---
title: "Running fedup with a single test set"
author: Catherine Ross
output: rmarkdown::html_vignette
vignette: >
%\VignetteIndexEntry{Running fedup with a single test set}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "vignettes/figures/",
out.width = "100%"
)
```
This is an R package that tests for enrichment and depletion of user-defined
pathways using a Fisher's exact test. The method is designed for versatile
pathway annotation formats (eg. gmt, txt, xlsx) to allow the user to run
pathway analysis on custom annotations. This package is also
integrated with Cytoscape to provide network-based pathway visualization
that enhances the interpretability of the results.
This vignette will explain how to use `fedup` when testing a single set of genes
for pathway enrichment and depletion.
# System prerequisites
**R version** ≥ 4.1
**R packages**:
* **CRAN**: openxlsx, tibble, dplyr, data.table, ggplot2, ggthemes, forcats,
RColorBrewer
* **Bioconductor**: RCy3
# Installation
Install `fedup` from Bioconductor:
```{r, eval = FALSE, message = FALSE}
if(!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("fedup")
```
Or install the development version from Github:
```{r, message = FALSE}
devtools::install_github("rosscm/fedup", quiet = TRUE)
```
Load necessary packages:
```{r, message = FALSE}
library(fedup)
library(dplyr)
library(tidyr)
library(ggplot2)
```
# Running the package
## Input data
Load test genes (`geneSingle`) and pathways annotations (`pathwaysGMT`):
```{r}
data(geneSingle)
data(pathwaysGMT)
```
Take a look at the data structure:
```{r}
str(geneSingle)
str(head(pathwaysGMT))
```
To see more info on this data, run `?geneDouble` or `?pathwaysGMT`. You
could also run `example("prepInput", package = "fedup")` or
`example("readPathways", package = "fedup")` to see exactly how the data
was generated using the `prepInput()` and `readPathways()` functions.
`?` and `example()` can be used on any other functions mentioned here to
see their documentation and run examples.
The sample `geneSingle` list object contains two vector elements: `background`
and `FASN_negative`. The `background` consists of all genes that the test
sets (in this case `FASN_negative`) will be compared against. `FASN_negative`
consists of genes that form **negative genetic interactions** with the
*FASN* gene after CRISPR-Cas9 knockout. If you're interested in seeing how this
data set was constructed, check out the
[code](https://github.com/rosscm/fedup/blob/main/inst/script/genes.R).
Also, the paper the data was taken from is found
[here](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7566881/).
Given that [*FASN*](https://www.genecards.org/cgi-bin/carddisp.pl?gene=FASN) is
a fatty acid synthase, we would expect to see **enrichment** of the negative
interactions for pathways associated with *sensitization* of fatty acid
synthesis, as well as **enrichment** of the positive interactions for pathways
associated with *suppression* of the function. Conversely, we expect to find
**depletion** for pathways not at all involved with *FASN* biology. Let's see!
## Pathway analysis
Now use `runFedup` on the sample data:
```{r}
fedupRes <- runFedup(geneSingle, pathwaysGMT)
```
The `fedupRes` output is a list of length `length(which(names(geneSingle) !=
"background"))`, corresponding to the number of test sets in `geneSingle`
(i.e., 1).
View `fedup` results for `FASN_negative` sorted by pvalue:
```{r}
set <- "FASN_negative"
print(head(fedupRes[[set]][which(fedupRes[[set]]$status == "enriched"),]))
print(head(fedupRes[[set]][which(fedupRes[[set]]$status == "depleted"),]))
```
Here we see the strongest enrichment for the `ASPARAGINE N-LINKED GLYCOSYLATION`
pathway. Given that *FASN* mutant cells show a strong dependence on lipid
uptake, this enrichment for negative interactions with genes involved in
glycosylation is expected. We also see significant enrichment for other related
pathways, including `DISEASES ASSOCIATED WITH N-GLYCOSYLATION OF PROTEINS` and
`DISEASES OF GLYCOSYLATION`. Conversely, we see significant depletion for
functions not associated with these processes, such as `OLFACTORY SIGNALING
PATHWAY`, `GPCR LIGAND BINDING` and `KERATINIZATION`. Nice!
## Dot plot
Prepare data for plotting via `dplyr` and `tidyr`:
```{r}
fedupPlot <- fedupRes %>%
bind_rows(.id = "set") %>%
separate(col = "set", into = c("set", "sign"), sep = "_") %>%
subset(qvalue < 0.05) %>%
mutate(log10qvalue = -log10(qvalue)) %>%
mutate(pathway = gsub("\\%.*", "", pathway)) %>%
mutate(status = factor(status, levels = c("enriched", "depleted"))) %>%
as.data.frame()
```
If you're interested, take a look at `?dplyr::bind_rows` for details on how the
output `fedup` results list (`fedupRes`) was bound into a single dataframe and
`?tidyr::separate` for how the `sign` column was created.
Plot significant results (qvalue < 0.05) in the form of a dot plot via
`plotDotPlot`. Facet points by the `status` column:
```{r, fedupDotPlot, fig.width = 11, fig.height = 7}
p <- plotDotPlot(
df = fedupPlot,
xVar = "log10qvalue",
yVar = "pathway",
xLab = "-log10(qvalue)",
fillVar = "status",
fillLab = "Enrichment\nstatus",
sizeVar = "fold_enrichment",
sizeLab = "Fold enrichment") +
facet_grid("status", scales = "free", space = "free") +
theme(strip.text.y = element_blank())
print(p)
```
We can also colour in the points via the `sign` column from `fedupPlot`, while
still faceting by `status`:
```{r, fedupDotplot_signCol, fig.width = 11, fig.height = 7}
p <- plotDotPlot(
df = fedupPlot,
xVar = "log10qvalue",
yVar = "pathway",
xLab = "-log10(qvalue)",
fillVar = "sign",
fillLab = "Genetic interaction",
fillCol = "#6D90CA",
sizeVar = "fold_enrichment",
sizeLab = "Fold enrichment") +
facet_grid("status", scales = "free", space = "free") +
theme(strip.text.y = element_blank())
print(p)
```
Look at all those chick... enrichments! This is a bit overwhelming, isn't it?
How do we interpret these 38 fairly redundant pathways in a way that doesn't
hurt our tired brains even more? Oh I know, let's use an enrichment map!
## Enrichment map
First, make sure to have [Cytoscape](https://cytoscape.org/download.html)
downloaded and and open on your computer. You'll also need to install the
[EnrichmentMap](http://apps.cytoscape.org/apps/enrichmentmap) (≥ v3.3.0)
and [AutoAnnotate](http://apps.cytoscape.org/apps/autoannotate) apps.
Then format results for compatibility with EnrichmentMap using `writeFemap`:
```{r}
resultsFolder <- tempdir()
writeFemap(fedupRes, resultsFolder)
```
Prepare a pathway annotation file (gmt format) from the pathway list you
passed to `runFedup` using the `writePathways` function (you don't need to run
this function if your pathway annotations are already in gmt format, but it
doesn't hurt to make sure):
```{r}
gmtFile <- tempfile("pathwaysGMT", fileext = ".gmt")
writePathways(pathwaysGMT, gmtFile)
```
Cytoscape is open right? If so, run these lines and let the `plotFemap`
magic happen:
```{r, fedupEM, eval = FALSE}
netFile <- tempfile("fedupEM_geneSingle", fileext = ".png")
plotFemap(
gmtFile = gmtFile,
resultsFolder = resultsFolder,
qvalue = 0.05,
chartData = "NES_VALUE",
hideNodeLabels = TRUE,
netName = "fedupEM_geneSingle",
netFile = netFile
)
```
After some manual rearrangement of the annotated pathway clusters, this is the
resulting enrichment map we get from our `fedup` results. Much better!
This has effectively summarized the 36 pathways from our dot plot into 9 unique
biological themes (including 4 unclustered pathways). We can now see clear
themes in the data pertaining to negative *FASN* genetic interactions,
such as `glycan diseases, glycosylation`, `retrograde golgi transport`, and
`Rab regulation trafficking`.
# Session information
```{r}
sessionInfo()
```