cTRAP: identifying candidate causal perturbations from differential gene expression data


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 (CMap; Subramanian et al., Cell 2017). 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 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:


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:

gene <- "EIF4G1"
cellLine <- "HepG2"

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

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

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

# 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$gene_id <- convertENSEMBLtoGeneSymbols(counts$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.

# Get t-statistics of differential expression with respective gene names 
# (expected input for downstream analyses)
diffExprStat <- diffExpr$t
names(diffExprStat) <- diffExpr$Gene_symbol

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:

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

# Summarise conditions for all CMap perturbations

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

# Summarise conditions for a specific CMap perturbation in HepG2 cell line
    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 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

# 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",

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

# 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")
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:

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

compareKD <- rankSimilarPerturbations(diffExprStat, cmapPerturbationsKD)

To compare against selected CMap small molecule perturbations:

compareCompounds <- rankSimilarPerturbations(diffExprStat, 

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.

# 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)

# Most negatively associated perturbations

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

For small molecules:

# Most positively and negatively associated perturbations
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).

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:

# 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)

# 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:

attr(compareKD, "zscoresFilename") <- cmapPerturbationsKD
# 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:

attr(compareCompounds, "zscoresFilename") <- cmapPerturbationsCompounds
# 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 | 481 | 860 | | CTRP 2.1 | 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:

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.

# Label by compound name
  predicted, compareCompounds, column="spearman_rank")
# Label by compound's gene target
  predicted, compareCompounds, column="spearman_rank", labelBy="target")
# Label by compound's mechanism of action (MOA)
  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.

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$pathway, 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)

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cTRAP documentation built on Nov. 8, 2020, 10:58 p.m.