L. panamensis 202009: Sample Estimation

1 Annotation

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")]

2 Sample Estimation

2.1 Generate expressionsets

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

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
## I think samples 7,10 should be removed at minimum, probably also 9,11
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"))

From here on hisat2 is the primary method used.

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

## I think samples 7,10 should be removed at minimum, probably also 9,11
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

## This is causing segmentation faults in R
##hs_write <- write_expt(hs_expt, excel=glue("excel/hs_hisat2_written-v{ver}.xlsx"))

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

## Attempting mixed linear model with: ~  (1|condition) + (1|batch)

## Fitting the expressionset to the model, this is slow.

## Dividing work into 100 chunks...

## 
## Total:129 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:115 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:215 s

## Placing factor: condition at the beginning of the model.

## Writing the median reads by factor.

3 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.

3.1 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.

3.2 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")
all_norm <- normalize_expt(hs_valid, 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 39597 low-count genes (18705 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 7701 values equal to 0, adding 1 to the matrix.

## Step 5: not doing batch correction.

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.

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
tmrc30001	TMRC30001	PBMCs	d1010	1	#1B9E77	TMRC30001	-0.0042	-0.0928	-0.0042	-0.0928	-0.1604	-0.0763	-0.1431	0.2520	0.0143	-0.1147	0.0894	-0.0210	0.0314	-0.0068	0.0305	-0.1096	0.0971	-0.0341	0.0190	-0.1261	0.1270	-0.0281	0.2150	-0.2192	0.3273	-0.2048	0.1802	-0.0042	-0.0400	-0.1764	0.0465	0.0453	0.1890	-0.0881	-0.0642	-0.0599	-0.0309	0.0118	0.1224	0.0769	-0.0162	-0.0132	-0.0275	-0.0033	-0.0632	-0.0404	-0.0306	-0.1812	0.1008	0.0499	0.1451	0.0782	0.0170	0.0026	-0.5512	-0.0683	-0.0602	-0.0071	-0.0155	-0.0240	-0.0203	-0.0138	-0.0095
tmrc30002	TMRC30002	PBMCs	d1010	1	#1B9E77	TMRC30002	0.0060	-0.0913	0.0060	-0.0913	-0.1726	-0.0825	-0.1301	0.2681	-0.0038	0.0146	-0.0592	0.0051	-0.0375	-0.0366	0.0340	-0.0422	-0.0123	-0.0745	-0.0793	-0.0932	0.0584	-0.0273	-0.0643	-0.2439	0.2534	0.1144	-0.0319	-0.0102	-0.1015	-0.0293	0.1453	-0.0502	0.0436	-0.0212	0.0249	-0.0547	-0.0696	0.0073	-0.0044	-0.0642	0.0298	0.0176	0.0093	0.0109	0.1055	0.0373	0.0625	0.4297	-0.3986	-0.2968	-0.0775	-0.3469	0.0748	0.0923	0.1715	0.0527	-0.0259	-0.0285	0.0210	-0.0075	0.0024	0.0128	0.0035
tmrc30003	TMRC30003	PBMCs	d1010	1	#1B9E77	TMRC30003	0.0076	-0.0945	0.0076	-0.0945	-0.1792	-0.0745	-0.1373	0.2793	0.1065	0.0885	-0.0521	0.0350	-0.0734	-0.0599	0.0805	-0.0029	-0.0822	-0.0758	-0.1189	-0.0787	0.0004	-0.0201	-0.1692	-0.2111	0.1683	0.2818	-0.1093	0.0333	-0.1009	0.1648	0.2156	-0.1234	-0.1084	-0.0472	-0.0040	0.0828	-0.0582	-0.0445	-0.0520	0.0339	0.0103	-0.0159	0.0365	0.0309	-0.0254	0.0052	-0.0489	-0.3122	0.2982	0.2350	-0.0663	0.2511	-0.0734	-0.0788	0.3333	0.0241	0.0780	0.0290	-0.0023	0.0238	0.0102	-0.0077	-0.0027
tmrc30004	TMRC30004	PBMCs	d1011	2	#1B9E77	TMRC30004	-0.0064	-0.0936	-0.0064	-0.0936	-0.1584	-0.0737	-0.1364	0.2710	0.0122	-0.1462	0.0811	-0.0059	0.0560	0.0784	-0.0087	0.0129	0.1092	0.0330	0.1232	0.0676	0.0236	0.0458	0.1981	0.1976	-0.1028	-0.3426	0.1095	-0.0182	0.1060	-0.1607	-0.1753	0.0836	0.1576	0.0412	-0.0146	-0.0373	0.0342	0.0126	0.0940	0.0101	0.0411	-0.0680	-0.0041	0.0290	-0.0315	-0.0192	0.0189	-0.0813	-0.0009	-0.0777	-0.2195	0.0295	-0.3903	-0.0071	0.4645	0.0762	0.0448	0.0059	0.0093	0.0313	0.0129	0.0032	0.0095
tmrc30005	TMRC30005	PBMCs	d1011	2	#1B9E77	TMRC30005	0.0007	-0.0984	0.0007	-0.0984	-0.1802	-0.0570	-0.1491	0.3200	0.1371	-0.0526	0.0610	0.0314	0.0165	0.0282	-0.0003	0.0255	-0.0078	-0.0096	0.0267	0.0740	-0.0593	0.0721	0.0135	0.2513	-0.2263	-0.0662	-0.0578	0.0115	0.0653	-0.0234	-0.1284	0.0871	-0.0612	0.1009	0.0143	0.0188	0.0367	-0.0105	-0.0138	0.0317	-0.0016	0.0936	0.0002	-0.0227	-0.0011	0.0502	-0.0175	0.1164	0.0305	0.1799	0.2187	0.0380	0.6982	0.0021	0.0645	0.0197	0.0452	0.0116	0.0113	0.0019	0.0063	0.0052	-0.0019
tmrc30006	TMRC30006	PBMCs	d1011	2	#1B9E77	TMRC30006	0.0074	-0.0939	0.0074	-0.0939	-0.1787	-0.0668	-0.1362	0.2876	0.1617	0.0050	-0.0234	0.0562	-0.0108	-0.0078	-0.0200	0.0933	-0.0945	-0.0026	-0.0006	0.1298	-0.0988	0.0388	-0.1520	0.2393	-0.2966	0.1817	-0.1609	-0.0128	0.0519	0.1356	-0.0293	-0.0535	-0.1833	0.0356	0.0102	0.0443	0.0516	0.0168	-0.1495	-0.0675	-0.0106	-0.0401	-0.0092	-0.0441	-0.0008	-0.0089	0.0454	0.0066	-0.0587	-0.1283	-0.0498	-0.0766	-0.3960	0.0185	-0.4832	-0.0921	-0.0853	-0.0243	-0.0232	-0.0220	-0.0114	0.0022	0.0052
tmrc30009	TMRC30009	Neutrophils	d1034	3	#D95F02	TMRC30009	0.1924	-0.0285	0.1924	-0.0285	0.1789	-0.0752	0.0803	0.0231	-0.0963	-0.3000	0.0980	0.0056	-0.0152	-0.0402	0.1272	0.3280	-0.0443	0.0039	-0.0188	-0.1566	-0.0567	-0.0622	-0.0788	0.1084	0.1289	0.1571	0.0029	-0.4025	0.0896	-0.0035	0.0816	-0.0893	0.0800	0.4286	-0.3264	-0.1752	0.1120	0.0106	0.0225	0.1508	-0.1032	-0.0234	0.0029	0.0747	-0.0394	0.0240	-0.0459	-0.0084	-0.0484	0.0363	0.0107	0.0154	-0.0042	0.0273	0.0031	0.0041	0.0008	0.0007	-0.0069	-0.0071	0.0014	0.0019	0.0039
tmrc30015	TMRC30015	Biopsy	NA	NA	#E7298A	TMRC30015	-0.1597	-0.1621	-0.1597	-0.1621	0.1027	0.0890	-0.0118	-0.0760	0.1403	-0.1685	-0.0862	0.0665	-0.0667	0.1722	0.0292	-0.0373	0.0885	0.0368	0.0790	0.0719	-0.1021	0.0745	-0.0314	-0.0232	-0.0048	0.0937	0.1537	0.0671	0.0940	0.1447	0.1636	0.3260	-0.1720	0.0449	0.1231	-0.5270	-0.2872	-0.3065	0.1001	0.0858	0.0901	0.1499	-0.0705	0.0313	0.1086	0.0125	-0.0317	-0.0288	0.0251	-0.0290	-0.0370	0.0103	-0.0279	0.0142	-0.0100	-0.0191	-0.0148	-0.0152	-0.0117	0.0056	0.0099	-0.0059	-0.0062
tmrc30011	TMRC30011	Neutrophils	d1034	3	#D95F02	TMRC30011	0.1945	-0.0295	0.1945	-0.0295	0.2038	-0.0837	0.0725	0.0333	-0.0426	-0.3258	0.1897	0.0576	0.0093	-0.0006	0.1575	0.3524	-0.1651	-0.1615	-0.1321	-0.1238	-0.1654	-0.1288	-0.0775	-0.1046	-0.1092	0.0140	0.0918	0.1438	-0.0508	-0.1637	-0.0591	0.1618	0.0577	-0.2586	0.4272	0.2218	-0.0182	-0.1636	-0.0363	-0.0643	0.0401	-0.0302	-0.0159	-0.0710	0.0054	-0.0359	-0.0303	-0.0291	-0.0171	-0.0208	-0.0139	-0.0090	0.0113	-0.0134	-0.0031	-0.0101	-0.0069	0.0029	0.0004	0.0030	0.0048	-0.0008	0.0011
tmrc30012	TMRC30012	Monocytes	d1034	3	#7570B3	TMRC30012	0.0090	-0.0406	0.0090	-0.0406	-0.0963	-0.1956	-0.0048	-0.1176	-0.2492	-0.3137	-0.0543	-0.0474	0.0722	0.0314	0.0323	0.0141	0.1537	0.2698	0.1187	0.1314	0.1087	-0.0628	-0.0894	-0.0482	-0.0092	-0.0471	-0.0647	0.1230	-0.0489	0.2045	0.0195	0.0098	-0.0946	-0.1321	-0.1125	0.0924	-0.0163	0.0552	-0.0223	0.0100	-0.0244	-0.0222	0.0671	0.0232	-0.0463	0.0131	-0.0344	-0.0576	0.0638	0.2743	-0.4354	-0.3755	0.1418	-0.0561	-0.1143	0.1047	0.0663	0.0391	0.0122	-0.0096	-0.0058	0.0004	-0.0062
tmrc30013	TMRC30013	Monocytes	d1034	3	#7570B3	TMRC30013	0.0147	-0.0420	0.0147	-0.0420	-0.1006	-0.1855	-0.0017	-0.1022	-0.2414	-0.3209	-0.0768	-0.0261	0.0971	0.0315	-0.0396	0.0391	0.1018	0.2344	0.1087	0.1564	0.1508	-0.0874	-0.0655	-0.0256	0.0005	0.0501	-0.0741	0.1508	-0.1008	0.1584	0.0058	-0.0383	-0.0408	-0.0881	-0.0670	0.0601	-0.0047	0.0626	-0.0240	-0.0426	0.0196	0.0875	0.0037	0.0466	0.0576	-0.0382	0.0646	0.0573	-0.0323	-0.2706	0.5127	0.3384	-0.0784	0.0089	0.1293	-0.0944	-0.0943	-0.0501	0.0026	-0.0064	-0.0012	0.0124	0.0053
tmrc30016	TMRC30016	Biopsy	d2050	4	#E7298A	TMRC30016	-0.1488	-0.1479	-0.1488	-0.1479	0.0678	0.0654	-0.0189	-0.0038	0.0760	0.0004	0.2769	-0.0974	0.1353	-0.3661	-0.2135	-0.0994	-0.0490	0.2426	-0.3398	-0.1200	0.1174	-0.1738	-0.0318	-0.0311	-0.0348	-0.0711	0.2776	-0.0005	0.0913	0.2558	-0.0877	-0.0040	-0.1140	0.1042	0.0550	-0.0295	0.1091	-0.0864	0.0922	-0.3280	-0.2293	0.0215	-0.0015	0.0017	-0.0230	0.0405	-0.0258	0.0405	-0.0115	-0.0090	-0.0483	0.0531	-0.0014	-0.0069	0.0242	-0.0090	0.0088	-0.0101	-0.0120	-0.0444	0.0117	-0.0033	-0.0042
tmrc30017	TMRC30017	Biopsy	d2052	5	#E7298A	TMRC30017	-0.1573	-0.1660	-0.1573	-0.1660	0.1030	0.1036	-0.0217	-0.0708	0.1839	-0.1199	-0.1233	0.0995	-0.0971	0.2257	0.0311	-0.0988	0.0638	-0.0204	-0.0466	-0.0571	-0.0868	0.0476	0.0678	-0.1479	0.1307	0.0512	0.3022	-0.1209	-0.1465	0.1366	-0.3370	0.0745	-0.1778	0.0255	-0.0158	0.1750	0.3974	0.1995	-0.3233	0.1678	0.1664	0.0295	0.0434	0.0288	0.0291	-0.0162	-0.0080	0.0140	0.0033	-0.0308	0.0062	-0.0297	-0.0097	0.0029	0.0301	-0.0005	-0.0155	0.0077	-0.0101	-0.0007	-0.0064	-0.0070	-0.0003
tmrc30071	TMRC30071	Eosinophils	d2052	5	#66A61E	TMRC30071	0.0693	0.1409	0.0693	0.1409	-0.0896	0.2752	-0.1466	-0.0358	-0.0434	0.0558	0.0028	-0.1419	-0.1270	0.0115	0.3062	0.0270	0.1511	0.0465	-0.2735	0.2415	-0.0028	-0.0429	-0.1336	0.0177	-0.2327	-0.0836	0.1818	-0.4262	-0.1990	0.0205	0.0687	-0.0595	0.0364	-0.3758	-0.1239	-0.0745	-0.0157	-0.0425	0.0952	-0.0309	0.0668	-0.0261	-0.0533	0.0967	-0.0220	-0.0550	0.0512	-0.0114	-0.0466	-0.0265	0.0405	0.0334	0.0365	-0.0374	-0.0119	0.0443	0.0632	0.0122	0.0201	0.0067	0.0165	0.0189	0.0078
tmrc30058	TMRC30058	Neutrophils	d2052	5	#D95F02	TMRC30058	0.1244	0.1396	0.1244	0.1396	0.2136	0.0679	-0.2962	0.0210	-0.1050	0.1200	-0.1789	0.3842	0.3878	-0.0263	0.0577	-0.0880	0.2330	-0.0228	-0.2031	0.2207	-0.2082	-0.3714	0.2256	0.0896	0.1329	0.0630	-0.0886	0.0517	0.0421	0.0257	0.0275	0.0333	0.0022	0.1039	0.0610	0.0307	-0.0003	-0.0222	-0.0122	0.0383	-0.0231	-0.0247	0.0049	-0.0295	-0.0317	0.0115	-0.0080	0.0029	0.0094	0.0176	0.0073	-0.0241	-0.0071	0.0115	0.0011	-0.0038	-0.0025	0.0004	0.0071	0.0007	-0.0040	-0.0003	-0.0024
tmrc30018	TMRC30018	Biopsy	d2065	6	#E7298A	TMRC30018	-0.1588	-0.1582	-0.1588	-0.1582	0.1208	0.0835	-0.0108	-0.1475	0.2783	-0.1548	-0.1937	0.1531	-0.1277	0.1651	-0.0279	-0.0804	0.0203	-0.0932	-0.2212	-0.2128	0.3620	-0.2390	-0.1848	0.1759	-0.0123	-0.1509	-0.3772	-0.0139	-0.1177	-0.1634	-0.1299	0.0100	0.0484	0.0037	-0.1206	0.1277	-0.1776	-0.0517	0.1381	-0.0201	-0.0788	0.0166	-0.0084	0.0230	-0.0172	-0.0308	0.0253	-0.0179	-0.0130	-0.0067	-0.0271	0.0055	-0.0113	-0.0140	-0.0138	-0.0005	0.0053	0.0007	0.0021	-0.0066	0.0085	-0.0002	0.0042
tmrc30019	TMRC30019	Biopsy	d2066	7	#E7298A	TMRC30019	-0.1566	-0.1659	-0.1566	-0.1659	0.1124	0.0884	-0.0172	-0.0448	0.0607	-0.0628	-0.0987	0.0911	-0.1212	0.2154	0.0943	0.0077	0.0727	-0.0658	0.1246	0.0351	-0.0369	0.0391	0.0902	0.0028	-0.1433	0.0083	0.1719	0.0515	0.2727	0.0892	0.4052	-0.3963	0.2584	-0.0136	0.1517	0.1882	0.1101	0.1658	0.1287	-0.1126	-0.2505	0.2333	0.0062	-0.0484	-0.0171	0.0092	0.0117	0.0548	-0.0073	0.0244	-0.0150	0.0148	0.0147	-0.0042	-0.0118	-0.0022	0.0217	0.0217	-0.0010	0.0006	-0.0038	0.0082	-0.0002
tmrc30014	TMRC30014	Monocytes	d2068	8	#7570B3	TMRC30014	0.0054	-0.0602	0.0054	-0.0602	-0.1278	-0.1891	-0.0385	-0.2508	0.1342	0.2296	0.0884	0.1359	0.0372	0.0069	0.0848	0.2293	-0.0718	-0.0536	-0.0862	0.0517	-0.0097	0.1120	-0.1259	0.1563	0.2248	-0.1537	0.0976	-0.0159	0.1758	0.1992	-0.0432	0.1554	0.0834	-0.2060	-0.0068	-0.0501	-0.2375	0.2812	-0.1004	0.1832	-0.2848	-0.1675	0.2683	-0.0718	0.0339	-0.0026	-0.1245	0.0651	0.0234	-0.1065	0.0230	0.0433	0.0194	0.0081	0.0129	0.0108	0.0127	0.0110	-0.0170	-0.0150	0.0046	-0.0010	-0.0111
tmrc30021	TMRC30021	Neutrophils	d2068	8	#D95F02	TMRC30021	0.1873	-0.0433	0.1873	-0.0433	0.1369	-0.0708	0.0536	0.0141	0.1606	0.1889	0.0316	-0.0616	-0.1019	0.0019	-0.0015	0.0165	0.0510	0.2209	0.1897	0.1275	0.1063	-0.1016	-0.0840	0.0798	0.2304	0.0734	0.0853	-0.1535	0.2570	-0.1836	0.0878	0.1754	-0.0906	-0.0602	-0.0925	0.2847	0.0406	-0.1621	0.0710	-0.1369	0.1907	0.0636	0.0784	-0.0942	0.1505	-0.0614	0.2580	0.2413	0.3373	-0.0042	-0.0126	-0.0154	-0.0289	0.0323	-0.0150	0.0218	-0.0093	-0.0210	0.0009	-0.0063	-0.0049	0.0031	0.0073
tmrc30029	TMRC30029	Eosinophils	d2068	8	#66A61E	TMRC30029	0.1234	-0.0430	0.1234	-0.0430	-0.1589	0.1579	0.1786	-0.0362	0.0637	0.1172	-0.0235	0.1458	0.0549	-0.0196	-0.0404	0.1020	-0.0514	0.0657	0.1880	0.0112	-0.0516	-0.1455	-0.2259	0.0977	0.1568	-0.1023	0.1269	0.1126	-0.0678	-0.1079	0.0580	-0.0077	-0.2033	-0.0008	0.1218	-0.0214	0.0962	0.2458	0.1716	0.0556	-0.0808	-0.0974	-0.4305	0.2537	0.0250	0.1373	0.2384	-0.1781	-0.1517	0.0568	-0.0013	-0.0414	0.0384	0.0725	0.0224	-0.1728	0.1220	-0.0719	0.0131	0.0188	-0.0045	0.0020	-0.0083
tmrc30020	TMRC30020	Biopsy	d2068	8	#E7298A	TMRC30020	-0.1524	-0.1587	-0.1524	-0.1587	0.0822	0.0657	-0.0007	0.0350	-0.2424	0.1939	0.3182	-0.0081	0.0055	-0.0315	-0.0815	-0.0221	-0.0366	0.0068	-0.0235	0.0050	0.0145	-0.0839	-0.0376	0.0479	-0.1202	0.1125	0.1148	0.0500	-0.0570	0.1065	-0.1212	-0.1269	0.1640	0.1237	-0.0376	0.2657	-0.3673	0.0287	0.0968	0.4533	0.2728	0.1622	-0.0922	0.1008	0.1499	0.0000	0.0257	0.0068	0.0081	-0.0422	-0.0391	-0.0407	-0.0182	-0.0160	-0.0445	-0.0087	-0.0095	0.0071	-0.0242	0.0209	0.0089	-0.0012	0.0173
tmrc30039	TMRC30039	Neutrophils	d2072	10	#D95F02	TMRC30039	0.1970	-0.0375	0.1970	-0.0375	0.1484	-0.0786	0.0709	0.0856	-0.0388	0.1656	-0.1922	-0.1750	-0.1643	0.0333	-0.0562	-0.0107	0.0871	0.2007	-0.0364	-0.0564	0.0495	0.1130	0.0603	0.3370	0.2065	-0.0483	0.0657	-0.0171	-0.0909	0.0676	0.0857	-0.0200	-0.0793	0.0328	0.1762	0.1467	-0.0635	-0.0510	0.0183	-0.0805	0.1722	0.0508	-0.0104	-0.0561	-0.0742	-0.1076	-0.4715	-0.1614	-0.3663	0.0673	0.0452	0.0355	-0.0111	-0.0358	-0.0170	-0.0181	0.0449	0.0111	0.0075	-0.0038	0.0030	0.0001	-0.0057
tmrc30023	TMRC30023	Eosinophils	d2072	10	#66A61E	TMRC30023	0.1021	-0.0515	0.1021	-0.0515	-0.1426	0.1203	0.1518	0.0443	-0.0815	0.1025	-0.2888	0.1586	0.0262	0.1527	-0.5550	0.3185	-0.3064	0.0218	-0.0122	0.0818	0.0472	-0.1405	0.1943	-0.1705	-0.1263	-0.0532	0.1063	-0.1674	-0.0819	0.0154	0.0164	-0.0064	0.0693	-0.1087	-0.1075	-0.0517	-0.0378	-0.1070	0.0027	-0.0131	0.0288	0.0782	0.1316	-0.0155	-0.0787	0.0582	-0.0814	0.0216	0.0615	0.0383	-0.0267	-0.0220	0.0267	0.0221	0.0062	-0.0255	-0.0466	-0.0316	0.0042	0.0012	0.0007	-0.0033	0.0090
tmrc30025	TMRC30025	Biopsy	d2072	10	#E7298A	TMRC30025	-0.1522	-0.1528	-0.1522	-0.1528	0.0910	0.0546	0.0084	0.0439	-0.3133	0.1725	0.1523	-0.0569	0.0274	-0.0843	-0.0355	0.0079	-0.0208	-0.0492	-0.0294	-0.0409	0.1076	-0.0407	-0.0423	0.1017	0.0280	-0.0837	-0.2798	-0.0648	0.0295	-0.2122	0.1785	0.1626	-0.1360	-0.2127	0.0962	-0.2138	0.3518	0.0925	-0.1889	0.1783	-0.0882	0.4264	0.0878	-0.0004	-0.0216	-0.0894	-0.0218	-0.0610	0.0293	-0.0276	-0.0215	-0.0360	-0.0401	0.0067	0.0255	-0.0207	0.0004	0.0175	0.0183	0.0060	-0.0096	0.0125	-0.0118
tmrc30022	TMRC30022	Biopsy	d2071	9	#E7298A	TMRC30022	-0.1449	-0.1597	-0.1449	-0.1597	0.0865	0.0610	0.0023	0.0703	-0.3936	0.2130	0.1523	0.0980	-0.1582	0.2653	0.0807	0.0099	0.0401	-0.1400	0.1663	0.0339	-0.1522	0.1043	-0.0038	-0.0480	0.0747	0.1021	-0.0540	-0.0211	-0.1836	0.0314	-0.2123	0.1847	-0.0997	0.0478	-0.1684	0.0969	-0.0437	-0.0834	0.2062	-0.3607	-0.3027	-0.1272	-0.0295	-0.0829	-0.1142	0.0228	-0.0241	0.0014	0.0064	0.0190	0.0622	-0.0052	-0.0194	0.0050	-0.0009	0.0027	-0.0069	-0.0004	0.0038	0.0233	-0.0146	0.0083	-0.0065
tmrc30044	TMRC30044	Monocytes	d2073	11	#7570B3	TMRC30044	-0.1692	-0.1626	-0.1692	-0.1626	0.1010	0.0957	-0.0106	-0.0560	0.1112	-0.0519	-0.0515	-0.1198	0.1198	-0.1462	-0.0785	0.0768	-0.0260	0.1970	-0.0230	0.1242	-0.3194	0.2160	-0.0043	-0.0768	0.1006	-0.0381	-0.2013	-0.0182	-0.0691	-0.1683	-0.0027	-0.0902	0.0896	-0.0408	-0.0301	0.0369	-0.0390	0.0045	0.0240	0.0002	0.0200	-0.0864	0.0049	0.0048	-0.0001	-0.0141	0.0120	-0.0084	-0.0041	0.0061	0.0095	0.0061	0.0144	0.0003	0.0007	0.0024	0.0011	-0.0036	0.0031	-0.0044	0.0017	-0.0031	0.0049
tmrc30048	TMRC30048	Eosinophils	d2073	11	#66A61E	TMRC30048	0.0620	0.1355	0.0620	0.1355	-0.0946	0.2679	-0.1378	-0.0841	0.0147	-0.0231	0.0840	-0.1075	-0.1346	0.0682	-0.0454	0.1572	-0.1021	0.0146	-0.0513	0.1189	0.1054	0.0195	0.0919	-0.0027	0.1268	0.0443	-0.0552	0.1899	0.0857	-0.0453	0.0156	0.0358	-0.1054	0.1397	0.0553	0.0322	0.0255	0.0659	0.0010	0.0062	-0.0380	-0.0067	-0.0903	0.1068	-0.0063	0.0166	0.0376	-0.0539	-0.0456	-0.0502	0.0090	0.0506	0.0096	-0.1462	-0.0443	0.5747	-0.3816	0.3099	-0.0049	-0.0390	0.0199	-0.0132	-0.0259
tmrc30026	TMRC30026	Biopsy	d2073	11	#E7298A	TMRC30026	-0.1432	-0.1502	-0.1432	-0.1502	0.0921	0.0486	0.0070	-0.0119	-0.1794	0.0458	0.1536	0.1138	-0.1298	0.0911	-0.0838	-0.0515	0.0376	0.0196	0.0022	-0.0279	0.1255	-0.1201	-0.0568	0.0303	-0.0694	-0.0553	-0.0155	0.0467	0.1817	-0.0727	0.1971	-0.1636	0.0275	-0.0225	0.0476	-0.1973	0.0520	-0.0080	-0.2916	-0.0770	0.3324	-0.6272	0.0392	-0.0605	-0.0447	0.0528	0.0154	0.0061	-0.0247	0.0692	0.0950	0.0264	0.0705	0.0231	-0.0029	0.0336	0.0090	-0.0201	0.0182	0.0160	-0.0050	-0.0038	-0.0021
tmrc30030	TMRC30030	Monocytes	d2068	8	#7570B3	TMRC30030	0.0095	-0.0505	0.0095	-0.0505	-0.1178	-0.1913	-0.0419	-0.2792	0.0908	0.0145	0.1716	0.0418	0.1423	0.0204	-0.0117	-0.0460	-0.0196	-0.1144	0.0494	0.0864	0.0883	0.0666	0.1791	0.0787	0.0179	0.0571	0.1149	-0.1069	-0.1458	-0.2169	0.0575	-0.1847	-0.0710	-0.0058	-0.1174	0.0425	-0.1805	-0.2387	-0.4741	-0.1285	-0.1433	0.1402	-0.3860	-0.1137	0.0413	0.0083	-0.0312	-0.0435	-0.0334	0.0384	-0.0379	0.0043	0.0013	0.1023	0.0367	0.0594	0.0527	0.0142	-0.0085	-0.0121	-0.0030	-0.0053	-0.0018
tmrc30031	TMRC30031	Neutrophils	d2068	8	#D95F02	TMRC30031	0.1977	-0.0371	0.1977	-0.0371	0.1766	-0.0738	0.0547	-0.0173	0.1944	-0.0080	0.2374	-0.0754	0.0526	0.0233	-0.0585	-0.1401	-0.0457	-0.2298	0.2068	0.3287	0.0966	-0.0852	-0.0087	-0.3408	-0.0393	-0.2313	-0.2861	-0.2924	-0.0092	0.2303	-0.0255	-0.0173	-0.0768	0.1199	0.1888	-0.0413	-0.0509	0.2134	0.0854	-0.0608	0.1059	0.0234	-0.0715	-0.0895	-0.0138	-0.0091	-0.1285	-0.0331	-0.0438	-0.0053	0.0035	0.0150	0.0049	-0.0241	0.0065	0.0034	0.0044	-0.0018	-0.0004	0.0001	-0.0008	0.0014	0.0015
tmrc30032	TMRC30032	Eosinophils	d2068	8	#66A61E	TMRC30032	0.1189	-0.0407	0.1189	-0.0407	-0.1533	0.1694	0.1716	-0.1382	0.1690	-0.0644	0.2882	0.1292	0.1998	-0.0303	0.0283	-0.1382	0.0417	-0.1453	0.2102	-0.0379	-0.0075	0.0094	0.0855	0.2218	0.0774	0.2031	0.0068	0.0488	-0.1983	-0.0107	-0.0201	-0.2166	-0.0370	-0.1345	-0.0098	-0.0748	0.0858	-0.1352	0.1740	-0.0771	0.1312	0.0371	0.5091	0.1377	-0.0399	0.0154	-0.0034	-0.0621	-0.0947	0.0195	-0.0066	-0.0328	0.0101	0.0504	0.0123	0.0140	-0.0970	0.0095	-0.0097	0.0095	0.0134	0.0058	0.0015
tmrc30024	TMRC30024	Monocytes	d2072	10	#7570B3	TMRC30024	0.0221	-0.0526	0.0221	-0.0526	-0.1229	-0.1955	-0.0262	-0.1806	-0.0920	-0.0051	-0.0680	-0.0454	0.0342	0.0093	-0.0738	-0.1414	0.0128	-0.0702	-0.1152	0.0079	0.0497	0.1167	0.0955	-0.0555	-0.1178	0.0953	0.0429	-0.1375	-0.0245	-0.2173	0.0607	0.0324	-0.2079	0.0740	0.1804	0.0565	0.0815	-0.0498	0.2224	0.2449	-0.1343	-0.1730	0.0718	-0.0502	0.1332	0.1122	0.0696	0.2795	-0.1561	0.3014	0.0010	0.1605	-0.1300	-0.4030	-0.0245	-0.1081	-0.0192	0.0494	0.0141	0.0412	0.0046	0.0107	0.0039
tmrc30040	TMRC30040	Neutrophils	d2072	10	#D95F02	TMRC30040	0.2079	-0.0327	0.2079	-0.0327	0.1866	-0.0835	0.0774	0.0744	-0.0670	0.0202	-0.0884	-0.1746	-0.0722	0.0433	-0.0686	-0.0984	0.0117	-0.1187	-0.1124	-0.0143	-0.0068	0.1687	0.0956	0.1673	0.0413	-0.1937	-0.0029	0.0696	-0.2758	0.1744	0.0494	-0.1422	-0.0052	0.0787	0.2081	-0.0806	-0.0512	-0.0887	-0.1186	0.0770	-0.1675	-0.1012	0.0105	0.3736	-0.1539	-0.0683	0.2334	0.2357	0.3621	-0.0360	0.0134	-0.0665	-0.0182	0.1138	-0.0244	0.0038	-0.0389	-0.0067	0.0138	0.0096	-0.0172	0.0031	-0.0057
tmrc30033	TMRC30033	Eosinophils	d2072	10	#66A61E	TMRC30033	0.1357	-0.0390	0.1357	-0.0390	-0.1427	0.1689	0.1924	0.0115	-0.1144	-0.0866	-0.0978	0.0080	0.0515	-0.0579	-0.1166	-0.1852	0.0134	-0.0927	-0.0277	-0.1226	-0.0423	0.0923	0.0779	0.0459	0.0075	0.0957	-0.0882	-0.1248	0.1721	0.1933	-0.0478	0.0830	0.1299	-0.0450	0.0434	0.0325	-0.0030	-0.0388	-0.0308	0.0250	-0.1171	-0.1325	-0.0950	-0.1815	0.2815	-0.5843	0.2553	-0.1904	-0.0850	-0.0131	0.0037	-0.0443	0.0836	-0.0162	0.0237	0.0455	0.0378	0.0319	-0.0015	-0.0084	0.0038	0.0156	0.0006
tmrc30049	TMRC30049	Monocytes	d2073	11	#7570B3	TMRC30049	-0.0266	0.1263	-0.0266	0.1263	-0.0628	-0.0583	-0.2706	-0.1728	-0.0107	-0.0367	-0.0072	-0.1300	-0.2136	-0.0785	-0.1114	-0.1362	-0.1204	-0.0883	0.2207	-0.1706	-0.2994	-0.2542	0.0169	0.0671	-0.0002	-0.0491	0.0121	-0.0491	0.0891	-0.0301	0.0304	-0.0394	-0.0645	-0.0377	-0.0727	0.0243	-0.0409	-0.0187	-0.0367	-0.0498	0.0040	-0.0263	-0.0228	0.1559	-0.0883	-0.1187	-0.1243	-0.0648	0.1124	-0.2668	-0.0654	-0.1140	0.1294	-0.3729	0.0739	-0.3061	-0.1753	0.0129	-0.0090	-0.0746	0.0577	-0.0115	0.0045
tmrc30053	TMRC30053	Neutrophils	d2073	11	#D95F02	TMRC30053	0.1225	0.1460	0.1225	0.1460	0.2069	0.0653	-0.2944	-0.0049	-0.0362	-0.0093	-0.0080	0.2319	0.1767	-0.0569	-0.0862	-0.0468	-0.1035	-0.0419	0.1298	-0.1968	0.0870	0.3083	-0.2138	-0.1342	-0.1491	-0.0689	0.0943	0.0969	0.0869	-0.0840	0.0137	-0.0149	-0.1294	-0.0998	-0.2001	0.0027	-0.0226	0.1059	0.0573	-0.0697	0.0672	0.0191	-0.0579	0.2410	0.0566	-0.3174	-0.2207	0.2035	-0.0166	0.1925	0.0052	0.0801	-0.0714	0.1454	-0.0030	0.0386	-0.0290	-0.0257	-0.0076	-0.0007	0.0015	-0.0023	0.0031
tmrc30054	TMRC30054	Eosinophils	d2073	11	#66A61E	TMRC30054	0.0644	0.1387	0.0644	0.1387	-0.0874	0.2730	-0.1360	-0.0897	-0.0217	-0.1112	0.1147	-0.0986	-0.1084	0.0600	-0.0376	0.0931	-0.0951	-0.0416	-0.0733	0.1161	0.1384	0.0926	0.1706	-0.0250	0.1175	0.1148	-0.1023	0.1898	0.1053	-0.0182	-0.0490	0.0231	-0.0161	0.1167	-0.0028	0.0130	0.0126	0.0055	-0.0401	-0.0377	0.0321	0.0196	0.0677	0.0743	-0.0284	-0.0184	-0.0529	0.0958	0.0413	-0.1290	-0.0260	0.0305	-0.0215	-0.1097	-0.0844	-0.1384	0.7100	-0.0568	-0.0054	0.0704	-0.0454	-0.0300	0.0088
tmrc30037	TMRC30037	Monocytes	d2068	8	#7570B3	TMRC30037	0.0276	-0.0549	0.0276	-0.0549	-0.1196	-0.1985	-0.0248	-0.1736	-0.0668	0.1383	-0.1117	0.0118	-0.0189	0.0013	0.0469	-0.0165	-0.0077	-0.0129	-0.1267	0.1048	0.0127	0.0373	-0.2071	-0.0682	-0.0227	0.1331	0.0863	0.0147	0.0413	-0.1600	-0.1978	0.0743	0.3457	0.2602	0.1634	-0.1789	-0.0538	0.1712	-0.0929	-0.3250	0.2093	0.2209	0.1129	0.1715	-0.1858	-0.1425	0.1996	-0.1008	-0.0493	0.1743	0.0111	-0.0283	-0.0110	-0.1015	-0.0279	-0.0527	-0.0387	0.0086	0.0365	0.0016	0.0012	0.0078	-0.0022
tmrc30027	TMRC30027	Neutrophils	d2068	8	#D95F02	TMRC30027	0.2061	-0.0351	0.2061	-0.0351	0.1762	-0.0738	0.0617	0.0515	0.1237	0.1095	-0.0167	-0.1122	-0.1080	-0.0060	-0.0542	-0.2180	-0.0148	-0.0456	0.0247	0.2663	-0.0008	-0.0600	-0.2049	-0.1656	-0.1193	0.0232	0.0702	0.2949	0.1043	-0.0553	-0.0380	0.0875	0.2219	-0.0351	-0.2775	0.0270	0.1984	-0.3078	-0.1036	0.2396	-0.2578	-0.0603	0.0820	0.1042	-0.0619	0.0617	-0.0190	-0.1476	-0.2581	-0.0399	0.0057	0.0254	0.0248	0.0128	0.0137	-0.0094	0.0194	0.0117	0.0043	0.0101	0.0071	-0.0066	-0.0029
tmrc30028	TMRC30028	Eosinophils	d2068	8	#66A61E	TMRC30028	0.1358	-0.0348	0.1358	-0.0348	-0.1607	0.1742	0.1882	-0.0009	-0.0111	0.0082	-0.0542	0.0422	0.0506	-0.0644	0.0422	-0.1348	0.0601	0.0367	0.0839	-0.0160	-0.0559	-0.0861	-0.2791	0.0593	-0.0095	0.0414	0.0545	0.1374	-0.2019	-0.0645	-0.0754	-0.0467	0.2448	0.0771	0.0407	-0.1748	0.1314	0.0474	0.0092	0.0539	-0.0115	0.0002	-0.1936	-0.3470	-0.0235	-0.0204	-0.3635	0.3102	0.3127	-0.0797	-0.0605	0.0566	-0.0467	-0.1512	-0.0270	0.0992	0.0162	0.0681	-0.0075	-0.0289	-0.0102	-0.0138	-0.0076
tmrc30034	TMRC30034	Monocytes	d2072	10	#7570B3	TMRC30034	0.0282	-0.0508	0.0282	-0.0508	-0.1088	-0.2010	-0.0202	-0.1837	-0.0974	0.0010	-0.1267	-0.0134	-0.0398	-0.0576	-0.0278	-0.1533	-0.0096	-0.0446	-0.1522	0.0270	0.0137	0.0941	0.0171	-0.0561	-0.1760	0.1692	0.0524	-0.0712	0.0938	-0.2264	0.0154	-0.0005	-0.1665	0.1030	0.1028	0.0602	0.0801	0.0743	0.2866	0.0778	0.0268	-0.1121	0.0239	-0.0195	-0.0683	0.0350	-0.1699	-0.2292	0.2015	-0.3691	-0.0399	-0.0772	0.1180	0.4328	-0.0076	0.1561	0.0511	-0.0665	-0.0382	-0.0010	-0.0019	-0.0233	0.0060
tmrc30035	TMRC30035	Neutrophils	d2072	10	#D95F02	TMRC30035	0.2116	-0.0309	0.2116	-0.0309	0.2099	-0.0829	0.0754	0.0528	-0.0541	-0.0735	-0.0478	-0.0577	-0.1010	-0.0501	-0.0451	-0.0879	-0.0780	-0.2039	-0.2413	-0.0004	-0.1227	0.0895	0.0895	0.1029	-0.0069	-0.0746	0.0917	0.2550	-0.0128	-0.0370	-0.0114	-0.1238	-0.1656	-0.1252	-0.3460	-0.1774	-0.0930	0.3096	0.0748	-0.1361	0.1600	0.1307	0.0117	-0.3121	0.1764	0.2054	0.1976	-0.0881	0.0186	-0.0067	-0.0258	-0.0084	0.0056	-0.1178	0.0154	-0.0059	-0.0062	0.0023	-0.0109	-0.0145	0.0075	0.0031	0.0023
tmrc30036	TMRC30036	Eosinophils	d2072	10	#66A61E	TMRC30036	0.1304	-0.0368	0.1304	-0.0368	-0.1469	0.1656	0.1991	-0.0293	-0.1107	-0.1022	-0.0529	0.0231	0.0325	-0.0959	0.0215	-0.2281	0.0863	-0.0910	-0.0753	-0.1671	-0.0904	0.1494	0.0478	-0.0022	0.0202	0.0345	-0.1699	-0.1366	0.3651	0.1196	-0.0292	0.2035	0.1148	-0.1703	-0.0381	0.1711	-0.0977	-0.0050	-0.1327	-0.0677	0.0913	0.0753	-0.1496	0.2122	-0.1671	0.4609	-0.0956	0.0958	0.0044	0.0615	0.0303	0.0477	-0.0738	0.0436	-0.0013	-0.0107	-0.0820	-0.0142	0.0034	-0.0052	-0.0002	-0.0148	-0.0018
tmrc30044.1	TMRC30044.1	Biopsy	d2159	12	#E7298A	TMRC30044.	-0.1692	-0.1626	-0.1692	-0.1626	0.1010	0.0957	-0.0106	-0.0560	0.1112	-0.0519	-0.0515	-0.1198	0.1198	-0.1462	-0.0785	0.0768	-0.0260	0.1970	-0.0230	0.1242	-0.3194	0.2160	-0.0043	-0.0768	0.1006	-0.0381	-0.2013	-0.0182	-0.0691	-0.1683	-0.0027	-0.0902	0.0896	-0.0408	-0.0301	0.0369	-0.0390	0.0045	0.0240	0.0002	0.0200	-0.0864	0.0049	0.0048	-0.0001	-0.0141	0.0120	-0.0084	-0.0041	0.0061	0.0095	0.0061	0.0144	0.0003	0.0007	0.0024	0.0011	-0.0036	0.0031	-0.0044	0.0017	-0.0031	0.0049
tmrc30055	TMRC30055	Monocytes	d2073	11	#7570B3	TMRC30055	-0.0228	0.1308	-0.0228	0.1308	-0.0783	-0.0558	-0.2695	-0.1638	-0.0325	-0.0049	-0.0466	-0.2202	-0.1758	-0.0189	-0.1245	-0.0752	-0.0800	-0.0351	0.1788	-0.2099	-0.2802	-0.2673	-0.0673	0.0579	0.0216	-0.0870	-0.0429	-0.0250	-0.0718	0.1142	-0.0639	0.0088	0.1020	-0.0190	0.0773	-0.0520	0.0365	-0.0289	0.0164	0.0365	0.0047	0.0712	0.0791	-0.1496	0.1043	0.1252	0.1190	0.0760	-0.1057	0.2721	0.0760	0.1267	-0.1269	0.3851	-0.0575	0.2479	0.1732	-0.0326	0.0007	0.0738	-0.0523	0.0195	-0.0046
tmrc30068	TMRC30068	Neutrophils	d2073	11	#D95F02	TMRC30068	0.1182	0.1476	0.1182	0.1476	0.1766	0.0709	-0.2778	-0.0044	-0.0204	0.0655	-0.0309	0.0768	0.1710	0.0350	-0.1116	0.0023	0.0479	0.1592	0.1906	-0.2559	0.1964	0.3091	-0.1682	-0.0082	-0.0615	0.0221	0.0138	-0.1396	-0.1302	0.0765	-0.0068	0.0133	0.1372	0.0187	0.1593	-0.0025	0.0154	-0.0807	-0.0456	0.0358	-0.0362	0.0009	0.0499	-0.2391	-0.0502	0.3296	0.2462	-0.2346	0.0053	-0.2304	-0.0194	-0.0517	0.0861	-0.1795	-0.0030	0.0011	0.0419	0.0389	-0.0005	0.0023	-0.0006	0.0007	-0.0046
tmrc30070	TMRC30070	Eosinophils	d2073	11	#66A61E	TMRC30070	0.0709	0.1436	0.0709	0.1436	-0.0892	0.2787	-0.1371	-0.0728	-0.0207	-0.0466	0.0736	-0.1564	-0.1073	0.0492	0.0660	0.0632	0.0050	0.0394	-0.0995	0.1012	0.1556	0.0940	0.0286	-0.0298	0.0448	0.0123	-0.0405	0.1178	0.0087	-0.0221	-0.0279	0.0010	0.0332	0.1427	0.0603	0.0218	0.0022	0.0057	-0.0362	0.0630	-0.0693	-0.0224	0.0324	-0.2378	0.0745	0.0327	-0.0479	-0.0178	0.0531	0.2059	-0.0270	-0.1289	-0.0263	0.2910	0.1328	-0.4689	-0.4028	-0.2549	-0.0078	-0.0419	0.0123	0.0255	0.0105
tmrc30041	TMRC30041	Monocytes	d2162	13	#7570B3	TMRC30041	0.0015	-0.0576	0.0015	-0.0576	-0.1239	-0.1954	-0.0319	-0.2339	0.0508	0.2420	0.0157	0.0832	0.0329	0.0225	0.2080	0.2217	-0.0492	-0.0294	-0.1074	-0.1387	-0.0074	0.1219	0.1519	-0.0604	-0.0257	-0.2070	-0.1880	0.1562	0.0145	0.2936	0.0138	-0.0395	0.0961	0.0227	-0.1294	-0.0261	0.3708	-0.3026	0.2154	0.0007	0.2791	0.0219	-0.2168	-0.0066	0.0539	-0.0198	0.0230	0.0202	0.0127	-0.0329	0.0122	-0.0510	0.0050	-0.0367	-0.0164	-0.0521	-0.0311	0.0181	0.0017	-0.0108	-0.0010	-0.0027	0.0001
tmrc30042	TMRC30042	Neutrophils	d2162	13	#D95F02	TMRC30042	0.1752	-0.0500	0.1752	-0.0500	0.1191	-0.0734	0.0273	0.0210	0.1763	0.1874	0.0671	-0.0680	-0.1020	0.0662	0.1090	0.0581	0.1250	0.3544	0.0828	-0.2454	0.0515	-0.1327	0.3813	-0.1345	-0.2315	0.3291	-0.1582	0.0465	-0.0016	-0.0512	-0.2005	-0.0294	0.0544	-0.2003	0.0202	-0.2279	-0.0676	0.2275	-0.0102	0.0179	-0.1394	-0.0499	-0.0923	0.1007	0.0364	-0.0165	-0.0288	0.0411	0.0086	0.0296	-0.0232	0.0145	0.0072	0.0175	0.0256	-0.0113	-0.0097	-0.0052	-0.0132	0.0053	0.0030	-0.0055	0.0016
tmrc30043	TMRC30043	Eosinophils	d2162	13	#66A61E	TMRC30043	0.1242	-0.0428	0.1242	-0.0428	-0.1557	0.1617	0.1770	-0.0195	-0.0270	0.1323	-0.0614	0.0419	0.0010	-0.0537	0.2702	0.0253	0.1343	0.1433	0.0403	-0.2207	-0.1372	-0.0624	0.0108	-0.2644	-0.2221	-0.3640	-0.0015	0.0803	-0.0083	-0.0994	0.1120	-0.0161	-0.2711	0.3168	-0.0552	0.1127	-0.2067	-0.0441	-0.1995	0.0100	-0.0385	0.0831	0.2823	-0.0683	-0.0248	-0.0527	0.0573	-0.0283	-0.0417	-0.0750	0.0466	0.0310	-0.0280	-0.0192	-0.0293	0.0409	0.0526	-0.0001	-0.0025	0.0177	-0.0064	0.0064	0.0044
tmrc30045	TMRC30045	Biopsy	d2162	13	#E7298A	TMRC30045	-0.1626	-0.1662	-0.1626	-0.1662	0.1176	0.1180	-0.0240	0.0015	-0.0240	0.0641	-0.3725	-0.3140	0.2377	-0.3411	0.2944	0.1338	-0.1710	-0.2894	0.2898	0.0197	0.2694	-0.1027	0.1423	0.0632	-0.0761	0.0925	0.1239	0.0417	-0.0143	0.0502	-0.0435	0.1083	-0.0059	0.0051	-0.0412	0.0174	-0.0418	0.0323	-0.0188	-0.0219	0.0363	-0.1296	0.0083	-0.0184	-0.0507	0.0342	0.0038	0.0106	-0.0076	0.0258	0.0178	-0.0044	0.0146	-0.0035	0.0019	0.0128	0.0025	0.0030	0.0095	-0.0053	-0.0014	0.0011	-0.0005
tmrc30059	TMRC30059	macrophage	unknown	14	#E6AB02	TMRC30059	-0.1304	0.1844	-0.1304	0.1844	-0.0013	-0.0526	0.0993	-0.0042	0.1227	0.0119	0.0999	-0.0794	0.0331	0.0005	-0.1680	0.1183	0.2702	-0.1273	-0.0143	-0.0640	-0.0374	0.0131	0.0331	0.0128	-0.0612	0.1026	0.0117	0.0716	-0.1047	0.0486	0.2040	0.2018	-0.0043	0.0123	-0.1074	0.0885	0.0009	0.1143	0.0220	0.0626	0.0876	-0.0142	-0.0631	-0.1184	-0.3963	-0.1460	0.1484	0.1038	-0.1149	-0.0683	-0.2557	0.2913	0.0863	0.0058	-0.0173	0.0521	-0.0573	-0.3051	0.1848	-0.1097	-0.1022	-0.0826	-0.1473
tmrc30060	TMRC30060	macrophage	unknown	14	#E6AB02	TMRC30060	-0.1244	0.1907	-0.1244	0.1907	0.0013	-0.0503	0.1114	0.0366	0.0949	0.0295	0.0581	-0.1577	0.2059	0.1972	-0.0111	-0.0447	-0.0495	0.0007	-0.0734	-0.0663	-0.0328	-0.0352	-0.0202	0.0366	-0.0502	0.0227	0.0198	0.0217	-0.0571	0.0008	0.1836	0.1577	0.0614	0.0408	-0.0609	0.0425	0.0318	0.0861	-0.0152	-0.0609	-0.0141	-0.0340	-0.0122	0.0533	0.0663	-0.0015	-0.0661	-0.0553	0.0475	0.0252	0.1523	-0.1905	-0.0361	-0.1299	0.0254	0.1259	-0.0390	-0.2480	-0.2561	0.3264	-0.1564	-0.1350	0.5460
tmrc30061	TMRC30061	macrophage	unknown	14	#E6AB02	TMRC30061	-0.1268	0.1860	-0.1268	0.1860	0.0150	-0.0554	0.1006	0.0298	0.0382	-0.0117	0.0104	0.0705	-0.1057	-0.1431	-0.0870	0.0621	0.1689	-0.0519	0.0207	0.0235	0.0168	0.0257	0.0428	-0.0069	-0.0540	0.0140	-0.0070	0.0138	-0.1318	0.0048	0.1228	0.1561	0.0626	0.0362	-0.0300	0.0402	0.0153	0.0688	-0.0173	-0.0380	0.0439	-0.0330	0.0098	-0.0103	-0.0182	0.0010	0.0446	0.0501	-0.0361	0.0042	-0.0902	0.0941	0.0237	0.1765	0.0083	-0.2256	0.0378	0.5397	-0.5532	-0.0255	0.1766	0.1059	0.0942
tmrc30062	TMRC30062	macrophage	unknown	14	#E6AB02	TMRC30062	-0.1220	0.1918	-0.1220	0.1918	0.0087	-0.0493	0.1107	0.0389	0.0939	-0.0093	0.0535	-0.0934	0.1585	0.1468	0.0163	-0.0834	-0.1374	0.0590	-0.0661	-0.0518	-0.0213	-0.0228	0.0056	0.0109	-0.0362	0.0338	0.0316	0.0114	-0.0013	0.0013	0.1080	0.0877	0.0098	0.0184	-0.0694	0.0364	-0.0162	0.0584	0.0214	0.0034	0.0139	-0.0262	-0.0228	-0.0410	-0.0800	-0.0225	-0.0413	-0.0197	0.0556	0.0120	0.1690	-0.2018	-0.0676	0.0186	0.0290	-0.0575	0.0128	0.1923	0.2485	0.4304	0.1216	0.4138	-0.4624
tmrc30063	TMRC30063	macrophage	unknown	14	#E6AB02	TMRC30063	-0.1216	0.1816	-0.1216	0.1816	0.0328	-0.0529	0.0864	0.0164	0.0552	-0.0504	0.0243	0.2509	-0.2492	-0.2930	0.0054	-0.0346	-0.0274	0.0413	0.0476	0.0686	0.0352	0.0533	0.1071	0.0199	-0.0733	-0.0015	0.0123	-0.0194	-0.1446	0.0142	0.0960	0.1304	0.0771	0.0389	-0.0076	0.0380	-0.0010	0.0435	0.0028	-0.0019	-0.0254	-0.0098	0.0247	0.0047	0.0995	0.0327	-0.0308	-0.0413	0.0391	0.0617	0.1747	-0.1930	-0.0841	0.0378	0.0452	-0.0561	0.0227	0.2335	0.4609	-0.0247	-0.2301	-0.4367	0.0584
tmrc30051	TMRC30051	macrophage	unknown	14	#E6AB02	TMRC30051	-0.1168	0.1897	-0.1168	0.1897	0.0276	-0.0455	0.1104	0.0614	-0.0141	-0.0236	0.0023	0.0747	0.0110	0.0634	0.1484	-0.1447	-0.3035	0.1412	0.0064	0.0149	0.0084	0.0113	0.0341	0.0009	0.0774	0.0030	0.0058	-0.0059	0.0936	0.0141	-0.0886	-0.0985	-0.0896	-0.0574	0.0268	-0.0435	-0.0464	-0.0440	0.0523	0.1294	0.0083	0.0313	-0.0352	-0.1515	-0.3525	-0.0664	0.1086	0.0601	-0.0513	0.0350	0.0259	0.0177	-0.0302	0.0521	0.0099	0.0142	0.0570	0.0516	0.2162	-0.2754	0.3645	0.1649	0.4603
tmrc30064	TMRC30064	macrophage	unknown	14	#E6AB02	TMRC30064	-0.1269	0.1862	-0.1269	0.1862	0.0079	-0.0453	0.0990	0.0575	0.0389	0.0356	-0.0430	-0.0510	-0.0197	-0.0704	-0.1217	0.1770	0.3265	-0.1592	0.0130	-0.0194	0.0165	0.0111	-0.0925	-0.0741	0.0355	0.0163	0.0235	0.0081	0.1825	-0.0369	-0.2169	-0.2216	-0.1228	-0.0694	0.0448	-0.0594	-0.0387	-0.1065	0.0385	0.0601	-0.0235	0.0316	-0.0170	-0.0148	-0.0490	-0.0015	-0.0405	-0.0377	0.0486	0.0580	0.2021	-0.2077	-0.0663	-0.0620	0.0553	0.0339	0.0454	0.0434	-0.0202	-0.2397	-0.4742	0.3679	0.0664
tmrc30065	TMRC30065	macrophage	unknown	14	#E6AB02	TMRC30065	-0.1239	0.1907	-0.1239	0.1907	0.0067	-0.0480	0.1158	0.0711	0.0136	0.0443	0.0108	-0.1331	0.1768	0.2226	0.0304	-0.0445	-0.0683	0.0014	-0.0404	-0.0379	-0.0251	-0.0269	-0.0291	0.0064	-0.0414	-0.0033	-0.0052	-0.0102	-0.0216	-0.0319	0.0609	0.0464	0.0471	0.0389	0.0004	0.0162	0.0201	0.0213	-0.0066	-0.0452	-0.0117	-0.0284	0.0460	0.0919	0.2098	0.0637	-0.0564	-0.0473	0.0435	0.0650	0.1502	-0.1435	-0.0717	-0.0385	0.0278	0.0705	0.1075	-0.1045	-0.1591	-0.6124	0.2178	-0.2444	-0.3539
tmrc30066	TMRC30066	macrophage	unknown	14	#E6AB02	TMRC30066	-0.1207	0.1922	-0.1207	0.1922	0.0133	-0.0490	0.1227	0.0996	-0.0784	0.0457	-0.0587	-0.1172	0.1670	0.2230	0.0886	-0.0334	-0.0873	0.0142	-0.0212	-0.0070	-0.0091	-0.0317	-0.0501	0.0022	0.0433	-0.0462	-0.0319	-0.0291	0.0303	-0.0310	-0.0734	-0.0712	0.0318	0.0364	0.0343	-0.0291	0.0231	-0.0218	-0.0572	-0.1270	-0.0008	-0.0233	0.0634	0.1509	0.3902	0.0969	-0.0700	-0.0122	-0.0120	-0.1445	-0.3703	0.3723	0.1526	0.0986	-0.1014	-0.1421	-0.0684	0.1746	0.2745	-0.0244	-0.2327	0.0728	0.1152
tmrc30067	TMRC30067	macrophage	unknown	14	#E6AB02	TMRC30067	-0.1248	0.1874	-0.1248	0.1874	0.0087	-0.0466	0.1038	0.0762	-0.0213	0.0426	-0.0770	-0.0663	-0.0001	-0.0426	-0.0833	0.1806	0.3082	-0.1468	0.0233	-0.0085	0.0243	0.0050	-0.1049	-0.0649	0.0804	-0.0167	-0.0020	-0.0042	0.1786	-0.0214	-0.2563	-0.2627	-0.1075	-0.0589	0.0645	-0.0732	-0.0105	-0.1097	-0.0020	0.0048	-0.0355	0.0484	-0.0067	-0.0086	0.0528	0.0047	-0.0222	0.0023	0.0196	-0.0092	-0.0037	0.0074	0.0178	-0.0076	-0.0418	0.0063	-0.0255	-0.0358	0.0653	0.3554	0.4710	-0.3899	-0.0306
tmrc30057	TMRC30057	macrophage	unknown	14	#E6AB02	TMRC30057	-0.1226	0.1813	-0.1226	0.1813	0.0396	-0.0528	0.0953	0.0374	-0.0195	-0.0233	-0.0359	0.2810	-0.2809	-0.3020	0.0435	-0.0069	-0.0177	0.0156	0.0795	0.1031	0.0499	0.0636	0.1068	0.0191	-0.0026	-0.0389	-0.0301	-0.0188	-0.1233	-0.0005	-0.0001	0.0465	0.1029	0.0514	0.0534	0.0095	0.0471	-0.0078	-0.0584	-0.0548	-0.0436	0.0072	0.0287	0.1240	0.2751	0.0813	-0.0591	-0.0450	0.0241	-0.0204	-0.0737	0.0676	0.0297	-0.1227	-0.0616	0.1840	-0.0237	-0.4599	-0.0571	0.0331	0.2029	0.3974	-0.0352
tmrc30069	TMRC30069	macrophage	unknown	14	#E6AB02	TMRC30069	-0.1160	0.1888	-0.1160	0.1888	0.0377	-0.0459	0.1088	0.0762	-0.0584	-0.0328	-0.0471	0.1481	-0.0638	-0.0282	0.2000	-0.1495	-0.3371	0.1659	0.0207	0.0699	0.0307	0.0113	0.0324	0.0142	0.0879	-0.0421	-0.0087	-0.0515	0.0960	0.0017	-0.1461	-0.1628	-0.0622	-0.0658	0.0537	-0.0610	-0.0411	-0.0822	0.0278	0.0758	-0.0011	0.0459	-0.0179	-0.0856	-0.2060	-0.0524	0.0917	0.0487	-0.0708	-0.0089	-0.0791	0.0878	0.0389	-0.0233	0.0187	0.0070	-0.0671	-0.0862	-0.3954	0.1695	-0.3638	-0.2427	-0.3077

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'

3.3 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
tmrc30001	TMRC30001	PBMCs	d1010	1	#1B9E77	TMRC30001	-0.5759	0.2704	-0.5759	0.2704	0.4004	-0.0566	0.5148
tmrc30002	TMRC30002	PBMCs	d1010	1	#1B9E77	TMRC30002	0.0048	0.4828	0.0048	0.4828	-0.6648	0.3977	-0.0112
tmrc30003	TMRC30003	PBMCs	d1010	1	#1B9E77	TMRC30003	0.4107	0.4514	0.4107	0.4514	0.4033	-0.3107	-0.4492
tmrc30004	TMRC30004	PBMCs	d1011	2	#1B9E77	TMRC30004	-0.4764	-0.4166	-0.4764	-0.4166	-0.2714	-0.3745	-0.4679
tmrc30005	TMRC30005	PBMCs	d1011	2	#1B9E77	TMRC30005	0.1313	-0.4486	0.1313	-0.4486	0.3425	0.6930	-0.1315
tmrc30006	TMRC30006	PBMCs	d1011	2	#1B9E77	TMRC30006	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"))

## 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"))

## 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.

## Warning: Removed 1 rows containing missing values (geom_abline).

## Warning: Removed 1 rows containing missing values (geom_abline).

## 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"))

## 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"))

## 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
tmrc30009	TMRC30009	Neutrophils	d1034	1	#D95F02	TMRC30009	-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
tmrc30011	TMRC30011	Neutrophils	d1034	1	#D95F02	TMRC30011	-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
tmrc30012	TMRC30012	Monocytes	d1034	1	#7570B3	TMRC30012	-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
tmrc30013	TMRC30013	Monocytes	d1034	1	#7570B3	TMRC30013	-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
tmrc30016	TMRC30016	Biopsy	d2050	2	#E7298A	TMRC30016	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
tmrc30017	TMRC30017	Biopsy	d2052	3	#E7298A	TMRC30017	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
tmrc30071	TMRC30071	Eosinophils	d2052	3	#66A61E	TMRC30071	-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
tmrc30058	TMRC30058	Neutrophils	d2052	3	#D95F02	TMRC30058	-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
tmrc30018	TMRC30018	Biopsy	d2065	4	#E7298A	TMRC30018	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
tmrc30019	TMRC30019	Biopsy	d2066	5	#E7298A	TMRC30019	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
tmrc30014	TMRC30014	Monocytes	d2068	6	#7570B3	TMRC30014	-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
tmrc30021	TMRC30021	Neutrophils	d2068	6	#D95F02	TMRC30021	-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
tmrc30029	TMRC30029	Eosinophils	d2068	6	#66A61E	TMRC30029	-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
tmrc30020	TMRC30020	Biopsy	d2068	6	#E7298A	TMRC30020	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
tmrc30039	TMRC30039	Neutrophils	d2072	8	#D95F02	TMRC30039	-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
tmrc30023	TMRC30023	Eosinophils	d2072	8	#66A61E	TMRC30023	-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
tmrc30025	TMRC30025	Biopsy	d2072	8	#E7298A	TMRC30025	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
tmrc30022	TMRC30022	Biopsy	d2071	7	#E7298A	TMRC30022	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
tmrc30044	TMRC30044	Monocytes	d2073	9	#7570B3	TMRC30044	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
tmrc30048	TMRC30048	Eosinophils	d2073	9	#66A61E	TMRC30048	-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
tmrc30026	TMRC30026	Biopsy	d2073	9	#E7298A	TMRC30026	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
tmrc30030	TMRC30030	Monocytes	d2068	6	#7570B3	TMRC30030	-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
tmrc30031	TMRC30031	Neutrophils	d2068	6	#D95F02	TMRC30031	-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
tmrc30032	TMRC30032	Eosinophils	d2068	6	#66A61E	TMRC30032	-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
tmrc30024	TMRC30024	Monocytes	d2072	8	#7570B3	TMRC30024	-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
tmrc30040	TMRC30040	Neutrophils	d2072	8	#D95F02	TMRC30040	-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
tmrc30033	TMRC30033	Eosinophils	d2072	8	#66A61E	TMRC30033	-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
tmrc30049	TMRC30049	Monocytes	d2073	9	#7570B3	TMRC30049	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
tmrc30053	TMRC30053	Neutrophils	d2073	9	#D95F02	TMRC30053	-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
tmrc30054	TMRC30054	Eosinophils	d2073	9	#66A61E	TMRC30054	-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
tmrc30037	TMRC30037	Monocytes	d2068	6	#7570B3	TMRC30037	-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
tmrc30027	TMRC30027	Neutrophils	d2068	6	#D95F02	TMRC30027	-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
tmrc30028	TMRC30028	Eosinophils	d2068	6	#66A61E	TMRC30028	-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
tmrc30034	TMRC30034	Monocytes	d2072	8	#7570B3	TMRC30034	-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
tmrc30035	TMRC30035	Neutrophils	d2072	8	#D95F02	TMRC30035	-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
tmrc30036	TMRC30036	Eosinophils	d2072	8	#66A61E	TMRC30036	-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
tmrc30044.1	TMRC30044.1	Biopsy	d2159	10	#E7298A	TMRC30044.	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
tmrc30055	TMRC30055	Monocytes	d2073	9	#7570B3	TMRC30055	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
tmrc30068	TMRC30068	Neutrophils	d2073	9	#D95F02	TMRC30068	-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
tmrc30070	TMRC30070	Eosinophils	d2073	9	#66A61E	TMRC30070	-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
tmrc30041	TMRC30041	Monocytes	d2162	11	#7570B3	TMRC30041	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
tmrc30042	TMRC30042	Neutrophils	d2162	11	#D95F02	TMRC30042	-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
tmrc30043	TMRC30043	Eosinophils	d2162	11	#66A61E	TMRC30043	-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
tmrc30045	TMRC30045	Biopsy	d2162	11	#E7298A	TMRC30045	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
tmrc30059	TMRC30059	macrophage	unknown	12	#E6AB02	TMRC30059	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
tmrc30060	TMRC30060	macrophage	unknown	12	#E6AB02	TMRC30060	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
tmrc30061	TMRC30061	macrophage	unknown	12	#E6AB02	TMRC30061	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
tmrc30062	TMRC30062	macrophage	unknown	12	#E6AB02	TMRC30062	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
tmrc30063	TMRC30063	macrophage	unknown	12	#E6AB02	TMRC30063	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
tmrc30051	TMRC30051	macrophage	unknown	12	#E6AB02	TMRC30051	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
tmrc30064	TMRC30064	macrophage	unknown	12	#E6AB02	TMRC30064	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
tmrc30065	TMRC30065	macrophage	unknown	12	#E6AB02	TMRC30065	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
tmrc30066	TMRC30066	macrophage	unknown	12	#E6AB02	TMRC30066	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
tmrc30067	TMRC30067	macrophage	unknown	12	#E6AB02	TMRC30067	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
tmrc30057	TMRC30057	macrophage	unknown	12	#E6AB02	TMRC30057	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
tmrc30069	TMRC30069	macrophage	unknown	12	#E6AB02	TMRC30069	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)