ConsensusTME: Tumour microenvironment cell estimation
The ConsensusTME package provides a integrative tool for R that uses a consensus approach to generating cancer specific signatures for multiple cell types found within the tumour microenvironment.
These consensus gene sets are then used within a ssGSEA framework to provide normalised enrichment scores for each of the cell types representing the relative abundance of cell types across multiple samples.
Installation
The recommended installation for the R package is to use the install_github function from the devtools package.
install.packages("devtools")
devtools::install_github("cansysbio/ConsensusTME")Usage
Cell Type Estimation
library(ConsensusTME)
bulkExpMatrix <- as.matrix(read.delim(bulkGeneExpression.txt, row.names = 1))
ConsensusTME::consensusTMEAnalysis(bulkExpMatrix, cancer = "OV", statMethod = "ssgsea")Example shows running ConsensusTME for ovarian samples where bulkExpMatrix is a numerical matrix
with HUGO gene symbols as row names.
Currently ConsensusTME can be run for the following
TCGA cancer types viewable through ConsensusTME::cancerAll:
ACC BLCA BRCA CESC CHOL COAD DLBC ESCA GBM HNSC KICH KIRC KIRP LGG LIHC LUAD
LUSC MESO OV PAAD PCPG PRAD READ SARC SKCM STAD TGCT THCA THYM UCEC UCS UVMThe statMethod argument gives the option to run ConsensusTME gene sets with the following statistical frameworks:
ssgsea singScore gsva plage plage zscoreFurther information can be found in the documentation ?consensusTMEAnalysis
Run Gene Set Enrichment Separately
To use the same statistical framework as ConsensusTME with a chosen signature from the consensus compendium.
bindeaGeneSet <- ConsensusTME::methodSignatures$Bindea
ConsensusTME::geneSetEnrichment(bulkExpMatrix, bindeaGeneSet)
Generate ConsensusTME Gene Sets
ConsensusTME gene sets can be generated for other uses. N.B. These gene sets are curated to be good signatures for immune cells within the tumour microenvironment. For immune cells in a different biological context other signatures may be more appropriate.
Pre-processed ConsensusTME gene sets can be accessed with ConsensusTME::consensusGeneSets
rawMethodSignatures <- ConsensusTME::methodSignatures
matchedSigs <- ConsensusTME::matchGeneSigs(rawMethodSignatures)
ConsensusTME::buildConsensusGenes(matchedSigs)
Notes
-
It is important to note that results generated in this way will provide quantification of cell types that are relative across samples rather than across cell types.
-
ConsensusTME gene sets have been made to be cancer specific, if analysis is aimed at healthy tissue or PBMCs better results may be obtained by using alternative gene sets or methodologies.
-
ConsensusTME is by design an evolvable tool. As more gene sets and signature matrices are made available we will assess whether including will improve performance and release a new version subsequently.
-
Please contact any of the authors if you believe there are signatures or approaches that ConsensusTME would benefit from including.
-
A comprehensive and unbiased set of benchmarking experiments were performed to test this approach. A shiny app for exploring results and further information can be found at: http://consensusTME.org
Publication
A manuscript is currently under consideration for publication, to cite currently please refer to the bioRxiv preprint: