---
title: "Running fedup with multiple test sets"
author: Catherine Ross
output: rmarkdown::html_vignette
vignette: >
    %\VignetteIndexEntry{Running fedup with multiple test sets}
    %\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 multiple sets 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 (`geneMulti`) and pathways annotations (`pathwaysGMT`):

```{r}
data(geneMulti)
data(pathwaysGMT)
```

Take a look at the data structure:

```{r}
str(geneMulti)
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 `geneMulti` list object contains thirteen vector elements:
`background`, `FASN_negative`, `FASN_positive`, `ACACA_negative`,
`ACACA_positive`, `LDLR_negative`, `LDLR_positive`, `SREBF1_negative`,
`SREBF1_positive`, `SREBF2_negative`, `SREBF2_positive`, `C12orf49_negative`,
and `C12orf49_positive`. The `background` consists of all genes that the test
sets (in this case all sets besides `background`) will be compared against.
`FASN_negative` consists of genes that form **negative genetic interactions**
with the FASN gene after CRISPR-Cas9 knockout. `FASN_positive` consists of genes
that form **positve genetic interactions** with *FASN*. The remaining elements
contain genes forming genetic interactions with their respective genes
(*ACACA*, *LDLR*, *SREBF1*, *SREBF2*, *C12orf49*). 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/).

As an example, [*FASN*](https://www.genecards.org/cgi-bin/carddisp.pl?gene=FASN)
is a fatty acid synthase, so 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(geneMulti, pathwaysGMT)
```

The `fedupRes` output is a list of length `length(which(names(geneMulti) !=
"background"))`, corresponding to the number of test sets in `geneMulti`
(i.e., 12).

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!

Let's also view `fedup` results for `FASN_positive`, sorted by pvalue:

```{r}
set <- "FASN_positive"
print(head(fedupRes[[set]][which(fedupRes[[set]]$status == "enriched"),]))
print(head(fedupRes[[set]][which(fedupRes[[set]]$status == "depleted"),]))
```

Results for any test set can be indexed by its name:

```{r}
names(fedupRes)
```

## 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)) %>%
    as.data.frame()
```

Since we're dealing with multiple test sets here, it's important we create the
`set` and `sign` columns in `fedupPlot` to distinguish between them. 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 `set` and `sign` columns were created.

Plot significant results (qvalue < 0.05) in the form of a dot plot via
`plotDotPlot`. Facet the points by the `set` and `sign` columns and colour by
`sign`:

```{r, fedupDotPlot, fig.width = 18, fig.height = 15.5}
p <- plotDotPlot(
        df = fedupPlot,
        xVar = "log10qvalue",
        yVar = "pathway",
        xLab = "-log10(qvalue)",
        fillVar = "sign",
        fillLab = "Genetic interaction",
        fillCol = c("#6D90CA", "#F6EB13"),
        sizeVar = "fold_enrichment",
        sizeLab = "Fold enrichment") +
    facet_grid("sign ~ set", scales = "free_y", 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 244 fairly redundant pathways in a way that doesn't
hurt our tired brains even more? Oh I know, let's first try another
ggplot-based plot.

We can instead plot the degree of fold enrichment for a subset of pathways
across our test sets. First, select the top 20 results to plot from `fedupRes`:

```{r}
topPath <- fedupRes %>%
    bind_rows(.id = "set") %>%
    arrange(desc(fold_enrichment)) %>%
    slice(1:20) %>%
    select(pathway) %>%
    unlist() %>%
    as.character()
```

View the selected pathways:

```{r}
print(topPath)
```

Now subset `fedupRes` across all test sets for the pathways stored in `topPath`:

```{r}
fedupPlot <- fedupRes %>%
    bind_rows(.id = "set") %>%
    separate(col = "set", into = c("set", "sign"), sep = "_") %>%
    subset(pathway %in% topPath) %>%
    mutate(pathway = gsub("\\%.*", "", pathway)) %>%
    mutate(sign = ifelse(status == "depleted", "none", sign)) %>%
    mutate(sign = factor(sign, levels = c("negative", "positive", "none"))) %>%
    group_by(set, pathway) %>%
    top_n(1, wt = fold_enrichment) %>%
    as.data.frame()
```

Plot via `plotDotPlot`, this time using `set` as our x-axis variable:

```{r, fedupDotPlot_sum, fig.width = 9.5, fig.height = 5}
p <- plotDotPlot(
        df = fedupPlot,
        xVar = "set",
        yVar = "pathway",
        xLab = NULL,
        fillVar = "sign",
        fillLab = "Genetic interaction",
        fillCol = c("#6D90CA", "#F6EB13", "grey80"),
        sizeVar = "fold_enrichment",
        sizeLab = "Fold enrichment") +
    theme(
        panel.grid.major.y = element_blank(),
        axis.text.x = element_text(face = "italic", angle = 90,
        vjust = 0.5, hjust = 1))
print(p)
```

Ok cool, that's easier to look at than the plot before. Now let's summarize
these pathways even more efficiently using EnrichmentMap!

## 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_geneMulti, eval = FALSE}
netFile <- tempfile("fedupEM_geneMulti", fileext = ".png")
plotFemap(
    gmtFile = gmtFile,
    resultsFolder = resultsFolder,
    qvalue = 0.05,
    chartData = "DATA_SET",
    hideNodeLabels = TRUE,
    netName = "fedupEM_geneMulti",
    netFile = netFile
)
```

![](figures/fedupEM_geneMulti.png)

To note here, the EM nodes were coloured manually (by a similar palette of
colours passed to `plotDotPlot`) in Cytoscape via the *Change Colors* option in
the EM panel. A feature for automated dataset colouring is set to be released in
[version 3.3.2](https://github.com/BaderLab/EnrichmentMapApp/issues/455)
of EnrichmentMap.

This has effectively summarized the 244 pathways from our dot plot into 27
unique biological themes (including 7 unclustered pathways). We can now see
clear themes in the data pertaining to negative and positive genetic
interactions related to our genes of interest.

# Session information

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