1 Differential Expression: 20180606

2 Filter the raw data

In sample_estimation, I created sc_filt which is precisely what I want.

3 Start with batch in the model

I am going to leave these running without silence as I want to make sure they are running without troubles.

3.1 Set up contrasts

I use the variable ‘keepers’ to define the numerators/denominators of interest and give their contrasts names which are appropriate. I do this because my all_pairwise() function performs all possible pairwise comparisons in the specific order of: a:b, a:c, a:d, … b:c, b:d, …, c:d, … which is not necessarily what is biologically interesting/intuitive. Thus when we specifically set the numerators/denominators here, it will make sure that the result of the contrast is in the chosen orientation.

keepers <- list(
  "D95A_vs_WT" = c("cbf5_D95A", "WT"),
  "upf1d_vs_WT" = c("upf1d", "WT"),
  "double_vs_D95A" = c("cbf5_D95Aupf1d", "cbf5_D95A"),
  "double_vs_upf1d" = c("cbf5_D95Aupf1d", "upf1d"),
  "double_vs_wt" = c("cbf5_D95Aupf1d", "WT"))

3.3 Repeat pairwise searches with experiment 1, experiment2 batch2

E1E2B2_filt <- sm(normalize_expt(E1E2B2_expt, filter=TRUE))
E1E2B2_fsva <- sm(all_pairwise(input=E1E2B2_filt, model_batch="fsva"))
E1E2B2_fsva_write <- sm(combine_de_tables(
  all_pairwise_result=E1E2B2_fsva, keepers=keepers,
  excel=paste0("excel/nor_sva_in_model_differential_merged-v", ver, ".xlsx"),
  abundant_excel=paste0("excel/nor_sva_in_model_abundance-v", ver, ".xlsx")))
E1E2B2_fsva_sig <- E1E2B2_fsva_write[["significant"]]
strict_E1E2B2_fsva_write <- sm(extract_significant_genes(
  E1E2B2_fsva_write, lfc=2,
  excel=paste0("excel/nor_sva_in_model_sig2lfc-v", ver, ".xlsx")))
new_colors <- c("#008000", "#4CA64C", "#7FBF7F", "#FF0000", "#FF4C4C", "#FF9999")
new_bars <- significant_barplots(E1E2B2_fsva_write, color_list=new_colors)
norcbf5_de_plots <- extract_de_plots(E1E2B2_fsva_write, type="deseq", table="cbf5_D95A_vs_WT")
pp(file="illustrator_input/24_norcbf5_deseq_ma.pdf", image=norcbf5_de_plots$ma$plot)
## Writing the image to: illustrator_input/24_norcbf5_deseq_ma.pdf and calling dev.off().

pp(file="illustrator_input/25_norcbf5_deseq_vol.pdf", image=norcbf5_de_plots$volcano$plot)
## Writing the image to: illustrator_input/25_norcbf5_deseq_vol.pdf and calling dev.off().

pp(file="illustrator_input/26_norredgreen_sigbars.pdf", image=new_bars$deseq)
## Writing the image to: illustrator_input/26_norredgreen_sigbars.pdf and calling dev.off().

pp(file="illustrator_input/27_nor_agreement.pdf", image=E1E2B2_fsva$comparison$heat)
## Writing the image to: illustrator_input/27_nor_agreement.pdf and calling dev.off().

4 20180606 Only old data

We want to use our ‘only_old’ experiment to look at some DE of only experiment 1.

E1_filt <- sm(normalize_expt(E1_expt, filter=TRUE))
keepers <- list("cbf5_vs_wt" = c("cbf5_D95A", "WT"))
E1_de <- sm(all_pairwise(input=E1_filt, model_batch=FALSE))

E1_write <- sm(combine_de_tables(
  all_pairwise_result=E1_de, keepers=keepers,
  excel=paste0("excel/E1_differential-v", ver, ".xlsx"),
  abundant_excel=paste0("excel/E1_abundance-v", ver, ".xlsx"),
  sig_excel=paste0("excel/E1_significant-v", ver, ".xlsx")))

E1_sig <- E1_write[["significant"]]
strict_E1_write <- sm(extract_significant_genes(
  E1_write, lfc=2,
  excel=paste0("excel/E1_sig2lfc-v", ver, ".xlsx")))
new_colors <- c("#008000", "#4CA64C", "#7FBF7F", "#FF0000", "#FF4C4C", "#FF9999")
new_bars <- significant_barplots(E1_write, color_list=new_colors)
E1_de_plots <- extract_de_plots(E1_write, type="deseq", table="cbf5_D95A_vs_WT")
pp(file="illustrator_input/28_E1_deseq_ma.pdf", image=E1_de_plots$ma$plot)
## Writing the image to: illustrator_input/28_E1_deseq_ma.pdf and calling dev.off().

pp(file="illustrator_input/29_E1_deseq_vol.pdf", image=E1_de_plots$volcano$plot)
## Writing the image to: illustrator_input/29_E1_deseq_vol.pdf and calling dev.off().

pp(file="illustrator_input/30_E1_sigbars.pdf", image=new_bars$deseq)
## Writing the image to: illustrator_input/30_E1_sigbars.pdf and calling dev.off().

---
title: "S. cerevisiae 2017: differential expression, merged sample edition (20180606)."
author: "atb abelew@gmail.com"
date: "`r Sys.Date()`"
output:
 html_document:
  code_download: true
  code_folding: show
  fig_caption: true
  fig_height: 7
  fig_width: 7
  highlight: default
  keep_md: false
  mode: selfcontained
  number_sections: true
  self_contained: true
  theme: readable
  toc: true
  toc_float:
    collapsed: false
    smooth_scroll: false
---

<style>
  body .main-container {
    max-width: 1600px;
  }
</style>

```{r options, include=FALSE}
if (!isTRUE(get0("skip_load"))) {
  library("hpgltools")
  tt <- devtools::load_all("~/hpgltools")
  knitr::opts_knit$set(progress=TRUE,
                       verbose=TRUE,
                       width=90,
                       echo=TRUE)
  knitr::opts_chunk$set(error=TRUE,
                        fig.width=8,
                        fig.height=8,
                        dpi=96)
  old_options <- options(digits=4,
                         stringsAsFactors=FALSE,
                         knitr.duplicate.label="allow")
  ggplot2::theme_set(ggplot2::theme_bw(base_size=10))
  set.seed(1)
  ver <- "20180606"
  previous_file <- paste0("02_sample_estimation_merged-v", ver, ".Rmd")

  tmp <- sm(loadme(filename=paste0(gsub(pattern="\\.Rmd", replace="", x=previous_file), "-v", ver, ".rda.xz")))
  rmd_file <- paste0("03_differential_expression_merged-v", ver, ".Rmd")
}
```

# Differential Expression: `r ver`

# Filter the raw data

In sample_estimation, I created sc_filt which is precisely what I want.

# Start with batch in the model

I am going to leave these running without silence as I want to make sure they
are running without troubles.

## Set up contrasts

I use the variable 'keepers' to define the numerators/denominators of interest
and give their contrasts names which are appropriate.  I do this because my
all_pairwise() function performs all possible pairwise comparisons in the
specific order of: a:b, a:c, a:d, ... b:c, b:d, ..., c:d, ...
which is not necessarily what is biologically interesting/intuitive.  Thus when
we specifically set the numerators/denominators here, it will make sure that the
result of the contrast is in the chosen orientation.

```{r batch_model}
keepers <- list(
  "D95A_vs_WT" = c("cbf5_D95A", "WT"),
  "upf1d_vs_WT" = c("upf1d", "WT"),
  "double_vs_D95A" = c("cbf5_D95Aupf1d", "cbf5_D95A"),
  "double_vs_upf1d" = c("cbf5_D95Aupf1d", "upf1d"),
  "double_vs_wt" = c("cbf5_D95Aupf1d", "WT"))
```

## Perform search

As I suspect you know, all_pairwise() is the work horse.  It runs
deseq/edger/limma/basic differential expression searches and attempts to use
similar/compatible statistical models for each search (except basic which is
intended as a diagnostic/negative control).

combine_de_tables() does most of the work.  It takes the data from
all_pairwise(), combines them, and writes them out.

I should add some more pictures here.

```{r perform_all_de, fig.show="hide"}
fsva <- sm(all_pairwise(input=all_filt, model_batch="fsva"))

fsva_write <- sm(combine_de_tables(
  all_pairwise_result=fsva, keepers=keepers,
  excel=paste0("excel/all_sva_in_model_differential-v", ver, ".xlsx"),
  abundant_excel=paste0("excel/all_sva_in_model_abundance-v", ver, ".xlsx")))
strict_sig_write <- sm(extract_significant_genes(
  fsva_write, according_to="deseq", lfc=2,
  excel=paste0("excel/all_sva_in_model_sig2lfc-v", ver, ".xlsx")))
new_colors <- c("#008000", "#4CA64C", "#7FBF7F", "#FF0000", "#FF4C4C", "#FF9999")
new_bars <- significant_barplots(fsva_write, color_list=new_colors)
cbf5_de_plots <- extract_de_plots(fsva_write, type="deseq", table="cbf5_D95A_vs_WT")
```

Show some plots!

```{r perform_all_de_plots}
pp(file="illustrator_input/21_cbf5_deseq_ma.pdf", image=cbf5_de_plots$ma$plot)
pp(file="illustrator_input/22_cbf5_deseq_vol.pdf", image=cbf5_de_plots$volcano$plot)
pp(file="illustrator_input/23_redgreen_sigbars.pdf", image=new_bars$deseq)
```

## Repeat pairwise searches with experiment 1, experiment2 batch2

```{r perform_E1E2B2_de, fig.show="hide"}
E1E2B2_filt <- sm(normalize_expt(E1E2B2_expt, filter=TRUE))
E1E2B2_fsva <- sm(all_pairwise(input=E1E2B2_filt, model_batch="fsva"))

E1E2B2_fsva_write <- sm(combine_de_tables(
  all_pairwise_result=E1E2B2_fsva, keepers=keepers,
  excel=paste0("excel/nor_sva_in_model_differential_merged-v", ver, ".xlsx"),
  abundant_excel=paste0("excel/nor_sva_in_model_abundance-v", ver, ".xlsx")))
E1E2B2_fsva_sig <- E1E2B2_fsva_write[["significant"]]
strict_E1E2B2_fsva_write <- sm(extract_significant_genes(
  E1E2B2_fsva_write, lfc=2,
  excel=paste0("excel/nor_sva_in_model_sig2lfc-v", ver, ".xlsx")))
new_colors <- c("#008000", "#4CA64C", "#7FBF7F", "#FF0000", "#FF4C4C", "#FF9999")
new_bars <- significant_barplots(E1E2B2_fsva_write, color_list=new_colors)
norcbf5_de_plots <- extract_de_plots(E1E2B2_fsva_write, type="deseq", table="cbf5_D95A_vs_WT")
```

```{r perform_E1E2B2_de_plots}
pp(file="illustrator_input/24_norcbf5_deseq_ma.pdf", image=norcbf5_de_plots$ma$plot)
pp(file="illustrator_input/25_norcbf5_deseq_vol.pdf", image=norcbf5_de_plots$volcano$plot)
pp(file="illustrator_input/26_norredgreen_sigbars.pdf", image=new_bars$deseq)

pp(file="illustrator_input/27_nor_agreement.pdf", image=E1E2B2_fsva$comparison$heat)
```

# 20180606 Only old data

We want to use our 'only_old' experiment to look at some DE of only experiment 1.

```{r de_E1}
E1_filt <- sm(normalize_expt(E1_expt, filter=TRUE))
keepers <- list("cbf5_vs_wt" = c("cbf5_D95A", "WT"))
E1_de <- sm(all_pairwise(input=E1_filt, model_batch=FALSE))

E1_write <- sm(combine_de_tables(
  all_pairwise_result=E1_de, keepers=keepers,
  excel=paste0("excel/E1_differential-v", ver, ".xlsx"),
  abundant_excel=paste0("excel/E1_abundance-v", ver, ".xlsx"),
  sig_excel=paste0("excel/E1_significant-v", ver, ".xlsx")))
E1_sig <- E1_write[["significant"]]
strict_E1_write <- sm(extract_significant_genes(
  E1_write, lfc=2,
  excel=paste0("excel/E1_sig2lfc-v", ver, ".xlsx")))
new_colors <- c("#008000", "#4CA64C", "#7FBF7F", "#FF0000", "#FF4C4C", "#FF9999")
new_bars <- significant_barplots(E1_write, color_list=new_colors)
E1_de_plots <- extract_de_plots(E1_write, type="deseq", table="cbf5_D95A_vs_WT")
```

```{r E1_plots}
pp(file="illustrator_input/28_E1_deseq_ma.pdf", image=E1_de_plots$ma$plot)
pp(file="illustrator_input/29_E1_deseq_vol.pdf", image=E1_de_plots$volcano$plot)
pp(file="illustrator_input/30_E1_sigbars.pdf", image=new_bars$deseq)
```

```{r saveme, include=FALSE}
if (!isTRUE(get0("skip_load"))) {
  pander::pander(sessionInfo())
  message(paste0("This is hpgltools commit: ", get_git_commit()))
  this_save <- paste0(gsub(pattern="\\.Rmd", replace="", x=rmd_file), "-v", ver, ".rda.xz")
  tmp <- sm(saveme(filename=this_save))
}
```
