--- title: 'cTRAP: identifying candidate causal perturbations from differential gene expression data' author: Bernardo P. de Almeida & Nuno Saraiva-Agostinho date: "`r Sys.Date()`" output: rmarkdown::html_vignette: toc: true vignette: > \usepackage[utf8]{inputenc} %\VignetteIndexEntry{cTRAP: identifying candidate causal perturbations from differential gene expression data} %\VignetteEngine{knitr::rmarkdown} editor_options: chunk_output_type: console --- --- # 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$gene_id <- convertENSEMBLtoGeneSymbols(counts$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. ```{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$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)][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