---
title: "Signature Overrepresentation Analysis - Sigora"
author: "Witold Wolski"
date: "24/11/2021"
output: html_document
vignette: >
  %\VignetteIndexEntry{Signature Overrepresentation Analysis - Sigora} 
  %\VignetteEncoding{UTF-8}
  %\VignetteEngine{knitr::rmarkdown}

---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```

# Signature Overrepresentation Analysis

This package implements the pathway analysis method SIGORA. For an in depth
description of the method, please see our manuscript in PeerJ.  In short: a
_GPS_ (gene pair signature) is a (weighted) pair of genes that _as
a combination_ occurs only in a single pathway within a pathway repository.
A query list is a vector containing a gene list of interest (e.g. genes that
are differentially expressed in a particular condition).  A _present_
GPS is a GPS for which both components are in the query list. SIGORA
identifies relevant pathways based on the over-representation analysis of
their (present) GPS.


@section Getting started:


To load the library:

```{r}
library("sigora")
```

# Motivation:

A thought experiment Imagine you randomly selected 3 KEGG pathways, and then randomly selected a total of 50 genes from all genes that that are associated with any of these pathways. Using traditional methods (hypergeometric test using individual genes), how many pathways would you estimate to show up as statistically overrepresented in this
"query list" of 50 genes?  

Let us do this experiment!  Everything related to
human KEGG Pathways can be found in `kegH`.  A function to randomly
select $n$ genes from m randomly selected pathways is
`genesFromRandomPathways`.  The traditional Overrepresentation Analysis
(which is the basis for many popular tools) is available through `ora`.
Putting these together:

```{r}
data(kegH)
set.seed(seed = 12345)
a1 <- genesFromRandomPathways( kegH, 3, 50)
## originally selected pathways:
a1[["selectedPathways"]] ## what are the genes
a1[["genes"]]
## Traditional ora identifies dozens of statistically significant pathways!

head(ora(a1[["genes"]], kegH))
## Now let us try sigora with the same input:

sigoraRes <-
  sigora(GPSrepo = kegH,
         queryList = a1[["genes"]],
         level = 4)
## Again, the three originally selected pathways were:
a1[["selectedPathways"]]

```

You might want to rerun the above few lines of code with different values
for `seed` and convince yourself that there indeed is a need for a new
way of pathway analysis.

# Available Pathway-GPS repositories in SIGORA:

The current version of the package comes with precomputed GPS-repositories
for __KEGG__ human and mouse (`kegH` and `kegM` respectively), as well as for
__Reactome__ human and mouse (`reaH` and `reaM` respectively).  The package
provides a function for creating GPS-repositories from user's own
gene-function repository of choice (example __Gene Ontology Biological
Processes__).  The following section describes this process of creating one's
own GPS-repositories using the __PCI-NCI__ pathways from National Cancer
Institute as an example.

# Creating a GPS repository:

You can create your own GPS repositories using the `makeGPS()` function.
There are no particular
requirements on the format of your source repository, except: it should be
provided either a tab delimited file or a dataframe with \bold{three columns
in the following order:
- PathwayID, PathwayName, Gene.

```{r}
data(nciTable) ## what does the input look like?
head(nciTable) ## create a SigObject. use the saveFile parameter for future reuse.
data(idmap)
nciH <- makeGPS(pathwayTable = nciTable, saveFile = NULL)
ils <- grep("^IL", idmap[, "Symbol"], value = TRUE)
ilnci <- sigora(queryList = ils,
                GPSrepo = nciH,
                level = 3)

```

# Analysing your data To preform Signature Overrepresentation:

Analysis, use the function `sigora`.  For traditional
Overrepresentation Analysis, use the function `ora`.

# Exporting:

Exporting the results Simply provide a file name to the
`saveFile` parameter of `sigora`, i.e. (for the above
experiment):

```{r}
sigRes <-
  sigora(kegH,
         queryList = a1$genes,
         level = 2,
         saveFile = NULL)

```

You will notice that the file also
contains the list of the relevant genes from the query list in each pathway.
The genes are listed as human readable gene symbols and sorted by their
contribution to the statistical significance of the pathway.

# Gene identifier mapping Mappings:

between __ENSEMBL__-IDS, __ENTREZ__-IDS and Gene-Symbols are performed
automatically.

You can, for instance, create a __GPS__-repository using
__ENSEMBL__-IDs and perform __Signature Overrepresentation Analysis__
using this repository on a list of __ENTREZ__-IDs.

```{r}
data('kegH')
data('idmap')
barplot(
  table(kegH$L1$degs),
  col = "red",
  main = "distribution of number of functions per gene in KEGG human pathways.",
  ylab = "frequency",
  xlab = "number of functions per gene"
)
## creating your own GPS repository
nciH <- makeGPS(pathwayTable = load_data('nciTable'))
ils <- grep("^IL", idmap[, "Symbol"], value = TRUE)
## signature overrepresentation analysis:
sigRes.ilnci <- sigora(queryList = ils,
                       GPSrepo = nciH,
                       level = 3)

```


# Session

```{r}
sessionInfo()
```
