---
title: 'cTRAP: identifying candidate causal perturbations from differential gene expression data'
author: Bernardo P. de Almeida & Nuno Saraiva-Agostinho
date: "`r Sys.Date()`"
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# Introduction

`cTRAP` is an R package designed to compare differential gene expression results
with those from known cellular perturbations (such as gene knockdown, 
overexpression or small molecules) derived from the [Connectivity Map][clue.io]
(CMap; [Subramanian et al., Cell 2017][subramanian2017]). Such analyses allow 
not only to infer the molecular causes of the observed difference in gene 
expression but also to identify small molecules that could drive or revert 
specific transcriptomic alterations.

To illustrate the package functionalities, we will use an example based on a
gene knockdown dataset from the [ENCODE project][ENCODE] for which there is
available RNA-seq data. After performing differential expression analyses to the
matched-control sample, we will compare the respective transcriptomic changes
with the ones caused by all CMap's gene knockdown perturbations to identify 
which ones have similar or inverse transcriptomic changes to the observed ones. 
As a positive control, we expect to find the knock-down of the gene depleted in
the ENCODE experiment as one of the most similar transcriptomic perturbations.

# Getting started

To load the `cTRAP` package into your R environment type:

```{r load package}
library(cTRAP)
```

# Load ENCODE RNA-seq data and perform differential gene expression analysis

In this example, we will use the EIF4G1 shRNA knockdown followed by RNA-seq
experiment in HepG2 cell line from the ENCODE project as the dataset of
interest. The RNA-seq processed data (gene quantifications from RSEM method) for
the EIF4G1 knock-down and respective controls (two replicates each) can be
automatically downloaded and loaded by typing:

```{r load ENCODE samples, eval=FALSE}
gene <- "EIF4G1"
cellLine <- "HepG2"

ENCODEmetadata <- downloadENCODEknockdownMetadata(cellLine, gene)
table(ENCODEmetadata$`Experiment target`)
length(unique(ENCODEmetadata$`Experiment target`))

ENCODEsamples <- loadENCODEsamples(ENCODEmetadata)[[1]]
counts <- prepareENCODEgeneExpression(ENCODEsamples)
```

```{r load data, include=FALSE}
data("ENCODEmetadata")
data("counts")
```

Gene expression data (read counts) were quantile-normalized using [`voom`][voom]
and differential expression analysis was performed using the [`limma`][limma] R
package.

```{r differential gene expression, eval=FALSE}
# Remove low coverage (at least 10 counts shared across two samples)
minReads   <- 10
minSamples <- 2
filter <- rowSums(counts[ , -c(1, 2)] >= minReads) >= minSamples
counts <- counts[filter, ]

# Convert ENSEMBL identifier to gene symbol
counts$gene_id <- convertGeneIdentifiers(counts$gene_id)

# Perform differential gene expression analysis
diffExpr <- performDifferentialExpression(counts)
```

For our metric of differential expression after EIF4G1 shRNA knock-down, we will
use the respective t-statistic.

```{r t-statistics, eval=FALSE}
# Get t-statistics of differential expression with respective gene names 
# (expected input for downstream analyses)
diffExprStat <- diffExpr$t
names(diffExprStat) <- diffExpr$Gene_symbol
```

```{r, echo=FALSE}
data("diffExprStat")
```

# Load CMap perturbation data

We will use our differential gene expression metric to compare with CMap's gene 
knock-down perturbations in the same cell line (HepG2). Note that this
comparison can also be done to perturbations in a different cell line (or in all
cell lines using the average result across cell lines).

To summarise conditions and check available data in CMap, we can use the 
following commands to download CMap metadata:

```{r CMap metadata conditions}
# Load CMap metadata (automatically downloaded if not found)
cmapMetadata <- loadCMapData("cmapMetadata.txt", type="metadata")

# Summarise conditions for all CMap perturbations
getCMapConditions(cmapMetadata)

# Summarise conditions for CMap perturbations in HepG2 cell line
getCMapConditions(cmapMetadata, cellLine="HepG2")

# Summarise conditions for a specific CMap perturbation in HepG2 cell line
getCMapConditions(
    cmapMetadata, cellLine="HepG2",
    perturbationType="Consensus signature from shRNAs targeting the same gene")
```

Now, we will filter the metadata to CMap gene knockdown perturbations in HepG2
cell line and load associated gene information and differential gene expression
data. Data is loaded based on the given filename (if no file with the given 
filename is available, data will be first downloaded).

> Differential gene expression data for each CMap perturbation are available in 
normalised z-score values (read [Subramanian et al., Cell 2017][subramanian2017]
for more details). As this file's size is big (around 20GB), a prompt will ask
you to confirm whether to start downloading the file.
>
> Alternatively, you can download the file from the following link (do not
forget to use the same filename as the argument of
`prepareCMapPerturbations()`):
ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE92nnn/GSE92742/suppl/GSE92742_Broad_LINCS_Level5_COMPZ.MODZ_n473647x12328.gctx.gz

```{r CMap knockdown perturbations, eval=FALSE}
# Filter CMap gene knockdown HepG2 data to be loaded
cmapMetadataKD <- filterCMapMetadata(
    cmapMetadata, cellLine="HepG2",
    perturbationType="Consensus signature from shRNAs targeting the same gene")

# Load filtered data (data will be downloaded if not found)
cmapPerturbationsKD <- prepareCMapPerturbations(
    metadata=cmapMetadataKD, zscores="cmapZscores.gctx",
    geneInfo="cmapGeneInfo.txt")
```

If interested in small molecules, the differential gene expression z-scores from
CMap can be downloaded for each small molecule perturbation:

```{r CMap small molecule perturbations, eval=FALSE}
# Filter CMap gene small molecule HepG2 data to be loaded
cmapMetadataCompounds <- filterCMapMetadata(
    cmapMetadata, cellLine="HepG2", perturbationType="Compound")

# Load filtered data (data will be downloaded if not found)
cmapPerturbationsCompounds <- prepareCMapPerturbations(
    metadata=cmapMetadataCompounds, zscores="cmapZscores.gctx",
    geneInfo="cmapGeneInfo.txt", compoundInfo="cmapCompoundInfo.txt")
```

```{r load CMap perturbations, include=FALSE}
data("cmapPerturbationsKD")
data("cmapPerturbationsCompounds")
cmapPerturbationsCompounds <- cmapPerturbationsCompounds[
    , grep("HEPG2", colnames(cmapPerturbationsCompounds))]
```

# Comparison with CMap perturbations

The `rankSimilarPerturbations` function compares the differential expression 
metric (the t-statistic, in this case) against the CMap perturbations' z-scores
using the available methods:

* Spearman's correlation
* Pearson's correlation
* Gene Set Enrichment Analysis (GSEA), where the most up- and down-regulated *n*
genes from the user's differential expression profile are used as gene sets (by 
default, *n* = 150 genes)

To compare against CMap knockdown perturbations using all the previous methods:

```{r compare knockdown, message=FALSE}
compareKD <- rankSimilarPerturbations(diffExprStat, cmapPerturbationsKD)
```

To compare against selected CMap small molecule perturbations:

```{r compare small molecules, message=FALSE}
compareCompounds <- rankSimilarPerturbations(diffExprStat, 
                                             cmapPerturbationsCompounds)
```

The output table contains the results of the comparison with each perturbation 
tested, including the test statistics (Spearman's correlation coefficient, 
Pearson's correlation coefficient and/or GSEA score), the respective p-value and
the Benjamini-Hochberg-adjusted p-value (for correlation statistics only). When
performing multiple methods, the [rank product][]'s rank will be included to
summarise other method's rankings.

```{r order knockdown perturbation comparison, fig.width=6, fig.asp=1}
# Most positively associated perturbations (note that EIF4G1 knockdown is the
# 7th, 1st and 2nd most positively associated perturbation based on Spearman's
# correlation, Pearson's correlation and GSEA, respectively)
head(compareKD[order(spearman_rank)], n=10)
head(compareKD[order(pearson_rank)])
head(compareKD[order(GSEA_rank)])
head(compareKD[order(rankProduct_rank)])

# Most negatively associated perturbations
head(compareKD[order(-spearman_rank)])
head(compareKD[order(-pearson_rank)])
head(compareKD[order(-GSEA_rank)])
head(compareKD[order(-rankProduct_rank)])

# Plot list of compared pertubations
plot(compareKD, "spearman", n=c(10, 3))
plot(compareKD, "pearson")
plot(compareKD, "gsea")
plot(compareKD, "rankProduct")
```

For small molecules:

```{r order small molecule perturbation comparison, fig.width=6, fig.asp=1}
# Most positively and negatively associated perturbations
compareCompounds[order(rankProduct_rank)]
plot(compareCompounds, "rankProduct")
```

> The Gene Set Enrichment Analysis (GSEA) score is based on the Weighted
Connectivity Score (WTCS), a composite and bi-directional version of the 
weighted Kolmogorov-Smirnov enrichment statistic (ES)
([Subramanian et al., Cell 2017][subramanian2017]).

> To calculate the GSEA score, GSEA is run for the most up- and down-regulated
genes from the user's differential expression profile. The GSEA score is the
mean between ES~top~ and ES~bottom~ (however, if ES~top~ and ES~bottom~ have the
same sign, the GSEA score is 0).

> If a perturbation has a similar differential expression profile to our data 
(higher GSEA score), we expect to see the most up-regulated (down-regulated) 
genes in the perturbation enriched in the top (bottom) *n* differentially
expressed genes from our data.

# Information on perturbations

To get associated information as made available from CMap:

```{r perturbation info, fig.width=5, fig.asp=1}
# Information on the EIF4G1 knockdown perturbation
EIF4G1knockdown <- grep("EIF4G1", compareKD[[1]], value=TRUE)
print(compareKD, EIF4G1knockdown)

# Information on the top 10 most similar compound perturbations (based on
# Spearman's correlation)
print(compareKD[order(rankProduct_rank)], 1:10)

# Get table with all information available (including ranks, metadata, compound
# information, etc.)
table <- as.table(compareKD)
head(table)

# Obtain available raw information from compared perturbations
names(attributes(compareKD)) # Data available in compared perturbations
attr(compareKD, "metadata")  # Perturbation metadata
attr(compareKD, "geneInfo")  # Gene information
```

# Relationship plots

To analyse the relationship between the user-provided differential expression
profile with that associated with a specific perturbation, scatter plots (for
Spearman and Pearson analyses) and GSEA plots are available.

For instance, let's plot the relationship between EIF4G1 shRNA knockdown from
ENCODE with the CMap knockdown perturbations:

```{r, echo=FALSE}
attr(compareKD, "zscoresFilename") <- cmapPerturbationsKD
```

```{r plot knockdown comparison, message=FALSE, fig.width=5, fig.asp=1}
# Plot relationship with EIF4G1 knockdown from CMap
plot(compareKD, EIF4G1knockdown, "spearman")
plot(compareKD, EIF4G1knockdown, "pearson")
plot(compareKD, EIF4G1knockdown, "gsea")

# Plot relationship with most negatively associated perturbation
plot(compareKD, compareKD[order(-spearman_rank)][1, 1], "spearman")
plot(compareKD, compareKD[order(-pearson_rank)][1, 1], "pearson")
plot(compareKD, compareKD[order(-GSEA_rank)][1, 1], "gsea")
```

For small molecules:

```{r, echo=FALSE}
attr(compareCompounds, "zscoresFilename") <- cmapPerturbationsCompounds
```

```{r plot small molecule comparison, message=FALSE, fig.width=5, fig.asp=1}
# Plot relationship with most positively associated perturbation
plot(compareCompounds, compareCompounds[order(spearman_rank)][1, 1], "spearman")
plot(compareCompounds, compareCompounds[order(pearson_rank)][1, 1], "pearson")
plot(compareCompounds, compareCompounds[order(GSEA_rank)][1, 1], "gsea")

# Plot relationship with most negatively associated perturbation
plot(compareCompounds, compareCompounds[order(-spearman_rank)][1,1], "spearman")
plot(compareCompounds, compareCompounds[order(-pearson_rank)][1, 1], "pearson")
plot(compareCompounds, compareCompounds[order(-GSEA_rank)][1, 1], "gsea")
```

# Predict targeting drugs

Compounds that target the phenotypes associated with the user-provided
differential expression profile can be inferred by comparing against gene 
expression and drug sensitivity associations. The gene expression and drug 
sensitivity datasets derived from the following sources were correlated using 
Spearman's correlation across the available cell lines.

| Source           | Screened compounds | Human cancer cell lines |
| ---------------- | ------------------:| -----------------------:|
| [NCI60][]        |          > 100 000 |                      60 |
| [GDSC 7][GDSC]   |                481 |                     860 |
| [CTRP 2.1][CTRP] |                138 |                    ~700 |

To use an expression and drug sensitivity association based on `CTRP 2.1`
(`GDSC 7` and `NCI60` could be used instead) to infer targeting drugs for the
user's differential expression profile:

```{r, fig.width=5, fig.asp=1}
listExpressionDrugSensitivityAssociation()
ctrp      <- listExpressionDrugSensitivityAssociation()[[2]]
assoc     <- loadExpressionDrugSensitivityAssociation(ctrp)
predicted <- predictTargetingDrugs(diffExprStat, assoc)
plot(predicted, method="rankProduct")

# Plot results for a given drug
plot(predicted, predicted[[1, 1]], method="spearman")
plot(predicted, predicted[[1, 1]], method="gsea")
```

> Compounds are ranked by their relative targeting potential based on the input
differential expression profile (i.e. the 1st-ranked compound has higher 
targeting potential than the 2nd-ranked one).

Candidate targeting drugs can be plotted against the similarity ranking of their
perturbations towards the user's differential expression profile. Note that the
highlighted values are the same compounds for the following plots annotated with
their name, gene target and mechanism of action (MOA), respectively.

```{r, fig.width=5, fig.asp=1}
# Label by compound name
plotTargetingDrugsVSsimilarPerturbations(
  predicted, compareCompounds, column="spearman_rank")
# Label by compound's gene target
plotTargetingDrugsVSsimilarPerturbations(
  predicted, compareCompounds, column="spearman_rank", labelBy="target")
# Label by compound's mechanism of action (MOA)
plotTargetingDrugsVSsimilarPerturbations(
  predicted, compareCompounds, column="spearman_rank", labelBy="moa")
```

# Molecular descriptor enrichment analysis

Next, from our candidate targeting drugs, we will analyse the enrichment of 2D
and 3D molecular descriptors based on CMap and NCI60 compounds. This allows to
check if targeting drugs are particularly enriched in specific drug descriptors
and may be useful to think about the relevance of these descriptors for
targeting a phenotype of interest.

```{r drug set enrichment analysis, fig.width=5, fig.asp=1}
descriptors <- loadDrugDescriptors("CMap", "2D")
drugSets    <- prepareDrugSets(descriptors)
dsea        <- analyseDrugSetEnrichment(drugSets, predicted)
# Plot the 6 most significant drug set enrichment results
plotDrugSetEnrichment(drugSets, predicted,
                      selectedSets=head(dsea$descriptor, 6))
```

# Contact information

All feedback on the program, documentation and associated material (including
this tutorial) is welcome. Please send any suggestions and comments to:

> Nuno Saraiva-Agostinho (nunoagostinho@medicina.ulisboa.pt)
>
> Bernardo P. de Almeida (bernardo.almeida94@gmail.com)
>
> [Disease Transcriptomics Lab, Instituto de Medicina Molecular (Portugal)][iMM]

[iMM]: http://imm.medicina.ulisboa.pt/group/distrans/
[ENCODE]: https://www.encodeproject.org/
[clue.io]: https://clue.io/
[limma]: https://www.ncbi.nlm.nih.gov/pubmed/25605792
[voom]: https://www.ncbi.nlm.nih.gov/pubmed/24485249
[subramanian2017]: https://doi.org/10.1016/j.cell.2017.10.049
[rank product]: https://en.wikipedia.org/wiki/Rank_product
[NCI60]: https://dtp.cancer.gov/discovery_development/nci-60
[CTRP]: https://portals.broadinstitute.org/ctrp/
[GDSC]: https://www.cancerrxgene.org
