02_sample_estimation.Rmd

---
title: "Sample estimation for TNSeq of Spyogenes (2017 including Andrew)."
author: "atb"
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}
## These are the options I tend to favor
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)
options(digits=4,
        stringsAsFactors=FALSE,
        knitr.duplicate.label="allow")
ggplot2::theme_set(ggplot2::theme_bw(base_size=10))
set.seed(1)
previous_file <- "01_annotation.Rmd"
ver <- "20180717"

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

rmd_file <- "02_sample_estimation.Rmd"
```

# Sample Estimation, Spyogenes: `r ver`

This document is concerned with analyzing TNSeq from S.pyogenes.

```{r initial_estimation, fig.show="hide"}
rpmi_expt <- subset_expt(expt=sp_expt, subset="experiment=='metal homeostasis'")
rpmi_metrics <- graph_metrics(expt=rpmi_expt)

rpmi_filt <- sm(normalize_expt(rpmi_expt, filter=TRUE))
rpmi_norm <- sm(normalize_expt(rpmi_expt, filter=TRUE, convert="cpm", norm="quant", transform="log2"))

rpmi_norm_metrics <- graph_metrics(expt=rpmi_norm)
```

## Show some graphs from before normalization.

```{r mouse_show_images_pre}
rpmi_metrics$legend
rpmi_metrics$libsize
## A few samples might be a problem: hpgl0898, hpgl0879; but I am guessing a factor
## of <4 between the highest and lowest samples should not be too big of a problem.
rpmi_metrics$density
## Nice consistent sample densities
rpmi_metrics$corheat
```

## Now some plots from after normalization

```{r mouse_show_images_post}
norm_pca <- plot_pca(rpmi_norm, cis=FALSE)
norm_pca$plot
## This clustering is kind of terrible.
```

## Try a couple of surrogate variables

Given the wretched clustering observed, I figure I should try a couple tools
from ruv/sva and see if they help.

```{r batch_testing}
rpmi_batch1 <- normalize_expt(rpmi_expt, transform="log2", convert="cpm",
                              filter=TRUE, batch="fsva")
plot_pca(rpmi_batch1)$plot
plot_corheat(rpmi_batch1)$plot
## This looks a bit more encouraging.

rpmi_batch2 <- normalize_expt(rpmi_expt, transform="log2", convert="cpm",
                              filter=TRUE, batch="svaseq")
plot_pca(rpmi_batch2)$plot
## as does this.

rpmi_batch_written <- write_expt(expt=rpmi_expt, transform="log2", convert="cpm",
                                 filter=TRUE, batch="fsva", violin=TRUE,
                                 excel=paste0("excel/rpmi_fsva-v", ver, ".xlsx"))
```

```{r surrogate_testing}
varpart_test <- varpart(expt=rpmi_filt, predictor=NULL,
                        factors=c("coverage", "replicate", "time", "cuzn", "medium"))
varpart_test$partition_plot
varpart_test$percent_plot

surrogate_test <- compare_surrogate_estimates(rpmi_filt)

surrogate_test$sva_unsupervised_adjust$svs_sample
rpmi_metrics$libsize
## Given the contribution of coverage in the variancePartition results above, one might
## assume that the library sizes will correspond to the surrogates detected by sva and friends.
## This appears to not be the case.
surrogate_test$plot
## it looks like the various surrogate estimators mostly agree on this data.
```

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