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.
## Error in checkForRemoteErrors(val): one node produced an error: c("Error in DESeqDataSet(se, design = design, ignoreRank) : \n  some values in assay are not integers\n", "deseq")

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 yes 
##  35   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 7 surrogate variable(s).
## Attempting svaseq estimation with 7 surrogates.
## There are 648 (0.153%) elements which are < 0 after batch correction.
---
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
    number_sections: true
    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

hs_t4h_de <- all_pairwise(hs_t4h_inf, model_batch="svaseq", filter=TRUE)
hs_t4h_table <- combine_de_tables
```

# 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 examine_plots}
mm_t4h_plots$legend
mm_t4h_plots$libsize
mm_t4h_plots$boxplot

mm_t4h_norm_plots$pc_plot
```




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