---
title: "Using fgsea package"
author: "Alexey Sergushichev"
date: "2016-06-22"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Using fgsea package}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

`fgsea` is an R-package for fast preranked gene set enrichment analysis (GSEA). The performance is achieved by using an algorithm for cumulative
GSEA-statistic calculation. This allows to reuse samples between different gene set sizes. See the [preprint](http://biorxiv.org/content/early/2016/06/20/060012) for algorithmic details. 

## Loading necessary libraryries

```{r echo=F, message=F}
library(fgsea)
library(data.table)
library(ggplot2)
```

## Quick run

Loading example pathways and gene-level statistics:
```{r}
data(examplePathways)
data(exampleRanks)
```

Running fgsea:
```{r}
fgseaRes <- fgsea(pathways = examplePathways, 
                  stats = exampleRanks,
                  minSize=15,
                  maxSize=500,
                  nperm=10000)
```

The resulting table contains enrichment scores and p-values:
```{r}
head(fgseaRes[order(pval), ])
```

It takes about ten seconds to get results with significant hits after FDR correction:
```{r}
sum(fgseaRes[, padj < 0.01])
```

One can make an enrichment plot for a pathway:
```{r, fig.width=7, fig.height=4}
plotEnrichment(examplePathways[["5991130_Programmed_Cell_Death"]],
               exampleRanks) + labs(title="Programmed Cell Death")

```

Or make a table plot for a bunch of selected pathways:
```{r, fig.width=7, fig.height=8, fig.retina=2}
topPathwaysUp <- fgseaRes[ES > 0][head(order(pval), n=10), pathway]
topPathwaysDown <- fgseaRes[ES < 0][head(order(pval), n=10), pathway]
topPathways <- c(topPathwaysUp, rev(topPathwaysDown))
plotGseaTable(examplePathways[topPathways], exampleRanks, fgseaRes, 
              gseaParam = 0.5)
```

## Performance considerations

Please, be aware that `fgsea` function takes about *O(nk^{3/2})* time,
where *n* is number of permutations and *k* is a maximal size of the
pathways. That means that setting `maxSize` parameter with a value of ~500 
is strongly recommended.

Also, `fgsea` is parallelized using `BiocParallel` package. 
By default the first registered backend returned by `bpparam()` is 
used. To tweak the parallelization one can either specify `BPPARAM`
parameter used for `bclapply` of set `nproc` parameter, which is 
a shorthand for setting `BPPARAM=MulticoreParam(workers = nproc)`.

## Using Reactome pathways

For convenience there is `reactomePathways` function that obtains pathways
from Reactome for given set of genes. Package `reactome.db` is required
to be installed.

```{r message=F}
pathways <- reactomePathways(names(exampleRanks))
fgseaRes <- fgsea(pathways, exampleRanks, nperm=1000, maxSize=500)
head(fgseaRes)
```

## Starting from files

One can also start from `.rnk` and `.gmt` files as in original GSEA:

```{r}
rnk.file <- system.file("extdata", "naive.vs.th1.rnk", package="fgsea")
gmt.file <- system.file("extdata", "mouse.reactome.gmt", package="fgsea")
```

Loading ranks:

```{r}
ranks <- read.table(rnk.file,
                    header=TRUE, colClasses = c("character", "numeric"))
ranks <- setNames(ranks$t, ranks$ID)
str(ranks)
```

Loading pathways:

```{r}
pathways <- gmtPathways(gmt.file)
str(head(pathways))
```

And runnig fgsea:

```{r}
fgseaRes <- fgsea(pathways, ranks, minSize=15, maxSize=500, nperm=1000)
head(fgseaRes)
```
