27-oncogenic_pathway_recount2_model.Rmd

---
title: "How well does MultiPLIER capture oncogenic pathways?"
output:   
  html_notebook: 
    toc: true
    toc_float: true
---

**J. Taroni 2018**

We've considered that a large sample size and a diverse set of biological 
contexts and conditions might allow us to discover _novel_ biology--latent
variables or patterns that are not associated with a pathway that was supplied
to the model but _do_ participate in a coherent biological process.

`PLIER` includes a prior information matrix for the [oncogenic pathways from
MSigDB.](http://software.broadinstitute.org/gsea/msigdb/collections.jsp#C6)

We did not include this in the prior information we used as input during 
training.
Thus, we can essentially treat this as a **holdout set** of pathways and ask
if there are any latent variables significantly associated with the 
oncogenic pathways learned by the model.

We've adapted [`PLIER:::crossVal`](https://github.com/wgmao/PLIER/blob/a2d4a2aa343f9ed4b9b945c04326bebd31533d4d/R/Allfuncs.R#L175) 
to do just that.
See the `CalculateHoldoutAUC` function in `util/plier_util.R`.

## Functions and directory set up

```{r}
# we need the PLIER library loaded so we can get the oncogenicPathways dataset
library(PLIER)
# magrittr pipe
`%>%` <- dplyr::`%>%`
```

```{r}
# plot and result directory setup for this notebook
plot.dir <- file.path("plots", "27")
dir.create(plot.dir, recursive = TRUE, showWarnings = FALSE)
results.dir <- file.path("results", "27")
dir.create(results.dir, recursive = TRUE, showWarnings = FALSE)
```

### Custom functions

We're specifically going to use the `CalculateHoldoutAUC` function.

```{r}
source(file.path("util", "plier_util.R"))
```

## Read in data and model

```{r}
# prior information matrix for the oncogenic pathways included with PLIER
data("oncogenicPathways")

# PLIER model being evaluated -- recount2/MultiPLIER
plier.result <- readRDS(file.path("data", "recount2_PLIER_data", 
                                  "recount_PLIER_model.RDS"))

```

## Analysis

First, we need to calculate the AUC for each heldout pathway-latent variable
pair.

```{r}
auc.df <- CalculateHoldoutAUC(plier.result = plier.result,
                              holdout.mat = oncogenicPathways)
```

### Cursory look at results

Let's take a look at the results!

```{r}
head(auc.df)
```

Significant (FDR < 0.05) results only, sorted by LV and then by AUC

```{r}
sig.auc.df <- auc.df %>%
  dplyr::filter(FDR < 0.05) %>%
  dplyr::arrange(`LV index`, dplyr::desc(AUC))
sig.auc.df
```

What proportion of the pathways are associated with a latent variable? 
Using FDR < 0.05 as a cutoff, here.

```{r}
length(unique(sig.auc.df$pathway)) / ncol(oncogenicPathways)
```

Write the results to file

```{r}
readr::write_tsv(auc.df, 
                 path = file.path(results.dir, 
                                  "recount2_oncogenic_pathway_AUC.tsv"))
```


**Most oncogenic pathways are captured in the MultiPLIER model (FDR < 0.05)**