0.0307 0.0378 0.0495 -0.1124 -0.2335 -0.0847 0.0800 -0.0484 -0.0873 0.1307 -0.0910 0.0975 0.1167 -0.2775 0.0533 -0.1311 -0.1333 0.1015 0.1721 -0.1719 -0.1208 -0.0169 0.2324 -0.0402 0.3098 0.2899 -0.1393 -0.2031 -0.1177 -0.0221 0.1465 -0.0358 0.3357 -0.1265 -0.0167 -0.1872 -0.1516 0.0022 0.0819 -0.0638 -0.0355
1034n2 1034n2 Neutrophils d1034 3 #D95F02 1034n2 -0.3230 0.1389 -0.3230 0.1389 0.3137 -0.1296 -0.0489 -0.3239 0.1652 0.2292 -0.4298 -0.0600 -0.0581 0.0832 0.1443 -0.3582 0.0380 0.0682 -0.1046 -0.0538 -0.0728 0.0576 0.1238 0.2180 -0.0310 -0.2034 0.0686 0.1079 -0.0082 -0.0998 0.0171 -0.0299 -0.0781 -0.0094 0.0508 -0.0004 0.0823 0.0721 0.0509 -0.0566 0.0078 -0.0237 0.0912 -0.0622 0.0301 0.0011 -0.0309 0.0233 0.0088 0.0613 0.0325 -0.0247 -0.0084 0.0219 -0.0186 0.0147 -0.0154 0.0217 -0.0072 0.0097 0.0072 -0.0010 -0.0003
1034m2 1034m2 Monocytes d1034 3 #7570B3 1034m2 -0.1326 -0.0061 -0.1326 -0.0061 0.0821 -0.0059 0.1444 0.0127 -0.1540 -0.2156 -0.0756 0.0146 0.0306 0.0829 0.2125 0.0504 0.0310 0.0450 -0.1632 0.0524 -0.0445 0.1358 0.0133 0.0163 -0.0958 0.0547 0.2520 -0.0411 -0.0509 0.0725 -0.0846 0.0366 0.3124 -0.2324 0.0430 0.0531 0.0364 0.0887 0.1443 -0.0903 0.1645 0.0291 -0.4025 0.2590 -0.3319 -0.0048 0.0595 -0.0222 -0.0624 -0.0719 -0.1460 0.1354 -0.0168 -0.0117 0.1297 -0.0337 -0.1477 -0.0647 -0.0248 -0.0018 0.0025 0.0275 -0.0087
1034m2- 1034m2- Monocytes d1034 3 #7570B3 1034m2- -0.2257 0.0210 -0.2257 0.0210 0.2731 0.1140 0.3021 -0.1255 -0.3034 -0.4326 0.2012 0.1919 0.1697 -0.1159 0.1996 0.1538 -0.0664 -0.0156 -0.1980 -0.0919 -0.0964 -0.0639 0.0225 0.0874 -0.0475 -0.0668 0.0356 -0.0782 0.0119 -0.0006 0.0114 -0.1121 -0.2374 0.1260 -0.0666 -0.0701 0.0041 0.0152 -0.0640 0.0088 -0.0602 -0.0169 0.1963 -0.0860 0.1083 -0.0362 -0.0238 -0.0205 0.0207 -0.0005 0.0952 -0.0730 0.0197 0.0072 -0.0784 0.0054 0.0458 0.0370 0.0036 -0.0092 -0.0085 -0.0032 0.0154
2050bp1 2050bp1 Biopsy d2050 4 #E7298A 2050bp1 -0.0728 -0.0178 -0.0728 -0.0178 -0.1274 -0.0117 -0.0031 0.0045 -0.0382 0.0118 -0.0519 0.0986 -0.0613 0.1342 0.1271 -0.0577 -0.0185 0.1349 -0.0169 0.0844 -0.1152 0.1626 -0.0462 -0.1063 0.1009 -0.0665 -0.1843 -0.1178 -0.1681 0.0928 -0.1357 0.0714 0.1119 -0.1095 -0.2015 -0.0734 -0.1522 -0.0475 -0.1352 0.0691 -0.0452 0.1427 -0.1341 -0.0107 0.1530 -0.1671 -0.1585 0.1456 -0.0937 -0.0998 0.2580 -0.1842 -0.2365 -0.1245 -0.0641 0.1399 -0.1041 0.2714 0.2918 0.2513 -0.0013 0.0150 -0.0215
2052bp1 2052bp1 Biopsy d2052 5 #E7298A 2052bp1 0.0413 -0.0787 0.0413 -0.0787 -0.0912 -0.0127 0.0159 -0.2144 -0.1036 0.1734 0.1115 0.0004 -0.1156 -0.1598 -0.0406 0.1476 -0.1280 0.1521 0.0956 -0.1316 -0.1269 0.2361 0.1606 0.0813 0.1096 0.2486 0.1977 0.2473 0.1046 0.0055 -0.0921 -0.3114 0.0474 -0.4273 -0.1769 -0.0278 0.0817 -0.1021 -0.1889 -0.0313 -0.0056 -0.0795 0.1399 -0.0789 0.0215 0.0217 0.0982 0.1134 0.0029 0.0469 0.0009 0.0682 0.0242 -0.0393 0.0209 0.0535 0.0553 0.0010 0.0224 -0.0518 -0.0061 0.0219 0.0006
2052e1 2052e1 Eosinophils d2052 5 #66A61E 2052e1 0.0044 -0.0473 0.0044 -0.0473 -0.1021 0.0533 -0.0126 -0.0448 -0.0600 -0.0068 -0.1188 -0.0834 0.0500 -0.1470 -0.0830 0.0794 0.0493 0.0964 0.1383 0.1782 -0.0724 -0.0585 -0.0176 0.0339 0.1722 -0.1560 0.2553 -0.1458 -0.2142 -0.0455 0.2348 -0.0584 -0.1049 0.0309 -0.2159 0.2033 -0.0482 -0.1006 0.1896 0.2720 0.1719 -0.3676 -0.0828 -0.0734 -0.1215 0.0608 -0.1746 0.0940 -0.2195 0.0382 0.0002 -0.2087 0.0327 0.0188 -0.1185 -0.0926 0.0349 -0.0621 0.0145 -0.0521 0.0131 0.0148 -0.0466
2052n2 2052n2 Neutrophils d2052 5 #D95F02 2052n2 -0.0818 0.0927 -0.0818 0.0927 0.0105 -0.3979 -0.0508 0.1484 -0.0969 -0.0531 0.3625 -0.3505 0.0149 -0.2231 0.2551 -0.3739 -0.0118 -0.1245 0.3604 -0.0547 -0.1166 0.0388 -0.0886 -0.0237 -0.1061 -0.0180 0.0542 -0.1493 0.1075 -0.0578 -0.0525 -0.0701 0.0145 -0.0082 0.0090 -0.0278 -0.0894 -0.0309 0.0489 -0.0557 -0.0272 -0.0451 -0.0674 -0.0149 0.0384 0.0570 -0.0202 0.0212 0.0382 -0.0008 0.0002 -0.0441 -0.0184 -0.0016 -0.0208 0.0090 0.0005 0.0084 -0.0017 -0.0020 -0.0009 -0.0011 0.0054
2065bp1 2065bp1 Biopsy d2065 6 #E7298A 2065bp1 -0.0073 -0.0968 -0.0073 -0.0968 -0.0972 -0.0547 0.0786 -0.2727 -0.1011 0.1435 0.0528 -0.1687 -0.0132 -0.1001 0.1349 0.0139 -0.1856 0.4736 -0.1573 0.0088 0.4528 -0.2468 -0.3028 -0.2055 -0.1844 0.0276 -0.0194 0.0845 0.0633 -0.0157 0.0427 0.0490 -0.0092 0.0050 0.0436 0.0436 -0.0731 -0.0536 0.0752 -0.0666 0.0199 0.0247 0.0084 -0.0571 -0.0341 0.0922 -0.0401 -0.0470 0.0330 -0.0703 0.0112 -0.0145 -0.0763 0.0252 -0.0472 0.0153 -0.0082 0.0256 -0.0053 0.0218 -0.0070 0.0003 0.0139
2066bp1 2066bp1 Biopsy d2066 7 #E7298A 2066bp1 -0.0164 -0.0524 -0.0164 -0.0524 -0.1225 -0.0403 0.0474 -0.1351 -0.1421 0.1507 0.0522 0.0217 -0.0251 0.0479 0.0599 0.0344 0.0596 -0.0097 0.0334 0.0535 0.1169 0.1162 -0.0301 0.1049 0.0338 -0.0717 -0.0878 -0.0707 0.0569 -0.0504 -0.0859 0.0865 -0.0294 0.0939 0.0178 0.0047 -0.0880 0.2118 -0.4407 0.0540 -0.1084 -0.0737 -0.1936 0.2000 -0.0195 -0.1443 -0.2867 -0.1848 -0.0320 0.4002 -0.0492 0.1249 0.2434 0.0372 -0.1553 -0.1010 0.0430 -0.0417 0.0067 -0.1710 -0.0114 0.0224 -0.0642
2068m1 2068m1 Monocytes d2068 8 #7570B3 2068m1 0.0261 -0.1464 0.0261 -0.1464 0.0207 0.0213 0.0348 -0.0719 -0.0741 -0.1224 -0.0343 -0.1894 -0.0042 0.0360 -0.2168 0.0673 0.0410 -0.0183 0.1699 0.1769 -0.0074 -0.0791 0.0423 0.1748 -0.1294 -0.0831 0.1512 0.0812 0.0652 -0.1726 0.0388 0.1103 -0.0335 0.1237 -0.2735 -0.0129 0.0711 -0.0737 -0.0561 -0.0066 0.3043 0.3479 -0.0976 -0.1154 0.2030 -0.1405 -0.0440 -0.0159 0.2775 0.1350 -0.1769 0.1665 -0.2438 0.0319 0.1136 0.0154 -0.0438 0.0068 0.0722 0.0538 -0.0079 -0.0088 -0.0020
2068n1 2068n1 Neutrophils d2068 8 #D95F02 2068n1 -0.0705 0.1074 -0.0705 0.1074 -0.0387 -0.1325 -0.0990 0.1263 0.0119 0.0042 0.0288 0.0883 0.0572 -0.0286 -0.2813 0.0863 -0.0103 0.0145 -0.1241 0.0039 0.0245 -0.0639 -0.1033 0.1605 -0.1654 0.0206 -0.0128 0.0182 0.1162 -0.1847 -0.0493 0.0191 0.0874 -0.1185 -0.1221 -0.1847 -0.0980 0.3262 0.2271 -0.1050 0.0476 -0.3478 0.0092 0.0476 0.2510 -0.2078 -0.0110 -0.1390 -0.2170 -0.1363 -0.0476 0.1317 -0.0211 0.0908 -0.0609 0.2233 0.1580 0.0161 0.0937 0.0680 -0.0294 0.0153 0.0020
2068e1 2068e1 Eosinophils d2068 8 #66A61E 2068e1 -0.0492 0.0136 -0.0492 0.0136 -0.1157 0.1293 -0.0687 0.0784 0.0218 -0.0307 -0.1387 0.0037 0.0904 -0.1797 0.0058 -0.0282 -0.2163 -0.1888 -0.0462 -0.2386 -0.0496 -0.1903 -0.1633 0.2245 -0.0778 0.0063 -0.0091 0.0294 0.0722 -0.1235 -0.3821 0.3231 0.0839 -0.0697 -0.0434 0.2617 0.0875 -0.2154 -0.0775 0.1662 0.0323 0.0426 0.1188 0.0381 0.0065 0.1393 -0.0717 0.0465 -0.1771 -0.0321 -0.0664 0.0393 0.1342 -0.0577 0.0307 -0.0388 -0.0771 0.2403 -0.0312 -0.0325 0.0069 0.0149 0.0086
2068bp1 2068bp1 Biopsy d2068 8 #E7298A 2068bp1 0.0037 -0.0540 0.0037 -0.0540 -0.1529 -0.0204 0.0616 -0.1976 -0.1431 0.2948 0.1944 0.1403 -0.0415 0.1894 -0.0275 0.1169 -0.0844 -0.4133 -0.0161 -0.1939 0.1542 0.3761 -0.1136 0.1497 -0.0290 -0.2449 0.0888 -0.0857 -0.0429 0.1134 0.1112 0.1362 -0.0311 0.0456 0.0407 -0.0157 -0.0929 -0.0960 0.1955 -0.0993 0.0831 0.0287 0.0649 -0.0872 -0.0402 0.1253 -0.0138 -0.0572 0.0349 -0.1476 -0.0435 -0.0423 -0.1127 0.0587 -0.0256 0.0177 0.0004 -0.0076 -0.0465 0.0330 -0.0083 -0.0043 0.0362
2072n1 2072n1 Neutrophils d2072 10 #D95F02 2072n1 -0.0487 0.0659 -0.0487 0.0659 -0.0973 -0.1115 -0.0723 0.1332 -0.0348 -0.0427 0.0542 0.1570 -0.0201 0.0834 -0.1759 0.0002 -0.0207 0.1124 -0.0133 -0.0606 -0.0500 -0.0859 -0.0673 0.2286 -0.1209 0.0959 -0.1493 0.0094 -0.0594 -0.1216 0.1993 0.0405 -0.0574 -0.2276 -0.0128 -0.1435 -0.1321 0.0567 0.0667 -0.0317 0.0173 -0.0047 0.0806 0.0381 -0.1623 0.0102 -0.1071 0.1024 0.2230 0.1788 0.2931 -0.1604 -0.0571 0.0454 0.1754 -0.2495 -0.3547 0.0681 -0.2443 -0.1660 0.0523 -0.0581 0.0020
2072e1 2072e1 Eosinophils d2072 10 #66A61E 2072e1 0.0037 -0.0938 0.0037 -0.0938 -0.0851 0.0394 0.0042 -0.1087 0.0322 -0.0827 -0.0865 -0.2917 0.1611 -0.0378 -0.2735 -0.0313 -0.3111 -0.0061 -0.1474 -0.2825 -0.3014 0.0645 0.0106 -0.3360 0.1616 -0.2213 -0.1710 -0.1299 0.1986 0.0281 0.1552 0.0366 -0.1176 -0.1105 0.0414 -0.0337 0.0560 0.2120 -0.0329 0.0212 0.1231 0.0316 -0.0655 0.1360 -0.0817 -0.0053 0.0850 -0.0610 0.1137 0.0167 -0.0293 -0.0621 0.0456 0.0012 -0.0029 0.0183 -0.0037 0.0091 0.0293 0.0294 -0.0136 -0.0205 0.0024
2072bp1 2072bp1 Biopsy d2072 10 #E7298A 2072bp1 0.0352 -0.0262 0.0352 -0.0262 -0.0850 -0.0140 0.0495 -0.1259 -0.0321 0.1156 0.0651 0.0128 0.0626 -0.0192 -0.0258 0.0381 0.0906 0.0402 0.0606 -0.0127 -0.0822 -0.0898 0.2665 0.1278 0.0437 0.2151 -0.0293 -0.0378 0.0616 -0.2442 0.0204 -0.0750 0.2021 0.3502 0.0455 0.1056 0.0581 0.1106 -0.1556 0.0006 0.0410 -0.0810 0.0594 0.1157 -0.1738 0.1621 -0.0532 -0.2560 0.1155 -0.2829 0.0656 -0.2392 0.0387 0.0785 0.1196 0.1624 -0.1785 0.0348 0.0373 0.2990 -0.0246 -0.0259 -0.0240
2071bp1 2071bp1 Biopsy d2071 9 #E7298A 2071bp1 -0.0291 -0.0098 -0.0291 -0.0098 -0.1418 -0.0421 0.0721 -0.0908 -0.1146 0.2344 0.1140 0.1096 0.0705 0.2720 0.0580 -0.1233 0.1222 -0.3337 -0.1135 0.1821 -0.1757 -0.6264 0.0560 -0.2003 0.0618 -0.0398 0.1160 0.1074 0.0811 0.1196 -0.0206 -0.1032 -0.0578 -0.2118 -0.0285 -0.0202 -0.0087 -0.0107 -0.0404 -0.0095 0.0378 -0.0118 -0.0213 0.0102 0.0211 0.0121 0.0031 -0.0036 0.0017 -0.0075 0.0090 0.0312 -0.0011 -0.0279 -0.0061 -0.0067 0.0031 0.0079 -0.0026 -0.0046 0.0048 -0.0014 -0.0018
2073m1 2073m1 Monocytes d2073 11 #7570B3 2073m1 -0.0063 0.0209 -0.0063 0.0209 -0.0762 0.0015 -0.0088 0.0519 -0.0734 -0.0845 -0.0898 0.0664 0.0123 0.1132 -0.0198 0.0046 0.0299 0.0747 0.0892 0.0424 -0.0085 0.0147 -0.0181 -0.0195 0.0437 0.0074 0.0635 -0.0124 0.0997 -0.0024 0.0538 0.0440 0.0909 0.0311 0.0930 0.0013 0.0113 -0.0091 0.0352 -0.0790 -0.0222 0.0281 0.0275 -0.0284 0.0465 0.1310 0.0095 0.0993 0.0140 0.1604 -0.0036 -0.0522 0.1469 -0.0075 -0.0644 0.1386 0.0569 0.0091 0.0219 0.0407 0.0420 -0.0310 0.5089
2073e1 2073e1 Eosinophils d2073 11 #66A61E 2073e1 -0.0675 -0.0011 -0.0675 -0.0011 -0.1104 0.0757 -0.0475 0.0637 -0.0543 -0.0246 -0.1180 0.0430 0.0268 -0.0075 0.0237 -0.0241 -0.0150 -0.0109 0.0287 -0.0191 -0.0584 -0.0398 -0.0159 0.0122 0.0523 0.0097 -0.0493 -0.0278 0.2266 -0.0313 -0.1099 0.2101 0.0376 0.0612 -0.1564 0.1490 0.0293 -0.0786 -0.0609 -0.2584 -0.2837 -0.1200 0.0006 -0.2261 -0.3027 -0.2149 0.1591 0.0244 0.0270 0.0476 0.1369 -0.0336 -0.3069 0.0977 -0.0699 -0.1257 0.1416 -0.4406 0.0093 0.1008 -0.0347 -0.0037 -0.0234
2073bp1 2073bp1 Biopsy d2073 11 #E7298A 2073bp1 0.0129 -0.0217 0.0129 -0.0217 -0.1156 -0.0330 0.0345 -0.1252 -0.0952 0.1658 0.1217 0.0368 -0.0163 0.0001 -0.0546 -0.0305 -0.0300 -0.0123 0.0021 -0.1202 -0.0169 -0.0324 0.0348 -0.0736 -0.1401 0.1184 0.0143 -0.1361 -0.2190 -0.0971 -0.0107 -0.0882 0.0720 0.2332 -0.0187 0.1421 0.3612 0.1726 0.3452 0.1411 -0.2185 0.1715 0.0583 0.1282 0.0168 -0.3128 0.1940 0.2194 -0.0756 0.2034 0.0518 0.0268 0.0511 -0.1411 0.0763 -0.0395 -0.0002 0.0221 0.0953 -0.0625 0.0022 0.0028 -0.0046
2068m2 2068m2 Monocytes d2068 8 #7570B3 2068m2 0.0346 -0.0935 0.0346 -0.0935 0.0733 -0.0398 0.0144 0.0368 -0.1413 -0.1589 -0.0604 -0.1174 -0.0189 0.2597 -0.0488 -0.0079 0.0574 -0.0897 0.0774 -0.0611 0.0474 0.1117 0.0765 -0.1112 -0.2110 0.0627 -0.2154 0.2608 0.1304 -0.0073 0.1229 -0.1198 -0.0339 0.0435 0.1705 0.1564 0.0586 -0.0963 -0.0261 0.1214 0.0415 -0.1667 0.0749 0.0454 -0.0166 0.1217 -0.1233 -0.1393 -0.3698 0.1353 0.1196 0.0714 -0.3386 -0.1920 0.2127 -0.0491 0.1079 0.0491 0.0759 -0.0337 0.0506 -0.0235 -0.0106
2068n2 2068n2 Neutrophils d2068 8 #D95F02 2068n2 -0.0645 0.1292 -0.0645 0.1292 0.0031 -0.1509 -0.0635 0.0907 0.0253 -0.0204 0.0229 0.0445 0.0966 -0.0702 -0.0969 0.0542 0.0060 -0.0418 -0.1601 0.1308 0.2465 0.1632 0.2234 -0.2134 0.1584 -0.1431 0.0731 0.1038 0.2338 0.0080 -0.2357 -0.1481 0.0246 0.0799 0.0552 0.2445 -0.0391 -0.2437 0.1312 0.1136 0.0800 -0.0029 -0.0469 0.1333 0.1779 -0.1962 0.0339 -0.0268 0.1115 0.0210 0.2769 -0.0088 0.0380 0.2883 0.0053 -0.0484 -0.1315 0.0520 -0.1314 -0.0565 -0.0143 -0.0098 0.0173
2068e2 2068e2 Eosinophils d2068 8 #66A61E 2068e2 -0.0547 0.0328 -0.0547 0.0328 -0.0799 0.1464 -0.0634 0.0927 -0.0469 -0.0011 -0.1804 -0.0371 0.0638 -0.1677 0.2148 -0.0669 -0.0601 -0.2670 -0.0115 -0.0888 0.3514 0.0238 0.3616 -0.1032 0.0123 0.3321 -0.0129 0.0451 0.1491 0.0115 0.3117 0.1473 -0.0989 -0.0442 -0.1266 -0.1736 -0.0244 0.1402 0.0609 0.0920 -0.0353 0.0481 -0.1471 -0.1746 0.0128 -0.0869 -0.1500 0.1290 -0.0136 -0.0800 -0.0415 0.0024 0.0863 -0.0367 0.0060 0.0016 -0.0296 0.0766 -0.0061 0.0106 -0.0348 0.0201 -0.0015
2072m2 2072m2 Monocytes d2072 10 #7570B3 2072m2 0.2058 -0.3683 0.2058 -0.3683 0.3274 -0.0801 -0.2565 0.0853 -0.0676 0.1486 0.0028 0.1434 0.0596 -0.0748 0.0402 0.0731 -0.4057 0.0373 0.0956 0.2270 -0.0837 -0.0773 0.2875 0.0441 -0.2594 -0.1641 -0.0598 -0.0147 -0.0142 0.2184 -0.0608 0.0613 0.0764 0.0953 0.0153 -0.0987 -0.0273 0.0102 0.0176 -0.0751 0.0366 -0.0373 -0.0098 0.0026 -0.1008 0.0084 0.0069 0.0615 -0.0366 -0.0079 0.1017 0.0031 0.1023 -0.0261 -0.0564 -0.0011 0.0396 0.0239 -0.0313 -0.0180 -0.0147 -0.0127 -0.0011
2072n2 2072n2 Neutrophils d2072 10 #D95F02 2072n2 -0.0106 0.1271 -0.0106 0.1271 -0.0381 -0.1506 -0.0487 0.1296 -0.0041 -0.0245 0.1157 0.0724 0.0481 -0.0331 -0.1474 -0.1704 -0.0056 0.0524 -0.2222 0.0677 0.1144 0.0157 0.1560 0.1236 -0.0843 0.0002 -0.0521 -0.0160 -0.1500 -0.0799 0.2588 0.0428 -0.1919 -0.0974 0.1024 0.1353 -0.0923 -0.3559 -0.2049 -0.0638 0.0570 0.0598 0.1137 0.2649 -0.2004 -0.1722 0.1039 0.1052 0.0430 -0.0735 -0.2263 0.0069 0.0747 -0.1564 -0.0978 0.0964 0.1675 -0.0424 0.2205 0.0835 -0.0524 -0.0136 -0.0015
2072e2 2072e2 Eosinophils d2072 10 #66A61E 2072e2 -0.0090 -0.0134 -0.0090 -0.0134 -0.1164 0.0867 -0.0292 0.0025 -0.0088 -0.0393 -0.1301 -0.0439 0.0426 -0.1986 0.0001 0.0324 -0.0101 -0.0502 0.0750 0.1041 0.0026 0.0069 -0.0321 -0.1072 0.0155 -0.1449 0.0473 -0.1090 -0.2042 0.0501 0.0088 -0.0389 -0.0316 -0.1104 0.1660 -0.1320 0.2220 -0.1552 0.0233 -0.1416 -0.2170 -0.0881 0.2010 0.0806 0.0867 -0.0608 -0.4057 -0.0993 0.1012 -0.1005 0.0686 0.3217 0.0342 -0.0867 0.2345 0.1674 -0.2167 -0.2552 0.0077 0.0827 0.0438 -0.0748 0.0081
2073m2 2073m2 Monocytes d2073 11 #7570B3 2073m2 0.0860 -0.2537 0.0860 -0.2537 0.1578 -0.0342 -0.0692 -0.0183 -0.0609 -0.0559 -0.0153 -0.1777 0.0945 0.0608 -0.1401 0.0081 0.4278 -0.0745 -0.0404 -0.0977 0.1836 0.0267 -0.0120 0.0090 -0.0425 0.0980 -0.2185 -0.1409 -0.1454 0.0752 -0.2980 -0.0757 -0.1790 -0.1839 -0.0171 0.2136 0.0825 0.1626 0.0158 -0.1746 0.1604 -0.0127 0.0223 -0.1931 -0.1141 0.0297 -0.0876 0.1792 0.1023 -0.1098 0.0917 -0.0208 0.1769 0.0340 -0.0942 0.0615 0.0228 -0.0035 0.0515 -0.0100 -0.0018 -0.0499 0.0101
2073n2 2073n2 Neutrophils d2073 11 #D95F02 2073n2 -0.0398 0.0003 -0.0398 0.0003 -0.1062 -0.0169 -0.0014 0.0140 0.0251 -0.0899 -0.0323 0.1347 -0.0243 0.1170 0.0407 -0.0446 -0.0633 0.0960 0.0280 -0.0384 -0.0374 -0.0176 0.1172 -0.0432 0.0484 0.0183 -0.0494 -0.0844 -0.0824 -0.1089 -0.0773 0.0170 0.2336 -0.0407 0.1280 0.0019 0.1040 -0.0147 0.0589 0.0849 0.0148 0.0077 -0.0307 -0.0305 0.0969 0.0575 -0.2541 -0.0081 0.3430 -0.2099 -0.0590 -0.1218 -0.1173 -0.0435 0.0165 -0.0218 0.5373 0.0012 -0.0850 -0.4088 -0.1039 -0.0306 -0.0999
2073e2 2073e2 Eosinophils d2073 11 #66A61E 2073e2 -0.0715 -0.0203 -0.0715 -0.0203 -0.1260 0.0714 -0.0232 0.0425 0.0145 0.0276 -0.0890 0.0382 -0.0099 0.0523 0.1564 -0.0490 -0.0476 0.0196 -0.0205 0.1055 -0.1127 0.1018 0.0359 -0.0687 0.0032 0.0765 -0.3070 0.0205 -0.0894 -0.1489 -0.0366 -0.1637 -0.1209 0.0915 -0.4422 0.1004 -0.2498 -0.0303 0.2105 -0.1218 0.0400 0.2077 0.0663 0.1782 0.0789 0.2792 0.0857 -0.1709 -0.0522 -0.0609 0.0033 0.2039 0.1570 -0.0943 -0.0569 -0.0182 0.0178 -0.2212 -0.1122 -0.1073 0.0412 -0.0126 0.0080
2068m3 2068m3 Monocytes d2068 8 #7570B3 2068m3 0.0511 -0.0587 0.0511 -0.0587 0.0599 -0.0265 -0.0112 0.0345 -0.0768 -0.1442 -0.0851 -0.2136 -0.0236 0.3067 -0.1064 -0.0282 -0.0046 -0.0605 0.0269 -0.0738 -0.0171 0.0503 -0.1288 -0.1147 -0.1564 -0.0690 0.1672 0.2347 -0.1816 -0.1246 0.1491 -0.0025 0.1805 0.0825 -0.1169 -0.0537 -0.0443 -0.0599 -0.2223 0.1536 -0.2989 -0.0585 0.0075 -0.0592 0.0075 -0.0905 0.1301 0.0711 0.0445 -0.3248 0.1078 0.1143 0.2264 0.0593 -0.2326 -0.0026 -0.1016 0.0241 -0.1905 -0.0576 -0.0846 0.0087 -0.0300
2068n3 2068n3 Neutrophils d2068 8 #D95F02 2068n3 -0.0342 0.1428 -0.0342 0.1428 -0.0309 -0.1438 -0.0960 0.1514 0.0140 0.0438 0.0554 0.0366 0.1102 -0.0846 -0.2633 -0.0264 0.0253 0.0396 -0.2838 0.1378 0.1130 0.1033 0.0235 -0.0324 -0.0378 -0.1469 0.1649 -0.0185 0.0227 0.0486 -0.1838 -0.1528 -0.0809 0.1147 -0.1452 -0.1229 0.1140 0.1800 -0.1515 0.1021 -0.2756 0.1124 -0.1177 -0.1865 -0.0846 0.3693 -0.0400 0.0344 -0.0398 -0.0419 -0.1662 -0.0680 -0.1263 -0.2613 0.1181 -0.1330 -0.0210 -0.0101 -0.0367 0.0148 0.0366 0.0270 0.0411
2068e3 2068e3 Eosinophils d2068 8 #66A61E 2068e3 -0.0540 0.0291 -0.0540 0.0291 -0.1134 0.1366 -0.1202 0.1572 -0.0189 0.0571 -0.1988 -0.0787 0.0511 -0.1154 0.0417 0.0031 -0.1260 -0.1479 -0.0777 -0.1754 0.0278 -0.0937 -0.2378 0.2204 -0.0712 0.1110 0.0011 0.1076 -0.1662 0.2525 -0.0342 -0.4469 0.0539 0.2062 0.1669 -0.0873 -0.1034 0.0247 -0.1366 0.0920 0.2140 0.0321 -0.1551 0.0640 0.0744 -0.0521 0.0622 0.0574 0.1129 0.0443 0.0953 -0.0902 -0.1144 0.0333 -0.0720 0.0502 0.0443 -0.2422 0.0722 0.0090 0.0123 -0.0127 0.0494
2072m3 2072m3 Monocytes d2072 10 #7570B3 2072m3 0.1625 -0.3357 0.1625 -0.3357 0.2955 -0.0956 -0.2287 0.2039 -0.0977 0.1318 -0.0571 0.3069 0.0459 -0.0693 0.1897 -0.0507 0.1170 -0.0095 -0.1111 -0.1409 -0.0341 0.0536 -0.3146 -0.1473 0.2528 0.0502 0.0895 -0.0005 0.0458 -0.3146 0.1573 -0.0294 -0.0334 -0.0616 0.0182 0.0705 0.0398 0.0094 -0.0377 0.1133 -0.0350 0.0447 0.0010 0.0288 0.1108 -0.0233 0.0170 -0.0861 0.0614 0.0207 -0.1650 -0.0250 -0.1110 0.0516 0.0662 -0.0009 -0.0724 -0.0466 0.0371 0.0288 0.0229 0.0272 0.0028
2072n3 2072n3 Neutrophils d2072 10 #D95F02 2072n3 -0.0536 0.0794 -0.0536 0.0794 -0.0600 -0.1362 -0.0357 0.0888 -0.1164 -0.0219 -0.0146 0.0624 0.0043 0.0381 -0.0198 -0.1764 0.0345 0.1458 0.0255 0.0548 -0.0337 0.0730 -0.0508 -0.0723 0.1248 0.1205 0.0389 -0.0222 -0.0417 0.3107 -0.0482 0.1221 0.0993 0.0568 0.0051 -0.1119 -0.0115 0.1336 -0.0179 0.3119 0.2813 0.1722 0.4601 -0.2288 -0.2110 -0.1478 0.0190 -0.2426 -0.0967 -0.0250 -0.0509 0.1619 0.0762 0.1120 0.0155 -0.0503 0.0209 -0.0529 -0.0184 0.0108 0.0238 0.0033 -0.0270
2072e3 2072e3 Eosinophils d2072 10 #66A61E 2072e3 -0.0123 0.0018 -0.0123 0.0018 -0.0979 0.0877 -0.0520 0.0555 -0.1127 -0.0211 -0.2100 -0.0713 -0.0239 -0.2072 0.0803 0.0769 0.1858 -0.1134 0.1081 0.0936 0.1654 -0.0056 0.0763 -0.1180 -0.0910 -0.1330 0.0263 -0.0543 -0.2560 -0.0151 -0.0349 0.0776 0.0205 -0.1483 -0.0368 -0.1735 0.0936 0.0405 -0.0954 -0.0926 -0.0412 -0.0756 0.1707 0.1787 0.0179 0.1165 0.4448 -0.2929 0.0768 0.1504 0.0232 -0.1833 -0.1542 0.0850 -0.0413 -0.0316 0.0798 0.2614 0.0072 -0.0698 -0.0232 -0.0134 -0.0213
2159bp1 2159bp1 Biopsy d2159 12 #E7298A 2159bp1 -0.0063 0.0209 -0.0063 0.0209 -0.0762 0.0015 -0.0088 0.0519 -0.0734 -0.0845 -0.0898 0.0664 0.0123 0.1132 -0.0198 0.0046 0.0299 0.0747 0.0892 0.0424 -0.0085 0.0147 -0.0181 -0.0195 0.0437 0.0074 0.0635 -0.0124 0.0997 -0.0024 0.0538 0.0440 0.0909 0.0311 0.0930 0.0013 0.0113 -0.0091 0.0352 -0.0790 -0.0222 0.0281 0.0275 -0.0284 0.0465 0.1310 0.0095 0.0993 0.0140 0.1604 -0.0036 -0.0522 0.1469 -0.0075 -0.0644 0.1386 0.0569 0.0091 0.0219 0.0407 0.0420 -0.0310 0.5089
2073m3 2073m3 Monocytes d2073 11 #7570B3 2073m3 0.0407 -0.2370 0.0407 -0.2370 0.0956 -0.0853 0.0482 -0.0780 0.0423 -0.0192 -0.0095 -0.2838 0.2004 0.1176 -0.0954 -0.0259 0.0425 0.0641 -0.1318 -0.0200 0.0096 -0.0691 0.0837 0.3081 0.2924 0.2636 0.0237 -0.1490 -0.0333 0.2781 -0.0124 0.1186 0.0362 -0.0010 0.0239 -0.0932 -0.2119 -0.1851 0.1770 0.0853 -0.3530 -0.0243 -0.0109 0.1166 0.1805 0.0430 0.0801 -0.1347 0.0057 0.0823 -0.0676 0.0126 -0.0870 0.0104 0.0665 -0.0136 0.0158 0.0232 -0.0312 -0.0238 -0.0089 0.0396 -0.0022
2073n3 2073n3 Neutrophils d2073 11 #D95F02 2073n3 -0.1386 0.0840 -0.1386 0.0840 0.0111 -0.3333 -0.0818 0.0948 0.1439 -0.0603 0.1411 -0.1514 -0.0296 -0.0134 0.2000 0.4356 -0.0489 -0.0438 -0.0154 -0.0331 0.0351 -0.1335 0.0207 0.1000 0.3650 -0.1485 -0.2026 0.3503 -0.2156 0.0325 0.1292 0.1242 0.0971 0.0266 -0.0083 0.0736 0.1874 0.1220 0.0035 -0.0962 0.0193 0.0714 -0.0091 -0.0674 -0.0260 0.0388 0.0039 0.0429 -0.0426 0.0398 -0.0167 0.0254 0.0224 -0.0360 -0.0164 0.0233 0.0132 -0.0252 0.0337 0.0248 0.0098 0.0048 -0.0220
2073e3 2073e3 Eosinophils d2073 11 #66A61E 2073e3 -0.0772 -0.0005 -0.0772 -0.0005 -0.1131 0.0964 -0.0729 0.1072 -0.0513 0.0128 -0.1006 0.0166 -0.0016 0.0459 0.0164 0.0031 0.0083 0.0763 0.0435 0.0751 -0.1214 0.0397 -0.1567 0.0186 0.0672 0.0061 -0.0745 0.0114 0.1100 0.1901 0.1018 -0.2604 -0.0376 0.2155 -0.0024 0.1069 0.0352 -0.0887 0.0346 -0.4074 -0.0820 -0.0911 -0.0131 -0.1446 -0.0616 -0.1999 -0.0358 -0.0277 0.0098 -0.0639 -0.2991 0.0206 0.1179 0.0495 0.1530 -0.1233 -0.0246 0.4900 -0.0448 -0.0763 -0.0485 0.0677 -0.1024
2162m1 2162m1 Monocytes d2162 13 #7570B3 2162m1 -0.0865 -0.1744 -0.0865 -0.1744 0.1030 -0.0725 0.7377 0.4497 0.1370 0.2638 -0.1277 0.0097 -0.1694 -0.0785 -0.1163 -0.0016 -0.0451 0.0241 0.0917 -0.0496 0.0496 -0.0252 0.0079 -0.0071 0.0237 -0.0371 0.0054 -0.0175 0.0254 0.0097 0.0051 -0.0053 -0.0347 -0.0024 0.0027 0.0159 0.0141 0.0169 -0.0187 -0.0154 0.0063 0.0052 0.0021 -0.0101 -0.0096 -0.0005 0.0090 -0.0013 -0.0001 -0.0337 0.0629 0.0141 0.0275 -0.0345 -0.0120 0.0085 0.0213 0.0138 -0.0029 -0.0066 0.0025 -0.0047 -0.0018
2162n1 2162n1 Neutrophils d2162 13 #D95F02 2162n1 -0.1912 0.0761 -0.1912 0.0761 0.0569 -0.1689 -0.1478 0.0180 0.3067 0.0943 -0.0843 -0.0916 -0.1356 0.1721 0.1209 0.4864 0.0129 -0.0324 -0.0255 -0.0044 -0.1426 0.0024 -0.0561 -0.1762 -0.2634 0.2650 0.1816 -0.3776 0.1075 -0.0001 -0.0109 0.0227 -0.2108 0.0311 0.0219 -0.0145 -0.0206 -0.1232 -0.1060 0.0774 0.0310 0.0133 0.0521 -0.0003 -0.0133 -0.0136 -0.0051 -0.0344 0.0209 0.0045 0.0157 0.0021 0.0054 -0.0099 -0.0031 -0.0057 0.0294 0.0049 0.0083 -0.0061 -0.0106 -0.0028 0.0069
2162e1 2162e1 Eosinophils d2162 13 #66A61E 2162e1 -0.2873 0.0421 -0.2873 0.0421 0.1790 0.5985 -0.2023 0.2829 -0.0393 0.2347 0.3881 -0.2399 -0.0923 0.1671 0.0117 -0.0187 0.0458 0.1849 -0.0166 -0.0230 0.0212 0.0006 0.0898 0.0188 0.0674 -0.0993 0.0547 -0.0046 0.0046 -0.0602 -0.0088 0.0558 0.0158 -0.0503 0.0615 -0.0096 0.0354 0.0235 0.0237 0.0349 0.0256 0.0045 0.0355 0.0343 0.0172 0.0159 -0.0168 -0.0191 -0.0169 0.0175 0.0193 0.0059 -0.0087 0.0043 -0.0020 0.0090 0.0021 -0.0218 -0.0093 0.0158 0.0084 -0.0061 0.0034
2162bp1 2162bp1 Biopsy d2162 13 #E7298A 2162bp1 0.0015 0.0479 0.0015 0.0479 -0.0517 0.0133 -0.0088 0.0765 -0.0684 -0.1155 -0.0933 0.0818 0.0368 0.1439 -0.0432 0.0146 -0.0160 0.0209 0.0640 0.0313 0.0534 0.0342 -0.0627 -0.0248 0.0055 0.0038 0.1210 0.0173 0.1513 -0.0217 0.0176 0.0238 0.0901 0.0054 0.1430 -0.0355 -0.0474 -0.0250 0.1311 -0.0576 -0.0224 0.0391 0.0519 -0.0696 0.0649 0.2580 0.0732 0.1380 0.1153 0.2761 0.0558 -0.0454 0.2500 -0.0673 -0.0658 0.1745 0.0402 -0.0491 0.1433 0.1992 0.1780 0.0068 -0.6415
macrofagos Macrofagos macrophage unknown 14 #E6AB02 Macrofagos 0.1075 0.0624 0.1075 0.0624 -0.0026 0.0136 0.0068 -0.0267 -0.0182 -0.0565 -0.0157 0.0460 -0.1562 0.0452 -0.0102 0.0170 -0.0393 0.0172 0.0535 -0.0129 0.0036 -0.0537 0.0727 0.0564 0.1401 0.0445 -0.1521 -0.0676 0.0486 -0.1644 -0.1324 -0.1287 -0.0779 0.0376 0.1734 -0.2674 0.1577 -0.1194 0.1756 0.1029 0.0224 -0.1022 -0.0765 -0.0020 -0.1265 0.1066 0.0035 0.0033 0.0948 -0.0611 -0.0801 0.3239 -0.1458 0.0275 -0.4889 -0.3210 -0.1308 0.1311 0.1721 0.1270 0.0476 0.0045 0.0410
macrofagos+sbv Macrofagos+SbV macrophage unknown 14 #E6AB02 Mcrfgs+SbV 0.2461 0.2008 0.2461 0.2008 0.1284 0.0689 0.0658 -0.0190 0.1453 0.0041 0.0322 -0.0069 0.1318 0.1484 0.0892 -0.0168 -0.0711 -0.0070 0.1284 0.0339 0.1182 -0.0054 -0.1291 0.0941 0.0056 -0.0390 -0.1253 -0.0184 0.0758 0.0320 -0.0235 -0.1005 -0.1182 -0.0772 -0.2271 -0.1288 0.2476 -0.0879 0.0001 0.1336 -0.0659 0.0417 -0.0707 0.1226 -0.2301 -0.1075 -0.0328 -0.0404 -0.0050 -0.0290 -0.0372 -0.1122 -0.0262 0.1829 0.0457 0.3001 0.1198 0.0454 -0.1550 -0.0327 0.4729 0.1243 0.0135
macrofagos+10772 Macrofagos+10772 macrophage unknown 14 #E6AB02 Mcrf+10772 0.2056 0.1108 0.2056 0.1108 0.1114 0.0386 -0.0039 -0.0446 -0.0178 -0.0793 0.0133 -0.0774 -0.4173 -0.0848 -0.0089 -0.0801 -0.0121 -0.1084 -0.2017 0.1012 0.0244 -0.0448 -0.0754 0.0394 0.0799 0.0152 0.0092 -0.1854 0.0835 0.1190 0.1686 0.0233 0.1395 0.0716 -0.1372 0.0929 0.0208 0.1435 -0.1329 -0.0665 0.0010 -0.0209 0.1643 0.2207 0.0675 0.2152 -0.0488 0.3140 -0.0222 -0.0452 0.1309 0.1730 -0.0631 0.3078 0.1636 -0.1111 0.1397 0.0343 0.0826 0.0194 -0.0228 0.0999 -0.0173
macrofagos+10772+sbv Macrofagos+10772+SbV macrophage unknown 14 #E6AB02 M+10772+SV 0.2185 0.2072 0.2185 0.2072 0.0968 0.0655 0.0649 0.0064 0.0686 0.0420 -0.0106 0.0185 0.1521 0.0727 0.0926 0.0297 0.0525 0.0316 0.0874 0.0657 0.0392 0.0298 -0.1354 0.0649 0.0206 -0.0418 -0.1093 -0.0217 0.1580 0.0680 0.0033 -0.0423 -0.0111 -0.0349 -0.0842 -0.0708 0.1283 -0.0239 0.0527 0.0959 0.0400 -0.0103 0.0440 0.0691 -0.0601 0.0496 0.0164 0.0632 0.0006 0.1048 -0.0172 -0.0201 -0.0325 -0.1031 -0.0250 0.1805 -0.1038 -0.0367 -0.0905 -0.0068 -0.7734 -0.0599 -0.0639
macrofagos+2169 Macrofagos+2169 macrophage unknown 14 #E6AB02 Mcrfg+2169 0.2075 0.1163 0.2075 0.1163 0.1052 0.0516 -0.0051 -0.0546 -0.0783 0.0077 -0.0131 -0.0856 -0.4097 -0.2387 -0.0120 0.0967 0.2515 -0.0537 -0.1953 -0.0115 -0.1604 -0.0282 -0.0253 0.0483 -0.1415 -0.1178 -0.1484 0.0902 0.1894 0.1078 0.0539 0.0158 0.1682 -0.0668 -0.0124 -0.0026 -0.0740 -0.1361 0.1849 0.1350 -0.0900 0.0784 -0.0782 -0.1228 -0.0034 -0.1468 -0.0507 -0.2562 0.1024 -0.0052 -0.0253 -0.2210 0.2015 -0.2459 -0.0358 -0.0223 -0.0029 -0.0736 0.0487 -0.0050 0.0737 -0.0478 0.0350
macrofagos+2169+sbv Macrofagos+2169+SbV macrophage unknown 14 #E6AB02 Mc+2169+SV 0.1373 0.1042 0.1373 0.1042 -0.0025 0.0808 0.0537 -0.0552 -0.0173 0.1008 -0.0798 -0.0615 0.1800 -0.1861 0.0171 0.2126 0.1882 0.0261 0.1041 -0.0116 -0.1441 -0.0372 0.0863 -0.0288 -0.0214 -0.1475 0.0050 0.0450 -0.0065 -0.1902 -0.0367 -0.0772 0.0127 -0.0266 0.2919 -0.0125 -0.4631 0.1180 0.0466 0.0435 -0.1496 0.3111 0.0885 -0.0204 -0.1693 -0.0448 -0.0532 0.2274 -0.1584 -0.0403 -0.0609 0.1352 -0.1521 0.1400 0.0222 0.0554 0.1130 0.0867 -0.1078 -0.0144 0.0197 0.0631 -0.0260
macrofagos+12309 Macrofagos+12309 macrophage unknown 14 #E6AB02 Mcrf+12309 0.1760 0.0807 0.1760 0.0807 0.0805 0.0380 0.0045 -0.0505 0.0507 -0.1267 0.0124 -0.0273 -0.2743 0.0785 -0.0038 -0.0654 -0.2135 -0.0818 -0.0249 0.0154 0.1121 -0.0533 0.0376 -0.0535 0.2242 0.0647 0.1305 -0.0749 -0.1032 -0.1056 -0.1680 -0.0202 -0.0622 0.0842 0.0420 0.0436 -0.1979 0.1089 -0.0474 -0.1188 0.0487 -0.0785 0.0353 -0.0680 -0.0390 -0.1273 0.0613 0.0065 -0.0843 0.0640 -0.1260 -0.0489 -0.1160 -0.1806 0.0139 0.1844 -0.1184 0.0403 -0.1721 -0.1024 0.0795 -0.5829 -0.0336
macrofagos+12309+sbv Macrofagos+12309+SbV macrophage unknown 14 #E6AB02 M+12309+SV 0.2401 0.2395 0.2401 0.2395 0.1023 0.0786 0.1011 -0.0291 0.1936 0.0718 0.0478 0.0124 0.3254 0.0345 0.0863 0.0073 -0.0142 0.0210 -0.0070 -0.0557 0.0624 0.0106 0.0704 -0.0359 -0.0557 -0.0274 -0.0331 -0.2144 -0.0567 0.0132 0.1386 -0.0281 0.3345 -0.0719 -0.0158 0.2111 -0.0649 0.0514 -0.1504 -0.1201 0.1505 -0.1438 0.0968 -0.1818 0.2465 -0.0503 0.0849 -0.0401 0.0656 0.0468 -0.0183 0.1058 -0.0597 -0.2660 -0.0841 -0.2292 -0.1312 -0.0364 -0.0264 -0.0088 0.1811 0.1571 0.0327
macrofagos+12367+sbv Macrofagos+12367+SbV macrophage unknown 14 #E6AB02 M+12367+SV 0.1979 0.1639 0.1979 0.1639 0.0752 0.0542 0.0522 -0.0151 0.1168 0.0107 0.0379 0.0100 0.1519 0.0780 0.0017 -0.0240 -0.1011 0.0208 0.0714 -0.0443 -0.0055 0.0105 -0.0416 -0.0013 -0.0231 0.0282 0.1041 0.0789 -0.1436 -0.0849 -0.0141 -0.0025 -0.0892 -0.0852 0.0130 -0.0736 -0.0020 -0.0530 0.0193 -0.0916 -0.0508 0.1452 -0.1618 -0.1218 0.0349 0.0311 -0.0080 -0.1995 -0.1361 -0.0418 0.1378 -0.0705 0.2433 0.2726 0.2497 -0.3390 0.1414 -0.0361 0.4261 0.0214 -0.0927 -0.2968 0.0381
macrofagos+1126 Macrofagos+1126 macrophage unknown 14 #E6AB02 Mcrfg+1126 0.1270 0.0400 0.1270 0.0400 0.0326 0.0177 -0.0116 -0.0324 0.0143 -0.1415 0.0026 -0.0063 -0.2474 0.0988 -0.0608 -0.0595 -0.1772 -0.0482 0.0449 -0.0309 0.0797 -0.0606 0.0482 -0.0660 0.1270 0.0320 0.0863 0.0100 -0.1811 -0.1583 -0.1977 0.0010 -0.1754 0.0134 0.1263 -0.0630 -0.1217 0.0097 0.0304 -0.1155 0.0658 -0.0012 -0.0339 -0.0973 -0.0189 -0.0786 0.0153 -0.1216 -0.0564 0.0476 0.0306 -0.1342 0.0826 -0.1035 0.1616 0.0237 -0.0307 -0.0878 -0.0306 0.0237 -0.1548 0.6851 0.0425
macrofagos+12251 Macrofagos+12251 macrophage unknown 14 #E6AB02 Mcrf+12251 0.0834 0.1119 0.0834 0.1119 -0.0464 -0.0147 0.0026 0.0197 -0.0440 0.0486 0.0788 0.1097 -0.0598 -0.1309 -0.0925 -0.1068 0.0638 0.1169 -0.1527 -0.1035 -0.2245 -0.0013 0.1096 -0.0471 -0.0943 0.1316 -0.0524 -0.0559 -0.2128 0.0684 0.1320 0.1350 -0.0767 0.0379 0.1141 0.0782 0.1815 -0.1440 -0.0756 -0.0946 0.0333 0.0732 -0.3286 -0.2589 0.1374 0.1551 0.0363 -0.1235 -0.1882 0.1200 0.0789 0.1449 -0.0910 0.2637 -0.1461 0.2775 -0.0462 0.0268 -0.2446 -0.0669 -0.0323 0.0076 -0.0854
macrofagos+12251+sbv Macrofagos+12251+SbV macrophage unknown 14 #E6AB02 M+12251+SV 0.1751 0.1842 0.1751 0.1842 0.0530 0.0392 0.0464 0.0145 -0.0390 0.0937 0.0068 0.0173 0.0806 -0.0511 0.0195 -0.0019 0.0911 0.0851 0.0532 -0.0075 -0.2133 0.1305 -0.0585 -0.1372 -0.0482 0.1926 0.1577 0.3905 -0.0733 0.2072 -0.1329 0.3526 -0.2949 0.1751 0.0013 0.0481 -0.0243 0.1384 0.0747 -0.0782 0.0376 -0.1746 0.0184 0.2669 0.0968 0.0126 -0.0958 0.0993 0.2197 -0.0799 -0.0319 -0.0563 -0.0910 -0.0937 -0.0833 -0.0862 -0.0036 0.0051 0.0154 0.0039 0.0890 0.0224 0.0056 
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 Monocytes

mono <- subset_expt(hs_valid, subset="typeofcells=='Monocytes'")
## Using a subset expression.
## There were 63, now there are 11 samples.
mono <- set_expt_conditions(mono, fact="visitnumber")
mono <- set_expt_batches(mono, fact="donor")
mono_norm <- normalize_expt(mono, transform="log2", convert="cpm", batch="svaseq", filter=TRUE)
## 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$best_libsize
## Leaving the data unnormalized.  This is necessary for DESeq, but
##  EdgeR/limma might benefit from normalization.  Good choices include quantile,
##  size-factor, tmm, etc.
## Step 1: performing count filter with option: cbcb
## Removing 45384 low-count genes (12918 remaining).
## Step 2: not normalizing the data.
## Step 3: converting the data with cpm.
## Step 4: transforming the data with log2.
## transform_counts: Found 4732 values equal to 0, adding 1 to the matrix.
## Step 5: doing batch correction with svaseq.
## Using the current state of normalization.
## Passing the data to all_adjusters using the svaseq estimate type.
## batch_counts: Before batch/surrogate estimation, 127993 entries are x>1: 90%.
## batch_counts: Before batch/surrogate estimation, 4732 entries are x==0: 3%.
## batch_counts: Before batch/surrogate estimation, 9373 entries are 0<x<1: 7%.
## The be method chose 2 surrogate variables.
## Attempting svaseq estimation with 2 surrogates.
## There are 429 (0%) elements which are < 0 after batch correction.
## Setting low elements to zero.
plot_pca(mono_norm)$plot

5.2 Neutrophils

neut <- subset_expt(hs_valid, subset="typeofcells=='Neutrophils'")
## Using a subset expression.
## There were 63, now there are 12 samples.
neut <- set_expt_conditions(neut, fact="visitnumber")
neut <- set_expt_batches(neut, fact="donor")
neut_norm <- normalize_expt(neut, transform="log2", convert="cpm", batch="svaseq", filter=TRUE)
## 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$best_libsize
## Leaving the data unnormalized.  This is necessary for DESeq, but
##  EdgeR/limma might benefit from normalization.  Good choices include quantile,
##  size-factor, tmm, etc.
## Step 1: performing count filter with option: cbcb
## Removing 47774 low-count genes (10528 remaining).
## Step 2: not normalizing the data.
## Step 3: converting the data with cpm.
## Step 4: transforming the data with log2.
## transform_counts: Found 5202 values equal to 0, adding 1 to the matrix.
## Step 5: doing batch correction with svaseq.
## Using the current state of normalization.
## Passing the data to all_adjusters using the svaseq estimate type.
## batch_counts: Before batch/surrogate estimation, 112211 entries are x>1: 89%.
## batch_counts: Before batch/surrogate estimation, 5202 entries are x==0: 4%.
## batch_counts: Before batch/surrogate estimation, 8923 entries are 0<x<1: 7%.
## The be method chose 2 surrogate variables.
## Attempting svaseq estimation with 2 surrogates.
## There are 302 (0%) elements which are < 0 after batch correction.
## Setting low elements to zero.
plot_pca(neut_norm)$plot

5.3 Eosinophils

eo <- subset_expt(hs_valid, subset="typeofcells=='Eosinophils'")
## Using a subset expression.
## There were 63, now there are 11 samples.
eo <- set_expt_conditions(eo, fact="visitnumber")
eo <- set_expt_batches(eo, fact="donor")
eo_norm <- normalize_expt(eo, transform="log2", convert="cpm", batch="svaseq", filter=TRUE)
## 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$best_libsize
## Leaving the data unnormalized.  This is necessary for DESeq, but
##  EdgeR/limma might benefit from normalization.  Good choices include quantile,
##  size-factor, tmm, etc.
## Step 1: performing count filter with option: cbcb
## Removing 45794 low-count genes (12508 remaining).
## Step 2: not normalizing the data.
## Step 3: converting the data with cpm.
## Step 4: transforming the data with log2.
## transform_counts: Found 6964 values equal to 0, adding 1 to the matrix.
## Step 5: doing batch correction with svaseq.
## Using the current state of normalization.
## Passing the data to all_adjusters using the svaseq estimate type.
## batch_counts: Before batch/surrogate estimation, 123031 entries are x>1: 89%.
## batch_counts: Before batch/surrogate estimation, 6964 entries are x==0: 5%.
## batch_counts: Before batch/surrogate estimation, 7593 entries are 0<x<1: 6%.
## The be method chose 1 surrogate variable.
## Attempting svaseq estimation with 1 surrogate.
## There are 437 (0%) elements which are < 0 after batch correction.
## Setting low elements to zero.
plot_pca(eo_norm)$plot

5.4 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 63, now there are 6 samples.
q1_norm <- normalize_expt(hs_q1, norm="quant", transform="log2", convert="cpm", batch=FALSE,
                           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$best_libsize
## Not correcting the count-data for batch effects.  If batch is
##  included in EdgerR/limma's model, then this is probably wise; but in extreme
##  batch effects this is a good parameter to play with.
## Step 1: performing count filter with option: cbcb
## Removing 44614 low-count genes (13688 remaining).
## Step 2: normalizing the data with quant.
## Step 3: converting the data with cpm.
## Step 4: transforming the data with log2.
## transform_counts: Found 22 values equal to 0, adding 1 to the matrix.
## Step 5: not doing batch correction.
q1_pca <- plot_pca(q1_norm)
q1_pca$plot

knitr::kable(q1_pca$table)
sampleid condition batch batch_int colors labels PC1 PC2 pc_1 pc_2 pc_3 pc_4 pc_5
1010-2 1010-2 PBMCs d1010 1 #1B9E77 1010-2 -0.5759 0.2704 -0.5759 0.2704 0.4004 -0.0566 0.5148
1010-7 1010-7 PBMCs d1010 1 #1B9E77 1010-7 0.0048 0.4828 0.0048 0.4828 -0.6648 0.3977 -0.0112
1010-12 1010-12 PBMCs d1010 1 #1B9E77 1010-12 0.4107 0.4514 0.4107 0.4514 0.4033 -0.3107 -0.4492
1011-2 1011-2 PBMCs d1011 2 #1B9E77 1011-2 -0.4764 -0.4166 -0.4764 -0.4166 -0.2714 -0.3745 -0.4679
1011-7 1011-7 PBMCs d1011 2 #1B9E77 1011-7 0.1313 -0.4486 0.1313 -0.4486 0.3425 0.6930 -0.1315
1011-12 1011-12 PBMCs d1011 2 #1B9E77 1011-12 0.5054 -0.3394 0.5054 -0.3394 -0.2100 -0.3488 0.5449 
write.csv(q1_pca$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 <- normalize_expt(hs_time, transform="log2",
                            batch="svaseq", filter=TRUE)
## This function will replace the expt$expressionset slot with:
## log2(svaseq(cbcb(data)))
## It will save copies of each step along the way
##  in expt$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 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 44614 low-count genes (13688 remaining).
## Step 2: not normalizing the data.
## Step 3: not converting the data.
## Step 4: transforming the data with log2.
## transform_counts: Found 113 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, 81926 entries are x>1: 100%.
## batch_counts: Before batch/surrogate estimation, 113 entries are x==0: 0%.
## The be method chose 1 surrogate variable.
## Attempting svaseq estimation with 1 surrogate.
## There are 6 (0%) elements which are < 0 after batch correction.
## Setting low elements to zero.
## 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,] "16.16:ENSG00000090382" "25.38:ENSG00000197746"
##  [2,] "13.73:ENSG00000075624" "25.27:ENSG00000198804"
##  [3,] "11.74:ENSG00000134954" "23.59:ENSG00000090382"
##  [4,] "9.364:ENSG00000100201" "23.19:ENSG00000075624"
##  [5,] "8.07:ENSG00000038427"  "20.06:ENSG00000038427"
##  [6,] "7.57:ENSG00000168685"  "18.63:ENSG00000019582"
##  [7,] "7.411:ENSG00000125538" "16.41:ENSG00000128016"
##  [8,] "7.029:ENSG00000121966" "14.33:ENSG00000124942"
##  [9,] "6.882:ENSG00000111913" "14.09:ENSG00000163131"
## [10,] "6.372:ENSG00000196924" "12.33:ENSG00000265972"
## [11,] "6.157:ENSG00000100345" "11.94:ENSG00000085265"
## [12,] "5.808:ENSG00000118515" "11.62:ENSG00000163220"
## [13,] "5.627:ENSG00000136167" "11.33:ENSG00000196924"
## [14,] "5.529:ENSG00000197629" "11.19:ENSG00000130066"
## [15,] "5.43:ENSG00000165168"  "10.93:ENSG00000121966"
## [16,] "5.202:ENSG00000159388" "10.88:ENSG00000136167"
## [17,] "5.022:ENSG00000081237" "9.705:ENSG00000210082"
## [18,] "4.791:ENSG00000128016" "9.435:ENSG00000159388"
## [19,] "4.743:ENSG00000171223" "9.296:ENSG00000125538"
## [20,] "4.726:ENSG00000152518" "8.735:ENSG00000116741"
## [21,] "4.623:ENSG00000188404" "8.497:ENSG00000211459"
## [22,] "4.572:ENSG00000081059" "8.324:ENSG00000245532"
## [23,] "4.409:ENSG00000110324" "8.174:ENSG00000165168"
## [24,] "4.405:ENSG00000122566" "8.052:ENSG00000087086"
## [25,] "4.316:ENSG00000127951" "7.82:ENSG00000081237" 
## [26,] "4.268:ENSG00000140575" "7.648:ENSG00000118515"
## [27,] "4.13:ENSG00000245532"  "7.608:ENSG00000211592"
## [28,] "4.045:ENSG00000101347" "7.603:ENSG00000197629"
## [29,] "3.878:ENSG00000109971" "6.782:ENSG00000160255"
## [30,] "3.842:ENSG00000196405" "6.652:ENSG00000184009"
## [31,] "3.793:ENSG00000185811" "6.466:ENSG00000100345"
## [32,] "3.756:ENSG00000120129" "6.402:ENSG00000171223"
## [33,] "3.677:ENSG00000130066" "6.349:ENSG00000119535"
## [34,] "3.557:ENSG00000082074" "6.297:ENSG00000137076"
## [35,] "3.544:ENSG00000073756" "6.165:ENSG00000185215"
## [36,] "3.468:ENSG00000116741" "6.085:ENSG00000152518"
## [37,] "3.441:ENSG00000184009" "6.079:ENSG00000140575"
## [38,] "3.091:ENSG00000197043" "5.692:ENSG00000135821"
## [39,] "3.049:ENSG00000160593" "5.496:ENSG00000123384"
## [40,] "3.019:ENSG00000182578" "5.28:ENSG00000000938"
## 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] "ENSG00000090382" "ENSG00000075624" "ENSG00000134954" "ENSG00000100201"
## [5] "ENSG00000038427" "ENSG00000168685"
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 <- normalize_expt(hs_donor, convert="cpm", transform="log2",
                            batch="svaseq", filter=TRUE)
## 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 44614 low-count genes (13688 remaining).
## Step 2: not normalizing the data.
## Step 3: converting the data with cpm.
## Step 4: transforming the data with log2.
## transform_counts: Found 113 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, 80526 entries are x>1: 98%.
## batch_counts: Before batch/surrogate estimation, 113 entries are x==0: 0%.
## batch_counts: Before batch/surrogate estimation, 1489 entries are 0<x<1: 2%.
## The be method chose 1 surrogate variable.
## Attempting svaseq estimation with 1 surrogate.
## There are 51 (0%) elements which are < 0 after batch correction.
## Setting low elements to zero.
## 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,] "117.6:ENSG00000090382" "11.46:ENSG00000118503"
##  [2,] "66.31:ENSG00000198804" "8.971:ENSG00000100316"
##  [3,] "42.52:ENSG00000198938" "8.813:ENSG00000167658"
##  [4,] "37.86:ENSG00000198886" "8.072:ENSG00000149273"
##  [5,] "31.64:ENSG00000198899" "8.008:ENSG00000156508"
##  [6,] "31.55:ENSG00000198763" "7.546:ENSG00000142541"
##  [7,] "29.58:ENSG00000163220" "7.511:ENSG00000112306"
##  [8,] "28.16:ENSG00000121966" "7.388:ENSG00000121966"
##  [9,] "28.09:ENSG00000198888" "7.346:ENSG00000164587"
## [10,] "27.93:ENSG00000265972" "6.672:ENSG00000198034"
## [11,] "21.72:ENSG00000118503" "6.443:ENSG00000167526"
## [12,] "17.95:ENSG00000157514" "5.733:ENSG00000107742"
## [13,] "16.67:ENSG00000143384" "5.468:ENSG00000108298"
## [14,] "16.35:ENSG00000038427" "5.435:ENSG00000174444"
## [15,] "14.46:ENSG00000129824" "5.197:ENSG00000171223"
## [16,] "13.78:ENSG00000067048" "5.165:ENSG00000063177"
## [17,] "13.29:ENSG00000152518" "5.052:ENSG00000137154"
## [18,] "12.95:ENSG00000237973" "5.001:ENSG00000167978"
## [19,] "12.19:ENSG00000026025" "4.994:ENSG00000142937"
## [20,] "11.7:ENSG00000225630"  "4.971:ENSG00000142676"
## [21,] "11.69:ENSG00000198712" "4.962:ENSG00000177600"
## [22,] "11.32:ENSG00000135046" "4.855:ENSG00000128016"
## [23,] "11.09:ENSG00000119535" "4.747:ENSG00000105372"
## [24,] "9.689:ENSG00000124942" "4.715:ENSG00000197756"
## [25,] "9.384:ENSG00000012817" "4.439:ENSG00000076928"
## [26,] "9.14:ENSG00000163131"  "4.438:ENSG00000156482"
## [27,] "8.384:ENSG00000087086" "4.317:ENSG00000178209"
## [28,] "7.99:ENSG00000128016"  "4.273:ENSG00000163346"
## [29,] "7.854:ENSG00000133112" "4.147:ENSG00000161016"
## [30,] "7.483:ENSG00000139318" "4.093:ENSG00000205542"
## [31,] "7.341:ENSG00000173821" "4.002:ENSG00000157514"
## [32,] "6.924:ENSG00000137154" "3.971:ENSG00000105193"
## [33,] "6.645:ENSG00000118515" "3.958:ENSG00000144713"
## [34,] "6.463:ENSG00000197629" "3.933:ENSG00000145592"
## [35,] "6.244:ENSG00000142102" "3.822:ENSG00000180448"
## [36,] "6.071:ENSG00000136167" "3.798:ENSG00000213741"
## [37,] "6.032:ENSG00000170345" "3.768:ENSG00000179094"
## [38,] "5.936:ENSG00000170776" "3.675:ENSG00000008988"
## [39,] "5.851:ENSG00000179344" "3.625:ENSG00000137818"
## [40,] "5.791:ENSG00000120129" "3.604:ENSG00000071082"
## 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 <- all_pairwise(q1_time, model_batch=FALSE, parallel=FALSE)
## Plotting a PCA before surrogate/batch inclusion.
## Not putting labels on the PC plot.
## Assuming no batch in model for testing pca.
## Not putting labels on the PC plot.
## Starting basic_pairwise().
## Starting basic pairwise comparison.
## Basic step 0/3: Filtering data.
## Basic step 0/3: Normalizing data.
## Basic step 0/3: Converting data.
## Basic step 0/3: Transforming data.
## Basic step 1/3: Creating mean and variance tables.
## Basic step 2/3: Performing 6 comparisons.
## Basic step 3/3: Creating faux DE Tables.
## Basic: Returning tables.
## Starting deseq_pairwise().
## Starting DESeq2 pairwise comparisons.
## The data should be suitable for EdgeR/DESeq/EBSeq. If they freak out, check the state of the count table and ensure that it is in integer counts.
## Choosing the non-intercept containing model.
## DESeq2 step 1/5: Including only condition in the deseq model.
## converting counts to integer mode
## DESeq2 step 2/5: Estimate size factors.
## DESeq2 step 3/5: Estimate dispersions.
## gene-wise dispersion estimates
## mean-dispersion relationship
## final dispersion estimates
## Using a parametric fitting seems to have worked.
## DESeq2 step 4/5: nbinomWaldTest.
## Starting ebseq_pairwise().
## The data should be suitable for EdgeR/DESeq/EBSeq. If they freak out, check the state of the count table and ensure that it is in integer counts.
## Starting EBSeq pairwise subset.
## Choosing the non-intercept containing model.
## Starting EBTest of t12hr vs. t2hr.
## Copying ppee values as ajusted p-values until I figure out how to deal with them.
## Starting EBTest of t12hr vs. t7hr.
## Copying ppee values as ajusted p-values until I figure out how to deal with them.
## Starting EBTest of t2hr vs. t7hr.
## Copying ppee values as ajusted p-values until I figure out how to deal with them.
## Starting edger_pairwise().
## Starting edgeR pairwise comparisons.
## The data should be suitable for EdgeR/DESeq/EBSeq. If they freak out, check the state of the count table and ensure that it is in integer counts.
## Choosing the non-intercept containing model.
## EdgeR step 1/9: Importing and normalizing data.
## EdgeR step 2/9: Estimating the common dispersion.
## EdgeR step 3/9: Estimating dispersion across genes.
## EdgeR step 4/9: Estimating GLM Common dispersion.
## EdgeR step 5/9: Estimating GLM Trended dispersion.
## EdgeR step 6/9: Estimating GLM Tagged dispersion.
## EdgeR step 7/9: Running glmFit, switch to glmQLFit by changing the argument 'edger_test'.
## EdgeR step 8/9: Making pairwise contrasts.
## Starting limma_pairwise().
## Starting limma pairwise comparison.
## libsize was not specified, this parameter has profound effects on limma's result.
## Using the libsize from expt$libsize.
## Limma step 1/6: choosing model.
## Choosing the non-intercept containing model.
## Limma step 2/6: running limma::voom(), switch with the argument 'which_voom'.
## Using normalize.method=quantile for voom.
## Limma step 3/6: running lmFit with method: ls.
## Limma step 4/6: making and fitting contrasts with no intercept. (~ 0 + factors)
## Limma step 5/6: Running eBayes with robust=FALSE and trend=FALSE.
## Limma step 6/6: Writing limma outputs.
## Limma step 6/6: 1/3: Creating table: t2hr_vs_t12hr.  Adjust=BH
## Limma step 6/6: 2/3: Creating table: t7hr_vs_t12hr.  Adjust=BH
## Limma step 6/6: 3/3: Creating table: t7hr_vs_t2hr.  Adjust=BH
## Limma step 6/6: 1/3: Creating table: t12hr.  Adjust=BH
## Limma step 6/6: 2/3: Creating table: t2hr.  Adjust=BH
## Limma step 6/6: 3/3: Creating table: t7hr.  Adjust=BH
## Comparing analyses.
time_all_tables <- combine_de_tables(time_de, excel=glue::glue("excel/time_de_tables-v{ver}.xlsx"))
## Deleting the file excel/time_de_tables-v202009.xlsx before writing the tables.
## Writing a legend of columns.
## Printing a pca plot before/after surrogates/batch estimation.
## Working on table 1/3: t2hr_vs_t12hr
## Working on table 2/3: t7hr_vs_t12hr
## Working on table 3/3: t7hr_vs_t2hr
## Adding venn plots for t2hr_vs_t12hr.
## Limma expression coefficients for t2hr_vs_t12hr; R^2: 0.967; equation: y = 0.985x - 0.0364
## Deseq expression coefficients for t2hr_vs_t12hr; R^2: 0.925; equation: y = 0.967x + 0.113
## Edger expression coefficients for t2hr_vs_t12hr; R^2: 0.94; equation: y = 0.968x + 0.054
## Adding venn plots for t7hr_vs_t12hr.
## Limma expression coefficients for t7hr_vs_t12hr; R^2: 0.975; equation: y = 0.986x - 0.0042
## Deseq expression coefficients for t7hr_vs_t12hr; R^2: 0.944; equation: y = 0.969x + 0.131
## Edger expression coefficients for t7hr_vs_t12hr; R^2: 0.947; equation: y = 0.968x + 0.0741
## Adding venn plots for t7hr_vs_t2hr.
## Limma expression coefficients for t7hr_vs_t2hr; R^2: 0.974; equation: y = 0.984x + 0.0049
## Deseq expression coefficients for t7hr_vs_t2hr; R^2: 0.942; equation: y = 0.961x + 0.185
## Edger expression coefficients for t7hr_vs_t2hr; R^2: 0.945; equation: y = 0.969x + 0.0932
## Writing summary information, compare_plot is: TRUE.
## Performing save of excel/time_de_tables-v202009.xlsx.
time_all_tables_all <- combine_de_tables(time_de,
                                         keepers="all",
                                         excel=glue::glue("excel/time_de_all_tables-v{ver}.xlsx"))
## Deleting the file excel/time_de_all_tables-v202009.xlsx before writing the tables.
## Writing a legend of columns.
## Printing a pca plot before/after surrogates/batch estimation.
## Working on table 1/3: t2hr_vs_t12hr
## Working on table 2/3: t7hr_vs_t12hr
## Working on table 3/3: t7hr_vs_t2hr
## Adding venn plots for t2hr_vs_t12hr.
## Limma expression coefficients for t2hr_vs_t12hr; R^2: 0.967; equation: y = 0.985x - 0.0364
## Deseq expression coefficients for t2hr_vs_t12hr; R^2: 0.925; equation: y = 0.967x + 0.113
## Edger expression coefficients for t2hr_vs_t12hr; R^2: 0.94; equation: y = 0.968x + 0.054
## Adding venn plots for t7hr_vs_t12hr.
## Limma expression coefficients for t7hr_vs_t12hr; R^2: 0.975; equation: y = 0.986x - 0.0042
## Deseq expression coefficients for t7hr_vs_t12hr; R^2: 0.944; equation: y = 0.969x + 0.131
## Edger expression coefficients for t7hr_vs_t12hr; R^2: 0.947; equation: y = 0.968x + 0.0741
## Adding venn plots for t7hr_vs_t2hr.
## Limma expression coefficients for t7hr_vs_t2hr; R^2: 0.974; equation: y = 0.984x + 0.0049
## Deseq expression coefficients for t7hr_vs_t2hr; R^2: 0.942; equation: y = 0.961x + 0.185
## Edger expression coefficients for t7hr_vs_t2hr; R^2: 0.945; equation: y = 0.969x + 0.0932
## Writing summary information, compare_plot is: TRUE.
## Performing save of excel/time_de_all_tables-v202009.xlsx.
time_all_tables <- combine_de_tables(time_de,
                                     keepers=time_keepers,
                                     excel=glue::glue("excel/{rundate}-time_de_tables-v{ver}.xlsx"))
## Deleting the file excel/20200922-time_de_tables-v202009.xlsx before writing the tables.
## Writing a legend of columns.
## Printing a pca plot before/after surrogates/batch estimation.
## Working on 1/3: 2hr_vs_7hr which is: t2hr/t7hr.
## Found inverse table with t7hr_vs_t2hr
## Working on 2/3: 2hr_vs_12hr which is: t2hr/t12hr.
## Found table with t2hr_vs_t12hr
## Working on 3/3: 7hr_vs_12hr which is: t7hr/t12hr.
## Found table with t7hr_vs_t12hr
## Adding venn plots for 2hr_vs_7hr.
## Limma expression coefficients for 2hr_vs_7hr; R^2: 0.974; equation: y = 0.984x + 0.0049
## Deseq expression coefficients for 2hr_vs_7hr; R^2: 0.942; equation: y = 0.961x + 0.185
## Edger expression coefficients for 2hr_vs_7hr; R^2: 0.945; equation: y = 0.969x + 0.0932
## Adding venn plots for 2hr_vs_12hr.
## Limma expression coefficients for 2hr_vs_12hr; R^2: 0.967; equation: y = 0.985x - 0.0364
## Deseq expression coefficients for 2hr_vs_12hr; R^2: 0.925; equation: y = 0.967x + 0.113
## Edger expression coefficients for 2hr_vs_12hr; R^2: 0.94; equation: y = 0.968x + 0.054
## Adding venn plots for 7hr_vs_12hr.
## Limma expression coefficients for 7hr_vs_12hr; R^2: 0.975; equation: y = 0.986x - 0.0042
## Deseq expression coefficients for 7hr_vs_12hr; R^2: 0.944; equation: y = 0.969x + 0.131
## Edger expression coefficients for 7hr_vs_12hr; R^2: 0.947; equation: y = 0.968x + 0.0741
## Writing summary information, compare_plot is: TRUE.
## Performing save of excel/20200922-time_de_tables-v202009.xlsx.
q1_donor <- set_expt_conditions(hs_q1, fact="donor")
donor_de <- all_pairwise(q1_donor, model_batch=FALSE)
## Plotting a PCA before surrogate/batch inclusion.
## Not putting labels on the PC plot.
## Assuming no batch in model for testing pca.
## Not putting labels on the PC plot.
## Finished running DE analyses, collecting outputs.
## Comparing analyses.
donor_tables <- combine_de_tables(donor_de, excel=glue::glue("excel/donor_de_tables-v{ver}.xlsx"))
## Deleting the file excel/donor_de_tables-v202009.xlsx before writing the tables.
## Writing a legend of columns.
## Printing a pca plot before/after surrogates/batch estimation.
## Working on table 1/1: d1011_vs_d1010
## Adding venn plots for d1011_vs_d1010.
## Limma expression coefficients for d1011_vs_d1010; R^2: 0.975; equation: y = 0.989x - 0.0403
## Deseq expression coefficients for d1011_vs_d1010; R^2: 0.953; equation: y = 0.959x + 0.196
## Edger expression coefficients for d1011_vs_d1010; R^2: 0.951; equation: y = 0.979x + 0.0198
## Writing summary information, compare_plot is: TRUE.
## Performing save of excel/donor_de_tables-v202009.xlsx.
hs_q2 <- subset_expt(hs_valid, subset="donor!='d1010'&donor!='d1011'")
## Using a subset expression.
## There were 63, now there are 56 samples.
q2_norm <- normalize_expt(hs_q2, transform="log2", convert="cpm", norm="quant", filter=TRUE)
## This function will replace the expt$expressionset slot with:
## log2(cpm(quant(cbcb(data))))
## It will save copies of each step along the way
##  in expt$normalized with the corresponding libsizes. Keep libsizes in mind
##  when invoking limma.  The appropriate libsize is non-log(cpm(normalized)).
##  This is most likely kept at:
##  'new_expt$normalized$intermediate_counts$normalization$libsizes'
##  A copy of this may also be found at:
##  new_expt$best_libsize
## Not correcting the count-data for batch effects.  If batch is
##  included in EdgerR/limma's model, then this is probably wise; but in extreme
##  batch effects this is a good parameter to play with.
## Step 1: performing count filter with option: cbcb
## Removing 40023 low-count genes (18279 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 6102 values equal to 0, adding 1 to the matrix.
## Step 5: not doing batch correction.
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
1034n1 1034n1 Neutrophils d1034 1 #D95F02 1034n1 -0.1948 -0.0374 -0.1948 -0.0374 -0.1797 -0.0022 -0.0923 0.0805 -0.3242 0.1119 -0.0121 -0.0047 0.0029 -0.1226 0.3067 -0.0960 0.0230 -0.0225 0.1560 -0.0128 0.0599 -0.0553 0.1779 -0.1145 0.4150 -0.0558 -0.0230 -0.0455 -0.0501 -0.1480 -0.4143 0.3231 -0.2237 -0.0448 -0.0370 -0.0307 -0.1689 -0.0764 0.0041 0.0716 0.0441 0.0184 0.0518 0.0261 -0.0600 -0.0095 -0.0075 -0.0254 -0.0006 -0.0008 -0.0018 -0.0078 -0.0077 -0.0013 -0.0020 -0.0039
1034n2 1034n2 Neutrophils d1034 1 #D95F02 1034n2 -0.1973 -0.0392 -0.1973 -0.0392 -0.2084 0.0052 -0.0925 0.0306 -0.3471 0.2037 0.0195 -0.0418 0.0628 -0.1344 0.3088 -0.1834 -0.1003 0.0916 0.1862 0.1386 0.1569 -0.1155 -0.1732 -0.0763 -0.1382 -0.1440 0.0510 0.0703 -0.0045 0.0960 0.2358 -0.4653 0.1856 0.2165 0.0246 0.0118 0.0810 -0.0113 -0.0173 -0.0681 -0.0175 -0.0360 0.0180 0.0458 0.0190 0.0128 0.0129 0.0144 0.0094 -0.0053 -0.0027 0.0009 0.0041 -0.0053 0.0005 -0.0010
1034m2 1034m2 Monocytes d1034 1 #7570B3 1034m2 -0.0069 -0.0449 -0.0069 -0.0449 0.0256 -0.2526 0.0782 0.2256 -0.3239 -0.0592 -0.0544 -0.0760 0.0144 -0.0083 0.0333 0.1253 0.2885 -0.1544 -0.2072 -0.0269 0.0085 -0.0016 -0.0045 0.1023 -0.1368 0.1685 -0.0150 -0.0030 -0.0199 -0.0799 0.1504 0.0909 0.0489 -0.0330 -0.0051 -0.0249 -0.0080 -0.0298 0.0575 0.0208 0.0403 0.0286 0.0158 0.0499 0.0451 -0.2081 0.6176 0.1105 -0.1265 0.0819 -0.0465 0.0118 -0.0066 0.0049 0.0013 0.0071
1034m2- 1034m2- Monocytes d1034 1 #7570B3 1034m2- -0.0127 -0.0454 -0.0127 -0.0454 0.0331 -0.2448 0.0703 0.2241 -0.3291 -0.0843 -0.0358 -0.0987 0.0170 0.0638 0.0485 0.0769 0.2508 -0.1399 -0.2494 -0.0390 0.0407 0.0371 0.0203 0.0472 -0.1661 0.0971 0.0151 0.0477 -0.0482 -0.0117 0.0843 0.0619 0.0130 -0.0390 -0.0151 -0.0029 0.0121 0.1019 -0.0062 0.0547 -0.0305 -0.0496 -0.0567 -0.0369 0.0044 0.1667 -0.6574 -0.0790 0.1102 -0.1173 0.0630 0.0014 -0.0063 0.0019 -0.0134 -0.0074
2050bp1 2050bp1 Biopsy d2050 2 #E7298A 2050bp1 0.1436 -0.1823 0.1436 -0.1823 -0.0106 0.0779 0.0234 -0.0898 -0.0107 0.2247 -0.1399 -0.1446 -0.3779 0.2371 -0.1070 -0.0641 0.2568 0.3061 0.0778 -0.1360 0.2687 -0.0411 -0.1109 -0.1784 -0.0280 0.2719 -0.1833 0.0182 0.1103 -0.0440 -0.0734 -0.0387 -0.1044 0.0768 -0.0829 0.3712 0.0251 0.0259 0.0007 -0.0046 0.0375 0.0577 0.0161 -0.0080 -0.0200 0.0675 0.0266 -0.0075 0.0054 0.0063 0.0154 -0.0131 -0.0434 -0.0113 0.0016 0.0035
2052bp1 2052bp1 Biopsy d2052 3 #E7298A 2052bp1 0.1498 -0.2043 0.1498 -0.2043 -0.0197 0.1211 0.0385 -0.2114 -0.1280 -0.1195 0.1132 0.1084 0.2633 0.0091 -0.0747 0.1189 0.0516 -0.0081 0.0710 0.1136 -0.0821 0.0397 -0.1109 -0.3298 0.0502 0.2409 -0.1254 0.3185 0.2775 -0.2027 0.0532 -0.0935 -0.2205 0.0164 0.2406 -0.3620 -0.0130 0.0837 0.0153 0.0606 -0.0475 -0.0193 0.0201 0.0149 0.0426 -0.0056 -0.0070 -0.0083 0.0062 -0.0200 -0.0036 -0.0133 0.0010 0.0044 0.0101 0.0026
2052e1 2052e1 Eosinophils d2052 3 #66A61E 2052e1 -0.0620 0.1427 -0.0620 0.1427 0.1961 0.2141 0.1475 0.0438 0.0548 0.0027 -0.1374 0.1200 0.0207 -0.3013 0.0427 0.1654 0.1217 0.2760 -0.1657 0.0923 0.0703 -0.1726 -0.1578 -0.2062 0.3273 0.1532 0.3493 -0.0953 -0.0993 0.1403 0.3180 0.1633 -0.0226 0.0251 -0.0545 -0.0111 0.0954 -0.0160 -0.0551 0.0810 0.0467 -0.0420 -0.0698 0.0305 -0.0200 0.0328 -0.0471 0.0347 -0.0421 0.0656 -0.0078 0.0198 0.0075 -0.0163 -0.0208 -0.0088
2052n2 2052n2 Neutrophils d2052 3 #D95F02 2052n2 -0.1189 0.1304 -0.1189 0.1304 -0.1903 0.1989 0.2495 0.1197 0.1226 -0.1527 0.4076 -0.3661 -0.0490 -0.0389 -0.0292 0.2480 0.0436 0.2024 -0.0852 0.3848 0.2677 0.2119 0.2076 0.0711 -0.0239 -0.0240 -0.0714 0.0134 -0.0101 -0.0472 -0.0956 -0.0766 0.0152 0.0173 -0.0076 -0.0123 -0.0351 -0.0429 0.0071 -0.0328 0.0262 0.0073 0.0149 -0.0059 0.0033 -0.0331 0.0047 -0.0100 0.0037 -0.0029 -0.0010 0.0072 0.0001 0.0045 0.0012 0.0026
2065bp1 2065bp1 Biopsy d2065 4 #E7298A 2065bp1 0.1514 -0.1988 0.1514 -0.1988 -0.0400 0.1034 0.0496 -0.3368 -0.1933 -0.1744 0.1977 0.1528 0.2506 0.0365 -0.0633 0.1234 -0.0411 0.1303 0.0257 -0.3490 0.3323 -0.1651 0.1435 0.3322 0.0394 -0.1218 0.1454 0.1641 -0.0326 0.0425 0.0118 0.1265 0.2174 0.0288 -0.1446 0.0989 -0.0525 0.0224 -0.0090 0.0184 0.0038 -0.0239 -0.0218 0.0044 -0.0046 0.0170 0.0173 0.0188 0.0045 0.0051 -0.0013 0.0010 -0.0066 -0.0101 0.0000 -0.0027
2066bp1 2066bp1 Biopsy d2066 5 #E7298A 2066bp1 0.1493 -0.2051 0.1493 -0.2051 -0.0359 0.1136 0.0289 -0.0834 -0.0738 -0.0874 0.1066 0.1344 0.2449 -0.0781 0.0202 0.0961 -0.0186 -0.1430 -0.0267 0.0849 -0.0677 0.1030 -0.1121 -0.2001 -0.0954 0.0827 -0.1333 -0.5965 -0.2425 0.1442 -0.1188 -0.1615 -0.0523 -0.0581 -0.1710 0.1950 -0.1769 0.2417 0.0018 -0.0582 0.0072 0.0294 -0.0092 -0.0371 -0.0281 0.0231 0.0263 -0.0042 0.0026 0.0212 -0.0150 -0.0040 0.0044 0.0010 -0.0077 -0.0001
2068m1 2068m1 Monocytes d2068 6 #7570B3 2068m1 -0.0042 -0.0647 -0.0042 -0.0647 0.0647 -0.2726 0.1514 -0.1798 0.2112 0.1115 0.1400 -0.0402 0.0195 -0.0993 0.2098 -0.1189 -0.0690 0.1227 -0.0179 -0.0130 -0.0840 -0.2294 0.1932 -0.0516 0.0174 0.2151 -0.2108 -0.0089 -0.1616 -0.0398 0.2244 -0.0213 0.1627 -0.3315 0.1643 0.0437 -0.2699 -0.2489 0.2574 -0.0461 -0.0764 -0.0090 0.1320 -0.0324 0.0632 0.0839 -0.0539 -0.0166 -0.0049 0.0130 -0.0085 -0.0198 -0.0137 -0.0062 -0.0004 0.0099
2068n1 2068n1 Neutrophils d2068 6 #D95F02 2068n1 -0.1901 -0.0495 -0.1901 -0.0495 -0.1401 -0.0170 -0.0614 -0.1513 0.2087 0.0158 -0.0665 0.1093 -0.0279 0.0056 0.0197 -0.0218 0.1989 -0.1787 -0.2026 -0.0099 0.0384 -0.0778 0.2251 -0.0782 0.2110 -0.1650 -0.1821 -0.1372 0.1063 -0.2672 0.1407 -0.0279 0.1290 0.2491 -0.0830 -0.0150 0.1992 0.1372 0.0779 -0.0764 -0.1574 -0.0520 -0.2211 -0.4125 0.1549 -0.0248 0.0257 -0.0416 -0.0296 -0.0103 0.0199 0.0016 -0.0066 0.0059 -0.0025 -0.0067
2068e1 2068e1 Eosinophils d2068 6 #66A61E 2068e1 -0.1241 -0.0397 -0.1241 -0.0397 0.2479 0.0236 -0.1400 -0.0725 0.1169 -0.0132 0.1474 -0.0501 -0.0290 0.0435 0.0853 -0.1044 0.0497 -0.1966 -0.0261 0.0753 0.1008 -0.2663 0.1749 -0.0389 -0.1357 -0.0026 0.0489 -0.0586 0.2276 -0.0313 0.0424 -0.1384 -0.1088 -0.2400 -0.1822 -0.0279 -0.0436 -0.0739 -0.4396 0.2196 0.0337 0.1907 -0.2653 0.1810 -0.0747 -0.0509 0.0374 -0.0527 0.1841 0.1181 0.0831 0.0129 0.0192 0.0056 -0.0017 0.0084
2068bp1 2068bp1 Biopsy d2068 6 #E7298A 2068bp1 0.1469 -0.1962 0.1469 -0.1962 -0.0212 0.0841 0.0009 0.2317 0.1729 0.3203 -0.0461 -0.0259 -0.0482 0.0800 -0.0335 -0.0221 -0.0059 0.0173 -0.0078 -0.0176 0.1024 0.0024 -0.0279 -0.1617 -0.0833 0.0094 0.0410 0.0510 -0.1479 0.0934 -0.2062 0.1249 0.4801 -0.0466 -0.1446 -0.5116 -0.0989 0.1362 -0.0724 0.1031 -0.1117 0.0014 -0.0227 -0.0208 0.0296 0.0002 0.0284 0.0314 0.0036 0.0014 -0.0127 -0.0208 0.0234 -0.0093 -0.0002 -0.0178
2072n1 2072n1 Neutrophils d2072 8 #D95F02 2072n1 -0.1991 -0.0433 -0.1991 -0.0433 -0.1570 -0.0134 -0.0970 0.0651 0.1829 -0.2062 -0.1483 0.1856 0.0033 0.0381 -0.0011 0.0459 0.1550 0.0594 0.0519 -0.1347 -0.1106 0.0349 0.3795 -0.0720 -0.0286 0.1669 0.0741 -0.0678 0.0837 0.0058 -0.0292 -0.1702 0.1328 0.0628 -0.0586 -0.0333 0.1598 0.0892 -0.0051 -0.0492 0.0447 -0.1530 0.4267 0.3771 -0.2485 0.0012 -0.0415 0.0363 0.0173 0.0494 -0.0073 0.0073 -0.0021 -0.0046 -0.0009 0.0051
2072e1 2072e1 Eosinophils d2072 8 #66A61E 2072e1 -0.1027 -0.0497 -0.1027 -0.0497 0.2099 0.0079 -0.1356 0.1052 0.1152 -0.2888 0.1691 0.0026 0.1401 0.5573 0.2548 -0.3894 -0.0691 0.0706 -0.1069 0.0826 0.1082 0.1353 -0.1807 -0.0393 0.1363 0.0580 0.1430 -0.0284 -0.0516 0.0353 0.0661 0.1304 0.0151 0.0929 0.0461 0.0024 0.0258 0.0546 0.1448 -0.0294 0.0729 0.0558 0.0827 -0.0394 0.0201 -0.0259 0.0605 -0.0243 0.0235 -0.0498 0.0317 0.0045 0.0015 -0.0010 0.0018 -0.0106
2072bp1 2072bp1 Biopsy d2072 8 #E7298A 2072bp1 0.1470 -0.1908 0.1470 -0.1908 -0.0343 0.0783 -0.0098 0.3013 0.1428 0.1558 -0.0603 -0.0338 -0.0888 0.0048 0.0022 -0.0105 -0.0850 0.0387 -0.0018 -0.1380 0.0719 -0.0309 0.1356 0.3025 0.1253 -0.1621 0.1004 -0.0852 0.0687 -0.0244 0.2834 -0.1746 -0.4129 -0.0228 0.1423 -0.1244 -0.3084 0.3451 0.1076 -0.0096 0.0446 -0.0860 0.0116 0.0430 0.0526 -0.0293 0.0078 -0.0064 0.0203 0.0004 -0.0178 0.0191 0.0026 0.0126 -0.0113 0.0121
2071bp1 2071bp1 Biopsy d2071 7 #E7298A 2071bp1 0.1391 -0.1964 0.1391 -0.1964 -0.0284 0.0836 -0.0107 0.3971 0.1990 0.1944 0.0779 0.1393 0.2559 -0.0753 0.0188 0.0704 -0.1222 -0.1725 0.0225 0.1399 -0.1554 0.0125 -0.0001 -0.0428 0.0121 0.0170 0.0374 0.3478 0.0639 -0.1909 0.0496 0.1024 0.0690 0.0478 -0.1917 0.4816 0.0365 -0.1108 -0.0312 -0.0946 0.0824 0.0358 0.0245 0.0026 -0.0017 -0.0379 -0.0380 -0.0184 -0.0042 -0.0082 0.0054 0.0037 0.0246 0.0133 -0.0070 0.0064
2073m1 2073m1 Monocytes d2073 9 #7570B3 2073m1 0.1625 -0.2026 0.1625 -0.2026 -0.0214 0.1136 0.0270 -0.1409 -0.0731 -0.0852 -0.1060 -0.1057 -0.1435 0.0640 0.0551 -0.0632 0.1457 0.0482 0.0393 0.2786 -0.3629 -0.0633 0.0586 0.1665 0.0313 -0.1353 0.0837 0.0156 -0.0301 0.0864 -0.0032 0.0388 0.0448 0.0062 -0.0121 0.0001 0.0380 -0.0588 -0.0077 0.0082 -0.0182 -0.0140 -0.0113 -0.0003 -0.0042 0.0038 -0.0035 0.0007 0.0027 -0.0013 0.0047 0.0012 -0.0038 -0.0037 0.0030 -0.0045
2073e1 2073e1 Eosinophils d2073 9 #66A61E 2073e1 -0.0550 0.1364 -0.0550 0.1364 0.2019 0.1989 0.1544 -0.0321 -0.0304 0.0829 -0.1193 0.1275 0.0804 0.0371 0.1318 -0.1308 -0.0179 0.0858 -0.1511 -0.0316 -0.0366 0.0866 0.0930 0.0724 -0.1459 -0.0859 -0.1370 0.0032 0.1087 -0.0918 -0.0873 -0.0829 -0.0163 -0.0652 -0.0407 0.0096 -0.0377 -0.0049 -0.0978 0.1038 0.0314 0.0182 -0.0415 0.0681 0.0006 0.0607 -0.0618 0.1342 -0.5672 -0.3634 -0.3468 -0.0095 -0.0411 -0.0231 0.0114 0.0244
2073bp1 2073bp1 Biopsy d2073 9 #E7298A 2073bp1 0.1377 -0.1872 0.1377 -0.1872 -0.0353 0.0678 0.0052 0.1560 0.0215 0.1807 0.0978 0.1226 0.0934 0.0896 -0.0416 0.0651 0.0222 -0.0299 -0.0433 -0.1017 0.1297 -0.0241 0.0096 0.0868 -0.0259 -0.0827 -0.0517 -0.3172 -0.0030 0.1048 -0.0485 0.0038 -0.1443 -0.0124 0.3460 -0.1245 0.4540 -0.5310 -0.0083 -0.0281 -0.0086 0.0140 0.0008 0.0069 -0.0504 -0.0254 -0.0588 -0.0246 -0.0301 -0.0001 0.0201 0.0169 0.0153 0.0046 0.0064 0.0036
2068m2 2068m2 Monocytes d2068 6 #7570B3 2068m2 -0.0079 -0.0554 -0.0079 -0.0554 0.0557 -0.2720 0.1620 -0.1573 -0.0076 0.1754 0.0420 -0.1450 0.0232 -0.0085 -0.0431 0.0054 -0.1480 -0.0103 -0.1119 -0.0373 -0.0761 0.1916 0.0875 -0.1246 0.0623 -0.0602 0.2055 -0.1169 0.2250 0.0845 -0.0328 0.1616 0.1733 0.2544 0.4320 0.1701 -0.1474 0.1321 -0.3934 -0.1257 -0.0113 -0.0033 0.0181 0.0709 -0.0299 -0.0048 0.0271 -0.1060 -0.0582 0.0491 -0.0150 -0.0101 -0.0144 0.0050 0.0070 0.0024
2068n2 2068n2 Neutrophils d2068 6 #D95F02 2068n2 -0.2008 -0.0452 -0.2008 -0.0452 -0.1766 -0.0026 -0.0600 -0.2073 -0.0022 0.2156 -0.1014 -0.0633 0.0161 0.0532 -0.1376 0.0086 -0.2233 -0.1755 -0.3719 0.1359 0.0313 0.0118 -0.2668 0.3529 0.3069 0.2822 -0.0288 0.0773 0.0225 0.0618 -0.1190 -0.1403 0.0014 -0.2505 -0.0806 -0.0191 0.1278 0.0706 -0.0691 -0.0895 -0.0080 -0.0234 0.1243 0.0693 -0.0234 0.0153 -0.0119 0.0224 -0.0034 0.0033 0.0037 -0.0001 -0.0001 0.0009 -0.0012 -0.0015
2068e2 2068e2 Eosinophils d2068 6 #66A61E 2068e2 -0.1200 -0.0392 -0.1200 -0.0392 0.2539 0.0276 -0.1064 -0.2229 -0.0795 0.2922 0.1089 -0.2137 -0.0323 -0.0315 -0.1210 0.0865 -0.1455 -0.2214 0.0166 -0.0086 -0.0246 0.1652 0.2388 -0.1572 -0.0942 -0.0159 0.1469 0.0428 0.0244 0.2265 0.0754 0.0952 -0.1182 0.1136 -0.1680 -0.0002 0.1585 0.0347 0.5055 0.1501 0.0472 0.0081 -0.0092 0.1039 -0.0416 -0.0229 0.0198 -0.0403 -0.0072 -0.0974 -0.0185 -0.0106 0.0113 -0.0141 -0.0065 -0.0014
2072m2 2072m2 Monocytes d2072 8 #7570B3 2072m2 -0.0203 -0.0552 -0.0203 -0.0552 0.0539 -0.2708 0.1209 0.0583 -0.0202 -0.0641 -0.0263 -0.0288 0.0056 0.0559 -0.1315 0.0694 -0.0898 0.1319 -0.0039 -0.0760 -0.0825 0.1116 -0.1164 -0.0712 0.1450 -0.1624 0.1060 -0.0459 0.2303 -0.0212 -0.0156 -0.1946 -0.0338 0.0184 -0.2646 -0.0799 -0.1746 -0.2515 0.0738 -0.0371 -0.1450 0.1070 -0.0240 -0.2513 -0.4449 0.0021 -0.0264 0.3820 0.0896 -0.0209 -0.0401 0.0217 0.0422 -0.0035 -0.0121 -0.0044
2072n2 2072n2 Neutrophils d2072 8 #D95F02 2072n2 -0.2102 -0.0404 -0.2102 -0.0404 -0.1935 -0.0005 -0.1045 0.0773 0.0232 -0.0979 -0.1550 0.0813 0.0375 0.0399 -0.0912 0.0622 -0.1495 0.1451 0.0582 -0.0629 -0.1345 0.0488 0.1242 0.0544 -0.1487 0.3185 0.2102 0.0069 0.0424 0.1814 -0.1378 -0.1318 0.0009 0.0288 0.0893 0.0526 -0.1535 -0.1651 -0.0045 0.3407 0.2276 -0.0566 -0.1864 -0.4003 0.2181 -0.0631 0.0248 -0.1218 -0.0107 -0.0361 0.0018 0.0150 0.0099 0.0187 -0.0011 0.0052
2072e2 2072e2 Eosinophils d2072 8 #66A61E 2072e2 -0.1364 -0.0357 -0.1364 -0.0357 0.2362 0.0443 -0.1685 0.1066 -0.0945 -0.0949 0.0237 -0.0439 -0.0632 0.1033 -0.1733 0.0739 -0.1071 0.0215 0.1471 -0.0538 -0.0600 0.1075 0.0226 -0.0088 0.1581 0.0674 -0.1890 0.0536 -0.2266 -0.0074 0.0347 -0.0598 0.0295 0.0311 0.0085 0.0531 -0.0869 -0.1403 -0.1165 -0.1319 -0.2909 -0.5450 -0.3549 0.2016 0.0358 -0.0162 0.0217 0.0201 -0.0391 0.0370 -0.0293 -0.0033 -0.0085 -0.0032 -0.0164 -0.0010
2073m2 2073m2 Monocytes d2073 9 #7570B3 2073m2 0.0361 0.1227 0.0361 0.1227 0.0234 -0.0747 0.3257 -0.0087 -0.0447 -0.0128 -0.1310 0.2171 -0.0776 0.0939 -0.1652 -0.0979 -0.1126 -0.2370 0.2653 0.2429 0.1938 -0.0079 0.0677 0.0190 0.0538 -0.0046 -0.0327 -0.1069 0.0559 -0.0075 0.0543 0.0808 0.0463 0.0147 0.0212 0.0429 0.0257 -0.0092 -0.0366 0.1531 0.0971 -0.1324 0.1207 0.0607 0.3125 0.0793 0.0172 0.3894 0.3090 -0.1756 -0.0116 -0.0071 -0.0728 -0.0580 0.0066 -0.0059
2073n2 2073n2 Neutrophils d2073 9 #D95F02 2073n2 -0.1172 0.1370 -0.1172 0.1370 -0.1852 0.1913 0.2542 0.0423 -0.0139 0.0074 0.2305 -0.1814 -0.0537 0.0938 -0.0603 -0.0874 -0.0652 -0.1435 0.1227 -0.2463 -0.2130 -0.2079 -0.2532 -0.0386 -0.0966 -0.0572 -0.0433 -0.0976 0.0973 -0.0662 0.1595 0.1824 0.0014 -0.0986 -0.0583 0.0255 0.0694 0.0719 -0.0766 0.2464 -0.0337 -0.3333 0.1996 -0.1608 -0.2183 -0.0323 0.0436 -0.1715 -0.0430 -0.0310 0.0262 -0.0076 0.0005 -0.0022 0.0014 -0.0036
2073e2 2073e2 Eosinophils d2073 9 #66A61E 2073e2 -0.0574 0.1392 -0.0574 0.1392 0.1980 0.2064 0.1533 -0.0037 -0.1254 0.1167 -0.1113 0.0979 0.0792 0.0265 0.0737 -0.0949 -0.0738 0.1119 -0.1556 -0.0713 -0.0968 0.1885 0.0673 0.0686 -0.1352 -0.1079 -0.1838 0.0667 0.0255 -0.0550 -0.0884 -0.0129 -0.0073 0.0028 0.0278 0.0135 0.0266 0.0365 0.0664 0.0789 0.0371 -0.0340 0.0747 -0.0904 0.0602 0.1027 -0.0415 0.1144 0.1233 0.7128 0.0997 -0.0039 0.0702 0.0441 0.0333 -0.0080
2068m3 2068m3 Monocytes d2068 6 #7570B3 2068m3 -0.0260 -0.0573 -0.0260 -0.0573 0.0492 -0.2713 0.1173 0.0412 0.1212 -0.0914 0.0354 0.0226 0.0053 -0.0620 -0.0201 0.0148 -0.0166 0.1350 -0.0821 -0.0026 -0.0200 -0.2320 -0.0790 -0.1376 0.0160 -0.2642 -0.0570 0.1929 -0.1727 0.0676 -0.3224 -0.1359 -0.0291 -0.1728 0.2310 0.1316 0.2877 0.3218 0.1297 0.1421 0.2163 -0.1223 -0.2210 0.0453 -0.1239 -0.1134 0.0564 0.1049 0.0470 -0.0416 -0.0061 0.0395 -0.0006 -0.0009 -0.0089 0.0024
2068n3 2068n3 Neutrophils d2068 6 #D95F02 2068n3 -0.2086 -0.0418 -0.2086 -0.0418 -0.1802 0.0035 -0.0854 -0.1069 0.1234 -0.0291 -0.1132 0.1101 -0.0221 0.0499 -0.2202 0.0433 -0.0452 -0.0088 -0.2468 0.1215 0.0349 -0.2175 -0.2060 -0.0242 -0.2597 -0.1859 -0.1078 0.0176 -0.2093 -0.1348 0.0279 0.1934 -0.1158 0.3599 0.0618 -0.0398 -0.3325 -0.1826 0.0822 0.0998 0.0802 0.0771 -0.0029 0.2679 -0.0974 0.0545 -0.0320 -0.0021 0.0214 0.0207 -0.0114 0.0020 0.0105 -0.0085 0.0063 0.0031
2068e3 2068e3 Eosinophils d2068 6 #66A61E 2068e3 -0.1360 -0.0299 -0.1360 -0.0299 0.2543 0.0404 -0.1604 0.0077 0.0074 -0.0510 0.0483 -0.0489 -0.0730 -0.0347 -0.1227 0.0850 0.0621 -0.1250 0.0181 0.0340 0.0820 -0.2668 0.0325 -0.1012 -0.1693 -0.0870 0.1480 0.1501 -0.1490 0.1199 -0.1515 -0.0223 -0.2026 -0.0320 0.0463 -0.0172 -0.0379 0.0051 -0.1761 -0.3465 -0.0591 -0.0603 0.4187 -0.3755 0.1445 0.0864 0.0153 0.1252 -0.1134 0.0233 -0.0650 -0.0079 -0.0309 0.0089 0.0125 0.0057
2072m3 2072m3 Monocytes d2072 8 #7570B3 2072m3 -0.0265 -0.0540 -0.0265 -0.0540 0.0391 -0.2696 0.1151 0.0625 -0.0240 -0.1087 0.0157 0.0480 -0.0488 0.0013 -0.1538 0.0520 -0.0687 0.1699 0.0043 -0.0517 -0.0617 0.0363 -0.1421 -0.1035 0.1017 -0.2796 0.0107 -0.0647 0.1549 -0.0306 -0.0532 -0.1000 -0.0371 -0.0968 -0.3238 -0.0782 0.0540 -0.1064 0.0279 -0.0153 0.0285 0.0440 0.1424 0.1903 0.4782 0.1049 0.0258 -0.4186 -0.1409 0.0552 0.0582 -0.0430 -0.0010 -0.0002 0.0245 -0.0059
2072n3 2072n3 Neutrophils d2072 8 #D95F02 2072n3 -0.2143 -0.0400 -0.2143 -0.0400 -0.2139 0.0098 -0.0996 0.0525 -0.0879 -0.0369 -0.0431 0.1031 -0.0256 0.0137 -0.0981 -0.0044 -0.2240 0.2773 0.1155 0.0647 -0.0569 0.0125 0.0464 -0.0296 -0.2819 0.0080 0.0023 -0.0796 0.1351 0.0021 0.1554 0.3842 -0.0597 -0.3259 0.0173 0.0063 0.1720 0.2047 0.0274 -0.2926 -0.2171 0.2167 -0.1903 0.0216 0.0283 0.0013 -0.0029 0.1304 0.0066 -0.0098 -0.0044 -0.0105 -0.0167 -0.0066 -0.0039 -0.0014
2072e3 2072e3 Eosinophils d2072 8 #66A61E 2072e3 -0.1309 -0.0343 -0.1309 -0.0343 0.2418 0.0343 -0.1613 0.0853 -0.1217 -0.0373 0.0452 -0.0290 -0.0909 -0.0409 -0.1991 0.1585 -0.0934 0.0611 0.2165 -0.0537 -0.1204 0.0620 -0.0017 0.1190 0.2179 -0.0204 -0.3188 -0.0270 -0.2283 -0.1274 0.1975 -0.0333 0.1970 0.0533 0.1282 -0.0284 0.0808 0.1113 -0.1447 0.1720 0.2040 0.4440 0.1635 -0.0975 -0.0899 -0.0159 -0.0414 -0.0508 0.0076 -0.0854 0.0098 0.0047 -0.0064 0.0003 0.0163 0.0035
2159bp1 2159bp1 Biopsy d2159 10 #E7298A 2159bp1 0.1625 -0.2026 0.1625 -0.2026 -0.0214 0.1136 0.0270 -0.1409 -0.0731 -0.0852 -0.1060 -0.1057 -0.1435 0.0640 0.0551 -0.0632 0.1457 0.0482 0.0393 0.2786 -0.3629 -0.0633 0.0586 0.1665 0.0313 -0.1353 0.0837 0.0156 -0.0301 0.0864 -0.0032 0.0388 0.0448 0.0062 -0.0121 0.0001 0.0380 -0.0588 -0.0077 0.0082 -0.0182 -0.0140 -0.0113 -0.0003 -0.0042 0.0038 -0.0035 0.0007 0.0027 -0.0013 0.0047 0.0012 -0.0038 -0.0037 0.0030 -0.0045
2073m3 2073m3 Monocytes d2073 9 #7570B3 2073m3 0.0326 0.1286 0.0326 0.1286 0.0381 -0.0793 0.3241 0.0184 -0.0078 -0.0634 -0.2184 0.1798 -0.0265 0.1112 -0.0964 -0.0676 -0.0538 -0.2159 0.2864 0.2020 0.2107 -0.0857 0.0787 0.0722 0.0095 0.1100 0.0387 0.0871 -0.0994 0.0772 -0.0289 -0.0518 -0.0384 0.0319 0.0138 -0.0458 -0.0184 0.0439 0.0971 -0.1440 -0.1063 0.1443 -0.1207 -0.0690 -0.3172 -0.0764 -0.0221 -0.4074 -0.2526 0.1709 0.0352 -0.0004 0.0727 0.0537 -0.0156 0.0054
2073n3 2073n3 Neutrophils d2073 9 #D95F02 2073n3 -0.1125 0.1399 -0.1125 0.1399 -0.1536 0.1795 0.2418 0.0301 0.0757 -0.0382 0.0785 -0.1685 0.0065 0.1238 0.0156 0.0163 0.1257 -0.2275 0.1081 -0.3795 -0.2367 -0.0959 -0.0610 -0.0557 0.1082 0.0786 0.1266 0.0688 -0.1146 0.0725 -0.0809 -0.1417 0.0037 0.0786 0.0596 -0.0227 -0.0278 -0.0342 0.0686 -0.2452 0.0296 0.3494 -0.2296 0.1902 0.2334 0.0797 -0.0534 0.2048 0.0032 0.0443 -0.0393 -0.0010 0.0010 0.0008 -0.0005 0.0049
2073e3 2073e3 Eosinophils d2073 9 #66A61E 2073e3 -0.0637 0.1451 -0.0637 0.1451 0.2006 0.2129 0.1492 0.0029 -0.0544 0.0684 -0.1611 0.0997 0.0600 -0.0730 0.0615 0.0000 0.0327 0.1174 -0.1491 -0.1117 -0.0804 0.0348 0.0048 0.0485 -0.0983 -0.0407 -0.0531 0.0432 0.0193 -0.0169 -0.1444 -0.0672 0.0104 0.0029 0.0367 -0.0054 -0.0843 -0.0414 0.0366 -0.2210 -0.1293 0.0312 0.0473 -0.0173 -0.0387 -0.2039 0.1572 -0.2784 0.4743 -0.4234 0.2423 -0.0046 -0.0398 -0.0083 -0.0250 -0.0089
2162m1 2162m1 Monocytes d2162 11 #7570B3 2162m1 0.0001 -0.0628 0.0001 -0.0628 0.0579 -0.2750 0.1418 -0.0963 0.2187 0.0398 0.1062 -0.0309 0.0486 -0.2242 0.2081 -0.1019 -0.0379 0.1337 0.1567 -0.0694 -0.0837 0.0951 -0.0784 0.2649 -0.1496 0.2460 -0.0922 0.0331 -0.1820 -0.0072 -0.0499 0.1210 -0.2956 0.3634 -0.2544 -0.1747 0.2149 0.0648 -0.2036 -0.0080 -0.0413 -0.0187 -0.0209 -0.0226 0.0349 -0.0296 -0.0040 0.0508 0.0523 -0.0223 -0.0217 0.0020 -0.0101 0.0010 0.0025 -0.0002
2162n1 2162n1 Neutrophils d2162 11 #D95F02 2162n1 -0.1781 -0.0555 -0.1781 -0.0555 -0.1270 -0.0244 -0.0366 -0.1594 0.2184 0.0405 -0.0847 0.1052 0.0399 -0.0676 0.0793 0.0188 0.3902 -0.1230 0.1698 -0.0708 0.1200 0.5016 -0.1811 -0.0054 -0.0085 -0.2492 -0.0180 0.1963 -0.1163 0.2182 0.1616 0.0616 -0.0664 -0.2843 0.0672 0.0654 -0.0933 -0.0648 -0.1015 0.0970 -0.0167 -0.0032 0.0179 -0.0316 -0.0251 0.0094 0.0331 -0.0258 0.0124 -0.0121 0.0085 -0.0130 0.0059 -0.0030 0.0048 -0.0025
2162e1 2162e1 Eosinophils d2162 11 #66A61E 2162e1 -0.1247 -0.0397 -0.1247 -0.0397 0.2461 0.0302 -0.1423 0.0170 0.1307 -0.0514 0.0643 0.0047 -0.0557 -0.2627 0.0509 0.0748 0.1814 -0.0712 0.2497 0.0495 0.0568 -0.0388 -0.3315 0.2886 -0.0862 0.0789 0.0938 -0.1907 0.3622 -0.1853 -0.2333 0.0515 0.2273 0.0168 0.1543 0.0182 -0.0861 0.0421 0.2887 -0.0507 0.0225 -0.0656 -0.0527 0.0497 0.0163 0.0414 -0.0870 0.0163 -0.0472 0.0553 0.0000 -0.0011 0.0186 0.0067 -0.0049 -0.0036
2162bp1 2162bp1 Biopsy d2162 11 #E7298A 2162bp1 0.1555 -0.2072 0.1555 -0.2072 -0.0303 0.1488 0.0221 0.0070 0.0316 -0.4237 -0.2222 -0.1965 -0.2877 -0.3823 0.0881 -0.2149 -0.3376 -0.2138 -0.1482 -0.1776 0.1816 0.1675 -0.0229 -0.1409 -0.0526 0.0109 -0.0017 0.0540 -0.0216 -0.0978 0.0263 0.0159 0.0493 -0.0397 0.0386 -0.0034 0.0807 -0.1159 0.0042 -0.0187 0.0353 0.0298 0.0043 -0.0040 -0.0161 -0.0176 -0.0031 0.0029 -0.0112 0.0002 -0.0020 0.0081 -0.0060 0.0013 -0.0004 0.0011
macrofagos Macrofagos macrophage unknown 12 #E6AB02 Macrofagos 0.1444 0.1680 0.1444 0.1680 -0.0254 -0.0492 -0.0944 -0.1309 0.0190 0.0830 -0.0898 -0.0310 -0.0362 0.1539 0.1875 0.2545 -0.1047 -0.0188 0.0685 -0.0034 -0.0112 0.0854 0.0016 -0.0323 -0.0853 -0.0219 0.1315 -0.0413 -0.1053 -0.2945 0.0491 -0.0056 0.0107 -0.0952 -0.0477 -0.1184 0.0394 -0.0244 -0.0456 -0.1900 0.3862 -0.1438 -0.1197 -0.0412 -0.1164 0.3838 0.1213 -0.0040 -0.0594 -0.0765 0.2958 0.1776 -0.1207 0.1031 0.0900 0.1495
macrofagos+sbv Macrofagos+SbV macrophage unknown 12 #E6AB02 Mcrfgs+SbV 0.1387 0.1752 0.1387 0.1752 -0.0294 -0.0424 -0.1192 -0.0897 0.0458 0.0262 -0.1691 -0.2171 0.1909 0.0324 -0.0517 -0.0029 0.0052 0.0483 0.0757 -0.0036 0.0430 -0.0060 0.0116 -0.0161 -0.0338 -0.0215 0.1150 -0.0746 -0.1105 -0.2196 0.0004 -0.0385 -0.0281 -0.0548 0.0190 0.0516 0.0254 -0.0023 -0.0254 0.0689 -0.0700 -0.0056 0.0575 0.0359 0.0795 -0.2255 -0.0766 0.1264 -0.1308 -0.0555 0.2497 -0.2497 0.3375 0.1343 0.1437 -0.5451
macrofagos+10772 Macrofagos+10772 macrophage unknown 12 #E6AB02 Mcrf+10772 0.1408 0.1691 0.1408 0.1691 -0.0438 -0.0405 -0.1069 -0.0383 -0.0142 0.0196 0.0725 0.1159 -0.1593 0.0675 0.1024 0.1331 -0.0442 -0.0168 -0.0282 -0.0070 -0.0244 0.0566 -0.0253 0.0046 -0.0324 0.0029 0.1539 0.0056 -0.0894 -0.1813 -0.0056 -0.0536 -0.0077 -0.0420 0.0311 -0.0017 0.0605 -0.0028 0.0042 -0.0146 0.0140 0.0015 -0.0365 -0.0395 -0.0651 0.1174 0.0612 -0.1607 0.2498 0.0659 -0.5295 -0.5485 0.0002 -0.1839 -0.1245 -0.0953
macrofagos+10772+sbv Macrofagos+10772+SbV macrophage unknown 12 #E6AB02 M+10772+SV 0.1362 0.1762 0.1362 0.1762 -0.0361 -0.0374 -0.1206 -0.0888 0.0024 0.0309 -0.1061 -0.1709 0.1540 0.0098 -0.1121 -0.0947 0.0529 0.0535 0.0589 -0.0045 0.0277 0.0128 -0.0063 -0.0307 -0.0148 -0.0264 0.0400 -0.0590 -0.0526 -0.1432 0.0123 0.0157 0.0151 -0.0548 -0.0227 -0.0076 0.0159 -0.0208 -0.0194 -0.0517 0.0635 -0.0292 0.0527 0.0028 0.0900 -0.2490 -0.0957 -0.0163 0.0675 0.0263 -0.1941 0.2727 0.4227 -0.0818 -0.4098 0.4632
macrofagos+2169 Macrofagos+2169 macrophage unknown 12 #E6AB02 Mcrfg+2169 0.1350 0.1637 0.1350 0.1637 -0.0589 -0.0294 -0.0918 -0.0602 -0.0668 0.0625 0.2523 0.2587 -0.2778 -0.0236 -0.0448 -0.0718 0.0222 -0.0030 -0.0634 -0.0080 -0.0476 0.0932 -0.0295 -0.0253 -0.0116 0.0326 0.1741 0.0119 -0.0985 -0.1325 -0.0142 -0.0574 0.0104 -0.0291 0.0032 0.0226 -0.0214 -0.0008 0.0205 0.0230 -0.1061 0.0285 0.0292 0.0150 0.0391 -0.2727 -0.0955 -0.0348 0.0663 0.0367 -0.2292 0.4414 -0.0524 0.2161 0.4568 -0.0630
macrofagos+2169+sbv Macrofagos+2169+SbV macrophage unknown 12 #E6AB02 Mc+2169+SV 0.1307 0.1729 0.1307 0.1729 -0.0525 -0.0229 -0.1276 0.0221 -0.0264 0.0160 0.0704 -0.0233 0.0996 -0.1239 -0.2168 -0.2743 0.1093 0.0198 -0.0137 0.0006 -0.0232 0.0115 0.0415 -0.0012 0.0226 0.0081 -0.1280 0.0116 0.0885 0.1273 0.0395 0.0428 0.0239 -0.0052 -0.0634 -0.0985 -0.0567 -0.0397 -0.0011 -0.2170 0.3410 -0.0641 -0.0790 -0.0361 -0.0687 -0.0222 -0.0271 -0.0521 -0.0072 0.0595 -0.0453 0.2132 -0.2808 -0.3581 -0.1836 -0.4549
macrofagos+12309 Macrofagos+12309 macrophage unknown 12 #E6AB02 Mcrf+12309 0.1409 0.1700 0.1409 0.1700 -0.0348 -0.0319 -0.1139 -0.0270 0.0459 -0.0560 -0.0447 0.0337 -0.1054 0.1016 0.2571 0.2857 -0.1105 -0.0477 -0.0035 -0.0168 0.0082 -0.0777 -0.0651 -0.0287 0.0253 -0.0420 -0.2342 0.0302 0.1695 0.2720 0.0321 0.0807 0.0263 0.0575 -0.0508 -0.0175 -0.0505 -0.0128 -0.0030 -0.0232 0.0497 -0.0070 0.0426 0.0195 0.0580 -0.2945 -0.1153 0.0588 -0.0289 0.0433 -0.0342 -0.0430 -0.2389 0.4888 -0.3417 -0.0732
macrofagos+12309+sbv Macrofagos+12309+SbV macrophage unknown 12 #E6AB02 M+12309+SV 0.1383 0.1749 0.1383 0.1749 -0.0349 -0.0349 -0.1333 0.0010 0.0613 -0.0138 -0.1397 -0.1868 0.2198 -0.0082 -0.0582 -0.0235 0.0080 0.0221 0.0447 0.0067 0.0304 -0.0240 -0.0155 0.0049 0.0063 -0.0284 0.0600 -0.0318 -0.0433 -0.0584 -0.0299 -0.0345 -0.0113 -0.0031 0.0146 0.0469 0.0164 0.0009 0.0300 0.1338 -0.2087 0.0615 0.0440 0.0125 0.0224 -0.2137 -0.0818 0.0319 -0.0664 0.0961 0.1115 -0.1847 -0.6051 -0.2373 0.2225 0.3567
macrofagos+12367+sbv Macrofagos+12367+SbV macrophage unknown 12 #E6AB02 M+12367+SV 0.1352 0.1763 0.1352 0.1763 -0.0425 -0.0304 -0.1474 0.0987 0.0592 -0.0754 -0.1129 -0.1740 0.2262 -0.0695 -0.0535 -0.0480 0.0174 0.0111 0.0070 0.0115 0.0245 -0.0606 0.0266 0.0415 0.0372 -0.0012 -0.0413 0.0218 0.0314 0.0901 -0.0630 -0.0036 -0.0216 0.0431 0.0787 0.0920 0.0643 0.0395 0.0303 0.2158 -0.3629 0.1031 0.0322 0.0162 -0.0195 0.5324 0.2109 -0.0973 0.1246 -0.0482 -0.1827 0.2771 -0.0374 0.2479 -0.0596 -0.1237
macrofagos+1126 Macrofagos+1126 macrophage unknown 12 #E6AB02 Mcrfg+1126 0.1390 0.1714 0.1390 0.1714 -0.0368 -0.0313 -0.1229 0.0372 0.0523 -0.0895 -0.0550 0.0142 -0.0743 0.0632 0.2546 0.2683 -0.1003 -0.0558 -0.0186 -0.0166 0.0075 -0.0965 -0.0304 0.0022 0.0372 -0.0070 -0.2546 0.0556 0.1815 0.3185 0.0031 0.0754 -0.0009 0.0786 -0.0050 0.0196 -0.0380 0.0261 -0.0026 0.0007 -0.0482 0.0073 0.0142 -0.0032 0.0178 0.0203 0.0108 0.0034 -0.0151 -0.0222 0.0304 0.0920 0.3535 -0.4870 0.3674 0.0373
macrofagos+12251 Macrofagos+12251 macrophage unknown 12 #E6AB02 Mcrf+12251 0.1360 0.1627 0.1360 0.1627 -0.0653 -0.0250 -0.1059 0.0180 -0.0439 0.0124 0.2943 0.2953 -0.2828 -0.0718 -0.0159 -0.0715 -0.0081 -0.0221 -0.1029 -0.0014 -0.0686 0.0770 0.0102 0.0214 -0.0023 0.0422 0.1125 0.0611 -0.0599 -0.0120 -0.0623 -0.0804 -0.0274 0.0432 0.0701 0.0625 -0.0180 0.0402 0.0062 0.1722 -0.2602 0.0857 0.0355 0.0206 0.0131 0.1011 0.0486 0.1104 -0.2074 -0.0473 0.4506 -0.0390 0.0432 -0.1815 -0.4108 0.0403
macrofagos+12251+sbv Macrofagos+12251+SbV macrophage unknown 12 #E6AB02 M+12251+SV 0.1298 0.1715 0.1298 0.1715 -0.0629 -0.0169 -0.1310 0.0693 -0.0426 -0.0197 0.1523 0.0566 0.0192 -0.1831 -0.2342 -0.3226 0.1286 0.0255 -0.0676 0.0063 -0.0242 -0.0109 0.0503 0.0126 0.0645 0.0307 -0.1315 0.0262 0.0986 0.2230 0.0238 0.0489 0.0218 0.0392 -0.0319 -0.0498 -0.0451 -0.0055 0.0038 -0.1247 0.2055 -0.0499 -0.0762 -0.0107 -0.0652 0.1185 0.0378 0.0308 -0.0040 -0.0762 0.0808 -0.4001 0.1804 0.3438 0.2588 0.3049 
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 4f025ebdf7b19ddfef0cf9ddaa9ebe2857477394
## This is hpgltools commit: Wed Aug 19 10:11:52 2020 -0400: 4f025ebdf7b19ddfef0cf9ddaa9ebe2857477394
## Saving to 01_annotation_v202009.rda.xz
tmp <- loadme(filename=savefile)
---
title: "L. panamensis 202009: 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 <- "202009"
  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_20191001.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_20200915.xlsx",
                       file_column="hg3891hisatfile",
                       gene_info=hs_annot)

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

# Questions from Maria Adelaida

The following comes from an email 20190830 from Maria Adelaida.

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

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

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

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

## Preparation

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

## Global view

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

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

## Monocytes

```{r monocytes}
mono <- subset_expt(hs_valid, subset="typeofcells=='Monocytes'")
mono <- set_expt_conditions(mono, fact="visitnumber")
mono <- set_expt_batches(mono, fact="donor")
mono_norm <- normalize_expt(mono, transform="log2", convert="cpm", batch="svaseq", filter=TRUE)
plot_pca(mono_norm)$plot
```

## Neutrophils

```{r neutrophils}
neut <- subset_expt(hs_valid, subset="typeofcells=='Neutrophils'")
neut <- set_expt_conditions(neut, fact="visitnumber")
neut <- set_expt_batches(neut, fact="donor")
neut_norm <- normalize_expt(neut, transform="log2", convert="cpm", batch="svaseq", filter=TRUE)
plot_pca(neut_norm)$plot
```

## Eosinophils

```{r eosinophils}

eo <- subset_expt(hs_valid, subset="typeofcells=='Eosinophils'")
eo <- set_expt_conditions(eo, fact="visitnumber")
eo <- set_expt_batches(eo, fact="donor")
eo_norm <- normalize_expt(eo, transform="log2", convert="cpm", batch="svaseq", filter=TRUE)
plot_pca(eo_norm)$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 <- 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 <- 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 <- 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 <- all_pairwise(q1_time, model_batch=FALSE, parallel=FALSE)
time_all_tables <- combine_de_tables(time_de, excel=glue::glue("excel/time_de_tables-v{ver}.xlsx"))

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

time_all_tables <- 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 <- all_pairwise(q1_donor, model_batch=FALSE)
donor_tables <- combine_de_tables(donor_de, excel=glue::glue("excel/donor_de_tables-v{ver}.xlsx"))
```

```{r question2}
hs_q2 <- subset_expt(hs_valid, subset="donor!='d1010'&donor!='d1011'")
q2_norm <- 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)
```
