1 Which genes are DE in human macrophages at 4 hours upon infection with L. major?

2 Gather annotation data

I want to perform a series of comparisons among the host cells: human and mouse. Thus I need to collect annotation data for both species and get the set of orthologs between them.

2.2 Generate expressionsets

The question is reasonably self-contained. I want to compare the uninfected human samples against any samples which were infected for 4 hours. So let us first pull those samples and then poke at them a bit.

## Reading the sample metadata.
## The sample definitions comprises: 437 rows(samples) and 55 columns(metadata fields).
## Reading count tables.
## Using the transcript to gene mapping.
## Reading salmon data with tximport.
## Finished reading count tables.
## Matched 19629 annotations and counts.
## Bringing together the count matrix and gene information.
## The mapped IDs are not the rownames of your gene information, changing them now.
## Some annotations were lost in merging, setting them to 'undefined'.
## There were 267, now there are 247 samples.
## There were 247, now there are 64 samples.
## 
## bead   no stim  yes 
##    3   18   35    8
## 
## lps-timecourse       m-gm-csf           mbio 
##              8             39             17

2.3 Examine t4h vs uninfected

## This function will replace the expt$expressionset slot with:
## log2(svaseq(cpm(quant(cbcb(data)))))
## It backs up the current data into a slot named:
##  expt$backup_expressionset. It will also save copies of each step along the way
##  in expt$normalized with the corresponding libsizes. Keep the libsizes in mind
##  when invoking limma.  The appropriate libsize is the non-log(cpm(normalized)).
##  This is most likely kept at:
##  'new_expt$normalized$intermediate_counts$normalization$libsizes'
##  A copy of this may also be found at:
##  new_expt$best_libsize
## Warning in normalize_expt(hs_t4h_expt, norm = "quant", convert = "cpm", :
## Quantile normalization and sva do not always play well together.
## Step 1: performing count filter with option: cbcb
## Removing 7360 low-count genes (12269 remaining).
## Step 2: normalizing the data with quant.
## Using normalize.quantiles.robust due to a thread error in preprocessCore.
## Step 3: converting the data with cpm.
## Step 4: transforming the data with log2.
## transform_counts: Found 3822 values equal to 0, adding 1 to the matrix.
## Step 5: doing batch correction with svaseq.
## Note to self:  If you get an error like 'x contains missing values' The data has too many 0's and needs a stronger low-count filter applied.
## Passing off to all_adjusters.
## batch_counts: Before batch/surrogate estimation, 711279 entries are x>1: 90.6%.
## batch_counts: Before batch/surrogate estimation, 3822 entries are x==0: 0.487%.
## batch_counts: Before batch/surrogate estimation, 70115 entries are 0<x<1: 8.93%.
## The be method chose 12 surrogate variable(s).
## Attempting svaseq estimation with 12 surrogates.
## There are 1760 (0.224%) elements which are < 0 after batch correction.

2.4 Remove stimulated samples

## There were 64, now there are 29 samples.
## This function will replace the expt$expressionset slot with:
## log2(svaseq(cpm(cbcb(data))))
## It backs up the current data into a slot named:
##  expt$backup_expressionset. It will also save copies of each step along the way
##  in expt$normalized with the corresponding libsizes. Keep the libsizes in mind
##  when invoking limma.  The appropriate libsize is the non-log(cpm(normalized)).
##  This is most likely kept at:
##  'new_expt$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 7759 low-count genes (11870 remaining).
## Step 2: not normalizing the data.
## Step 3: converting the data with cpm.
## Step 4: transforming the data with log2.
## transform_counts: Found 3276 values equal to 0, adding 1 to the matrix.
## Step 5: doing batch correction with svaseq.
## Note to self:  If you get an error like 'x contains missing values' The data has too many 0's and needs a stronger low-count filter applied.
## Passing off to all_adjusters.
## batch_counts: Before batch/surrogate estimation, 319013 entries are x>1: 92.7%.
## batch_counts: Before batch/surrogate estimation, 3276 entries are x==0: 0.952%.
## batch_counts: Before batch/surrogate estimation, 21941 entries are 0<x<1: 6.37%.
## The be method chose 6 surrogate variable(s).
## Attempting svaseq estimation with 6 surrogates.
## There are 557 (0.162%) elements which are < 0 after batch correction.

## batch_counts: Before batch/surrogate estimation, 339179 entries are x>1: 98.5%.
## batch_counts: Before batch/surrogate estimation, 3276 entries are x==0: 0.952%.
## batch_counts: Before batch/surrogate estimation, 216 entries are 0<x<1: 0.0627%.
## The be method chose 5 surrogate variable(s).
## Attempting svaseq estimation with 5 surrogates.
## This function will replace the expt$expressionset slot with:
## log2(cpm(quant(cbcb(data))))
## It backs up the current data into a slot named:
##  expt$backup_expressionset. It will also save copies of each step along the way
##  in expt$normalized with the corresponding libsizes. Keep the libsizes in mind
##  when invoking limma.  The appropriate libsize is the non-log(cpm(normalized)).
##  This is most likely kept at:
##  'new_expt$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 0 low-count genes (11870 remaining).
## Step 2: normalizing the data with quant.
## Using normalize.quantiles.robust due to a thread error in preprocessCore.
## Step 3: converting the data with cpm.
## Step 4: transforming the data with log2.
## transform_counts: Found 1964 values equal to 0, adding 1 to the matrix.
## Step 5: not doing batch correction.
## Plotting a PCA before surrogates/batch inclusion.
## Using svaseq to visualize before/after batch inclusion.
## Performing a test normalization with: raw
## This function will replace the expt$expressionset slot with:
## log2(svaseq(cpm(cbcb(data))))
## It backs up the current data into a slot named:
##  expt$backup_expressionset. It will also save copies of each step along the way
##  in expt$normalized with the corresponding libsizes. Keep the libsizes in mind
##  when invoking limma.  The appropriate libsize is the non-log(cpm(normalized)).
##  This is most likely kept at:
##  'new_expt$normalized$intermediate_counts$normalization$libsizes'
##  A copy of this may also be found at:
##  new_expt$best_libsize
## Leaving the data unnormalized.  This is necessary for DESeq, but
##  EdgeR/limma might benefit from normalization.  Good choices include quantile,
##  size-factor, tmm, etc.
## Step 1: performing count filter with option: cbcb
## Removing 0 low-count genes (11870 remaining).
## Step 2: not normalizing the data.
## Step 3: converting the data with cpm.
## Step 4: transforming the data with log2.
## transform_counts: Found 3276 values equal to 0, adding 1 to the matrix.
## Step 5: doing batch correction with svaseq.
## Note to self:  If you get an error like 'x contains missing values' The data has too many 0's and needs a stronger low-count filter applied.
## Passing off to all_adjusters.
## batch_counts: Before batch/surrogate estimation, 319013 entries are x>1: 92.7%.
## batch_counts: Before batch/surrogate estimation, 3276 entries are x==0: 0.952%.
## batch_counts: Before batch/surrogate estimation, 21941 entries are 0<x<1: 6.37%.
## The be method chose 6 surrogate variable(s).
## Attempting svaseq estimation with 6 surrogates.
## There are 557 (0.162%) elements which are < 0 after batch correction.
## Finished running DE analyses, collecting outputs.
## Comparing analyses.

## Writing a legend of columns.
## Working on 1/1: infection which is: yes/no.
## Found table with yes_vs_no
## 20181210 a pthread error in normalize.quantiles leads me to robust.
## Used Bon Ferroni corrected t test(s) between columns.
## Used Bon Ferroni corrected t test(s) between columns.
## Used Bon Ferroni corrected t test(s) between columns.

3 Which genes are DE in mouse macrophages at 4 hours upon infection with L. major?

4 Gather annotation data

I want to perform a series of comparisons among the host cells: human and mouse. Thus I need to collect annotation data for both species and get the set of orthologs between them.

4.2 Generate expressionsets

The question is reasonably self-contained. I want to compare the uninfected human samples against any samples which were infected for 4 hours. So let us first pull those samples and then poke at them a bit.

## Reading the sample metadata.
## The sample definitions comprises: 437 rows(samples) and 55 columns(metadata fields).
## Reading count tables.
## Using the transcript to gene mapping.
## Reading salmon data with tximport.
## Finished reading count tables.
## Matched 19660 annotations and counts.
## Bringing together the count matrix and gene information.
## The mapped IDs are not the rownames of your gene information, changing them now.
## Some annotations were lost in merging, setting them to 'undefined'.
## There were 105, now there are 41 samples.
## 
##   no stim  yes 
##   11   24    6
## 
## undefined 
##        41

4.3 Examine t4h vs uninfected

## This function will replace the expt$expressionset slot with:
## log2(svaseq(cpm(quant(cbcb(data)))))
## It backs up the current data into a slot named:
##  expt$backup_expressionset. It will also save copies of each step along the way
##  in expt$normalized with the corresponding libsizes. Keep the libsizes in mind
##  when invoking limma.  The appropriate libsize is the non-log(cpm(normalized)).
##  This is most likely kept at:
##  'new_expt$normalized$intermediate_counts$normalization$libsizes'
##  A copy of this may also be found at:
##  new_expt$best_libsize
## Warning in normalize_expt(mm_t4h_expt, norm = "quant", convert = "cpm", :
## Quantile normalization and sva do not always play well together.
## Step 1: performing count filter with option: cbcb
## Removing 9350 low-count genes (10310 remaining).
## Step 2: normalizing the data with quant.
## Using normalize.quantiles.robust due to a thread error in preprocessCore.
## Step 3: converting the data with cpm.
## Step 4: transforming the data with log2.
## transform_counts: Found 38 values equal to 0, adding 1 to the matrix.
## Step 5: doing batch correction with svaseq.
## Note to self:  If you get an error like 'x contains missing values' The data has too many 0's and needs a stronger low-count filter applied.
## Passing off to all_adjusters.
## batch_counts: Before batch/surrogate estimation, 390888 entries are x>1: 92.5%.
## batch_counts: Before batch/surrogate estimation, 38 entries are x==0: 0.00899%.
## batch_counts: Before batch/surrogate estimation, 31784 entries are 0<x<1: 7.52%.
## The be method chose 8 surrogate variable(s).
## Attempting svaseq estimation with 8 surrogates.
## There are 807 (0.191%) elements which are < 0 after batch correction.

5 Compare this to the previous result.

Let us see if our human differential expression result is similar to that obtained in Table S2.

6 Compare the previous human and these human results.

## Error in eval(expr, envir, enclos): object 'previous_hs_lfc' not found
## Error in eval(expr, envir, enclos): object 'previous_hs_lfc' not found
## Error in eval(expr, envir, enclos): object 'previous_hs_lfc' not found
## Error in eval(expr, envir, enclos): object 'previous_hs_lfc' not found
## Error in merge(previous_hs_lfc, hs_t4h_table$data[[1]], by.x = "ID", by.y = "row.names"): object 'previous_hs_lfc' not found
## Error in cor.test(merged[["limma_logfc"]], merged[["Fold change"]]): object 'merged' not found

7 Compare the previous mouse and these mouse results.

## 
##  Pearson's product-moment correlation
## 
## data:  merged[["limma_logfc"]] and merged[["Fold change"]]
## t = 220, df = 5700, p-value <2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.9445 0.9498
## sample estimates:
##    cor 
## 0.9472

First get the set of orthologs.

Side note: a different way of addressing this question resides in 20190220_host_comparisons.Rmd.

## 
##  Pearson's product-moment correlation
## 
## data:  both_table_hs[["limma_logfc.x"]] and both_table_hs[["limma_logfc.y"]]
## t = 34, df = 14000, p-value <2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.2656 0.2964
## sample estimates:
##    cor 
## 0.2811
## 
##  Pearson's product-moment correlation
## 
## data:  both_table_mm[["limma_logfc.x"]] and both_table_mm[["limma_logfc.y"]]
## t = 34, df = 22000, p-value <2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.2135 0.2387
## sample estimates:
##    cor 
## 0.2261

8 Compare the above with Table S7 from the Laura and Cecilia paper.

Table S7 has a set of genes from human which are also up-regulated in mouse upon infection.

R version 3.5.3 (2019-03-11)

Platform: x86_64-pc-linux-gnu (64-bit)

locale: LC_CTYPE=en_US.utf8, LC_NUMERIC=C, LC_TIME=en_US.utf8, LC_COLLATE=en_US.utf8, LC_MONETARY=en_US.utf8, LC_MESSAGES=en_US.utf8, LC_PAPER=en_US.utf8, LC_NAME=C, LC_ADDRESS=C, LC_TELEPHONE=C, LC_MEASUREMENT=en_US.utf8 and LC_IDENTIFICATION=C

attached base packages: parallel, stats, graphics, grDevices, utils, datasets, methods and base

other attached packages: ruv(v.0.9.7), hpgltools(v.1.0), Biobase(v.2.42.0) and BiocGenerics(v.0.28.0)

loaded via a namespace (and not attached): tidyselect(v.0.2.5), lme4(v.1.1-21), htmlwidgets(v.1.3), RSQLite(v.2.1.1), AnnotationDbi(v.1.44.0), grid(v.3.5.3), BiocParallel(v.1.16.6), Rtsne(v.0.15), devtools(v.2.0.1), munsell(v.0.5.0), codetools(v.0.2-16), preprocessCore(v.1.45.0), withr(v.2.1.2), colorspace(v.1.4-0), GOSemSim(v.2.8.0), knitr(v.1.22), rstudioapi(v.0.9.0), stats4(v.3.5.3), Vennerable(v.3.1.0.9000), robustbase(v.0.93-3), DOSE(v.3.8.2), labeling(v.0.3), urltools(v.1.7.2), tximport(v.1.10.1), GenomeInfoDbData(v.1.2.0), polyclip(v.1.9-1), bit64(v.0.9-7), farver(v.1.1.0), rprojroot(v.1.3-2), xfun(v.0.5), R6(v.2.4.0), doParallel(v.1.0.14), GenomeInfoDb(v.1.18.2), locfit(v.1.5-9.1), bitops(v.1.0-6), fgsea(v.1.8.0), gridGraphics(v.0.3-0), DelayedArray(v.0.8.0), assertthat(v.0.2.0), scales(v.1.0.0), ggraph(v.1.0.2), nnet(v.7.3-12), enrichplot(v.1.2.0), gtable(v.0.2.0), sva(v.3.30.1), processx(v.3.3.0), rlang(v.0.3.1), genefilter(v.1.64.0), splines(v.3.5.3), rtracklayer(v.1.42.2), lazyeval(v.0.2.1), acepack(v.1.4.1), checkmate(v.1.9.1), europepmc(v.0.3), yaml(v.2.2.0), reshape2(v.1.4.3), GenomicFeatures(v.1.34.4), backports(v.1.1.3), qvalue(v.2.14.1), Hmisc(v.4.2-0), RBGL(v.1.58.1), clusterProfiler(v.3.10.1), tools(v.3.5.3), usethis(v.1.4.0), ggplotify(v.0.0.3), ggplot2(v.3.1.0), gplots(v.3.0.1.1), RColorBrewer(v.1.1-2), sessioninfo(v.1.1.1), ggridges(v.0.5.1), Rcpp(v.1.0.1), plyr(v.1.8.4), base64enc(v.0.1-3), progress(v.1.2.0), zlibbioc(v.1.28.0), purrr(v.0.3.1), RCurl(v.1.95-4.12), ps(v.1.3.0), prettyunits(v.1.0.2), rpart(v.4.1-13), viridis(v.0.5.1), cowplot(v.0.9.4), S4Vectors(v.0.20.1), SummarizedExperiment(v.1.12.0), ggrepel(v.0.8.0), cluster(v.2.0.7-1), colorRamps(v.2.3), fs(v.1.2.6), variancePartition(v.1.12.1), magrittr(v.1.5), data.table(v.1.12.0), DO.db(v.2.9), openxlsx(v.4.1.0), triebeard(v.0.3.0), packrat(v.0.5.0), matrixStats(v.0.54.0), pkgload(v.1.0.2), hms(v.0.4.2), evaluate(v.0.13), xtable(v.1.8-3), pbkrtest(v.0.4-7), XML(v.3.98-1.19), readxl(v.1.3.0), IRanges(v.2.16.0), gridExtra(v.2.3), testthat(v.2.0.1), compiler(v.3.5.3), biomaRt(v.2.38.0), tibble(v.2.0.1), KernSmooth(v.2.23-15), crayon(v.1.3.4), minqa(v.1.2.4), htmltools(v.0.3.6), mgcv(v.1.8-27), corpcor(v.1.6.9), Formula(v.1.2-3), geneplotter(v.1.60.0), tidyr(v.0.8.3), DBI(v.1.0.0), tweenr(v.1.0.1), MASS(v.7.3-51.1), boot(v.1.3-20), Matrix(v.1.2-16), readr(v.1.3.1), cli(v.1.0.1), quadprog(v.1.5-5), gdata(v.2.18.0), igraph(v.1.2.4), GenomicRanges(v.1.34.0), pkgconfig(v.2.0.2), rvcheck(v.0.1.3), GenomicAlignments(v.1.18.1), foreign(v.0.8-71), xml2(v.1.2.0), foreach(v.1.4.4), annotate(v.1.60.1), XVector(v.0.22.0), stringr(v.1.4.0), callr(v.3.1.1), digest(v.0.6.18), graph(v.1.60.0), Biostrings(v.2.50.2), cellranger(v.1.1.0), rmarkdown(v.1.11), fastmatch(v.1.1-0), htmlTable(v.1.13.1), edgeR(v.3.24.3), directlabels(v.2018.05.22), curl(v.3.3), Rsamtools(v.1.34.1), gtools(v.3.8.1), nloptr(v.1.2.1), nlme(v.3.1-137), jsonlite(v.1.6), desc(v.1.2.0), viridisLite(v.0.3.0), limma(v.3.38.3), pillar(v.1.3.1), lattice(v.0.20-38), DEoptimR(v.1.0-8), httr(v.1.4.0), pkgbuild(v.1.0.2), survival(v.2.43-3), GO.db(v.3.7.0), glue(v.1.3.1), remotes(v.2.0.2), zip(v.2.0.1), UpSetR(v.1.3.3), iterators(v.1.0.10), pander(v.0.6.3), bit(v.1.1-14), ggforce(v.0.2.1), stringi(v.1.4.3), blob(v.1.1.1), DESeq2(v.1.22.2), latticeExtra(v.0.6-28), caTools(v.1.17.1.2), memoise(v.1.1.0) and dplyr(v.0.8.0.1)

## If you wish to reproduce this exact build of hpgltools, invoke the following:
## > git clone http://github.com/abelew/hpgltools.git
## > git reset dcf2a1659165dacca91ade9f3f8e56f234633e55
## This is hpgltools commit: Thu Apr 4 14:58:27 2019 -0400: dcf2a1659165dacca91ade9f3f8e56f234633e55
---
title: "20190326 An attempt to answer one of the big questions from Najib."
author: "atb abelew@gmail.com"
date: "`r Sys.Date()`"
output:
  html_document:
    code_download: true
    code_folding: show
    fig_caption: true
    fig_height: 7
    fig_width: 7
    highlight: tango
    keep_md: false
    mode: selfcontained
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    self_contained: true
    theme: readable
    toc: true
    toc_float:
      collapsed: false
      smooth_scroll: false
  rmdformats::readthedown:
    code_download: true
    code_folding: show
    df_print: paged
    fig_caption: true
    fig_height: 7
    fig_width: 7
    highlight: tango
    width: 300
    keep_md: false
    mode: selfcontained
    toc_float: true
  BiocStyle::html_document:
    code_download: true
    code_folding: show
    fig_caption: true
    fig_height: 7
    fig_width: 7
    highlight: tango
    keep_md: false
    mode: selfcontained
    toc_float: true
---

<style type="text/css">
body, td {
  font-size: 16px;
}
code.r{
  font-size: 16px;
}
pre {
 font-size: 16px
}
</style>

```{r options, include=FALSE}
library(hpgltools)
tt <- sm(devtools::load_all("~/hpgltools"))
knitr::opts_knit$set(progress=TRUE,
                     verbose=TRUE,
                     width=120,
                     echo=TRUE)
knitr::opts_chunk$set(error=TRUE,
                      fig.width=8,
                      fig.height=8,
                      dpi=96)
old_options <- options(digits=4,
                       stringsAsFactors=FALSE,
                       knitr.duplicate.label="allow")
ggplot2::theme_set(ggplot2::theme_bw(base_size=12))
ver <- "20190326"
rundate <- format(Sys.Date(), format="%Y%m%d")
rmd_file <- paste0("20190326_human_macrophages.Rmd")
```

# Which genes are DE in human macrophages at 4 hours upon infection with L. major?

# Gather annotation data

I want to perform a series of comparisons among the host cells: human and mouse.
Thus I need to collect annotation data for both species and get the set of
orthologs between them.

## Start with the human annotation data

```{r human_annotations}
hs_annot <- load_biomart_annotations()$annotation
rownames(hs_annot) <- make.names(
  paste0(hs_annot[["ensembl_transcript_id"]], ".",
         hs_annot[["transcript_version"]]),
  unique=TRUE)
hs_tx_gene <- hs_annot[, c("ensembl_gene_id", "ensembl_transcript_id")]
hs_tx_gene[["id"]] <- rownames(hs_tx_gene)
hs_tx_gene <- hs_tx_gene[, c("id", "ensembl_gene_id")]
new_hs_annot <- hs_annot
rownames(new_hs_annot) <- make.names(hs_annot[["ensembl_gene_id"]], unique=TRUE)
```

## Generate expressionsets

The question is reasonably self-contained.  I want to compare the uninfected
human samples against any samples which were infected for 4 hours.
So let us first pull those samples and then poke at them a bit.

```{r expts}
sample_sheet <- "sample_sheets/leishmania_host_metasheet_20190401.xlsx"
hs_expt <- create_expt(sample_sheet,
                       file_column="hsapiensfile",
                       gene_info=new_hs_annot,
                       tx_gene_map=hs_tx_gene)

hs_expt_noskipped <- subset_expt(hs_expt, subset="skipped!='yes'")
hs_t4h_expt <- subset_expt(hs_expt_noskipped, subset="expttime=='t4h'")
hs_t4h_expt <- set_expt_conditions(hs_t4h_expt, fact="infectstate")
hs_t4h_expt <- set_expt_batches(hs_t4h_expt, fact="study")
table(hs_t4h_expt$conditions)
table(hs_t4h_expt$batches)
```

## Examine t4h vs uninfected

```{r hs_examine_t4h, fig.show='hide'}
hs_t4h_plots <- sm(graph_metrics(hs_t4h_expt))

hs_t4h_norm <- normalize_expt(hs_t4h_expt, norm="quant", convert="cpm",
                              transform="log2", filter=TRUE, batch="svaseq")
hs_t4h_norm_plots <- sm(graph_metrics(hs_t4h_norm))
```

### Print some of the plots

```{r hs_examine_plots}
hs_t4h_plots$legend
hs_t4h_plots$libsize
hs_t4h_plots$boxplot

hs_t4h_norm_plots$pc_plot
```

## Remove stimulated samples

```{r hs_t4h_nounstim}
hs_t4h_inf <- subset_expt(hs_t4h_expt, subset="condition!='stim'")
hs_t4h_inf_norm <- normalize_expt(hs_t4h_inf, transform="log2", convert="cpm",
                                  filter=TRUE, batch="svaseq")

hs_t4h_pca <- plot_pca(hs_t4h_inf_norm, plot_title="H. sapiens, L. major, t4h")
hs_t4h_pca$plot

keepers <- list("infection" = c("yes", "no"))
hs_t4h_de <- all_pairwise(hs_t4h_inf, model_batch="svaseq", filter=TRUE, force=TRUE)
hs_t4h_table <- combine_de_tables(hs_t4h_de, keepers=keepers)
```

# Which genes are DE in mouse macrophages at 4 hours upon infection with L. major?

# Gather annotation data

I want to perform a series of comparisons among the host cells: human and mouse.
Thus I need to collect annotation data for both species and get the set of
orthologs between them.

## Start with the human annotation data

```{r mouse_annotations}
mm_annot <- load_biomart_annotations(species="mmusculus")$annotation
rownames(mm_annot) <- make.names(
  paste0(mm_annot[["ensembl_transcript_id"]], ".",
         mm_annot[["transcript_version"]]),
  unique=TRUE)
mm_tx_gene <- mm_annot[, c("ensembl_gene_id", "ensembl_transcript_id")]
mm_tx_gene[["id"]] <- rownames(mm_tx_gene)
mm_tx_gene <- mm_tx_gene[, c("id", "ensembl_gene_id")]
new_mm_annot <- mm_annot
rownames(new_mm_annot) <- make.names(mm_annot[["ensembl_gene_id"]], unique=TRUE)
```

## Generate expressionsets

The question is reasonably self-contained.  I want to compare the uninfected
human samples against any samples which were infected for 4 hours.
So let us first pull those samples and then poke at them a bit.

```{r mouse_expts}
mm_expt <- create_expt(sample_sheet,
                       file_column="mmusculusfile",
                       gene_info=new_mm_annot,
                       tx_gene_map=mm_tx_gene)
mm_t4h_expt <- subset_expt(mm_expt, subset="expttime=='t4h'")
mm_t4h_expt <- set_expt_conditions(mm_t4h_expt, fact="infectstate")
table(mm_t4h_expt$conditions)
table(mm_t4h_expt$batches)
```

## Examine t4h vs uninfected

```{r examine_t4h, fig.show='hide'}
mm_t4h_plots <- sm(graph_metrics(mm_t4h_expt))
mm_t4h_norm <- normalize_expt(mm_t4h_expt, norm="quant", convert="cpm",
                              transform="log2", filter=TRUE, batch="svaseq")
mm_t4h_norm_plots <- sm(graph_metrics(mm_t4h_norm))
```

### Print some of the plots

```{r mm_examine_plots}
mm_t4h_plots$legend
mm_t4h_plots$libsize
mm_t4h_plots$boxplot

mm_t4h_norm_plots$pc_plot
```

```{r mm_t4h_de}
mm_t4h_inf_norm <- normalize_expt(mm_t4h_expt, transform="log2", convert="cpm",
                                  filter=TRUE, batch="svaseq")

mm_t4h_pca <- plot_pca(mm_t4h_inf_norm, plot_title="M. musculus, L. major, t4h")
mm_t4h_pca$plot

mm_t4h_de <- all_pairwise(mm_t4h_expt, model_batch="svaseq", filter=TRUE, force=TRUE)
mm_t4h_table <- combine_de_tables(mm_t4h_de, keepers=keepers)
```

# Compare this to the previous result.

Let us see if our human differential expression result is similar to that
obtained in Table S2.

# Compare the previous human and these human results.

```{r compare_previous_hs}
previous_hs <- readxl::read_excel("excel/inline-supplementary-material-5.xls", sheet=2)
previous_hs_lfc <- previous_hs_lfc[, c("ID", "Fold change")]
neg_idx <- previous_hs_lfc[[2]] < 0
previous_hs_lfc[neg_idx, 2] <- -1 * (1 / previous_hs_lfc[neg_idx, 2])
previous_hs_lfc[[2]] <- log2(previous_hs_lfc[[2]])

merged <- merge(previous_hs_lfc, hs_t4h_table$data[[1]], by.x="ID", by.y="row.names")
cor.test(merged[["limma_logfc"]], merged[["Fold change"]])
```

# Compare the previous mouse and these mouse results.

```{r compare_previous_mm}
previous_mm <- readxl::read_excel("excel/12864_2015_2237_MOESM3_ESM.xls", sheet=2, skip=1)
previous_mm_lfc <- previous_mm[, c("ID", "Fold change")]
neg_idx <- previous_mm_lfc[[2]] < 0
previous_mm_lfc[neg_idx, 2] <- -1 * (1 / previous_mm_lfc[neg_idx, 2])
previous_mm_lfc[[2]] <- log2(previous_mm_lfc[[2]])

merged <- merge(previous_mm_lfc, mm_t4h_table$data[[1]], by.x="ID", by.y="row.names")
cor.test(merged[["limma_logfc"]], merged[["Fold change"]])
```

First get the set of orthologs.

Side note: a different way of addressing this question resides in 20190220_host_comparisons.Rmd.

```{r mm_hs_orthologs}
## The defaults of this function are suitable for mouse/human queries.
mm_hs_ortho <- load_biomart_orthologs()$all_linked_genes

mm_table <- mm_t4h_table$data[[1]]
hs_table <- hs_t4h_table$data[[1]]

mm_table <- merge(mm_hs_ortho, mm_table, by.x="mmusculus", by.y="row.names", all.y=TRUE)
hs_table <- merge(mm_hs_ortho, hs_table, by.x="hsapiens", by.y="row.names", all.y=TRUE)
both_table_hs <- merge(hs_table, mm_table, by.x="hsapiens", by.y="hsapiens")
both_table_mm <- merge(hs_table, mm_table, by.x="mmusculus", by.y="mmusculus")

cor.test(both_table_hs[["limma_logfc.x"]], both_table_hs[["limma_logfc.y"]])
cor.test(both_table_mm[["limma_logfc.x"]], both_table_mm[["limma_logfc.y"]])
tt <- plot_scatter(both_table_hs[, c("limma_logfc.x", "limma_logfc.y")])
tt
```

# Compare the above with Table S7 from the Laura and Cecilia paper.

Table S7 has a set of genes from human which are also up-regulated in mouse upon
infection.

```{r open_figs7}
fig_s7_up <- readxl::read_excel("excel/inline-supplementary-material-7.xls", sheet=3)
fig_s7_hs_up <- unique(fig_s7_up[[5]])

fig_s7_down <- readxl::read_excel("excel/inline-supplementary-material-7.xls", sheet=4)
fig_s7_hs_down <- unique(fig_s7_down[[5]])

both_up_idx <- both_table_hs[["limma_logfc.x"]] >= 0.8 &
  both_table_hs[["limma_logfc.y"]] >= 0.8 &
  both_table_hs[["limma_adjp.x"]] <= 0.1 &
  both_table_hs[["limma_adjp.y"]] <= 0.1
both_up_ids <- both_table_hs[both_up_idx, "hsapiens"]
up_venn <- Vennerable::Venn(Sets=list("figs7" = fig_s7_hs_up, "tables" = both_up_ids))
Vennerable::plot(up_venn)

both_down_idx <- both_table_hs[["limma_logfc.x"]] <= -0.8 &
  both_table_hs[["limma_logfc.y"]] <= -0.8 &
  both_table_hs[["limma_adjp.x"]] <= 0.1 &
  both_table_hs[["limma_adjp.y"]] <= 0.1
both_down_ids <- both_table_hs[both_down_idx, "hsapiens"]
down_venn <- Vennerable::Venn(Sets=list("figs7" = fig_s7_hs_down, "tables" = both_down_ids))
Vennerable::plot(down_venn)
```

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