Chapter 3 🌱 Tumor Microenviroment Analysis

3.1 Availiable TME Analysis Method in tigeR

Algorithm license PMID
TIMER free (GPL 2.0) 27549193
CIBERSORT free for non-commercial use only 25822800
MCPCounter free (GPL 3.0) 27765066
xCell free (GPL 3.0) 29141660
IPS free (BSD) 28052254
EPIC free for non-commercial use only (Academic License) 29130882
ESTIMATE free (GPL 2.0) 24113773
ABIS free (GPL 2.0) 30726743
ConsensusTME free (GPL 3.0) 31641033
quanTIseq free (BSD) 31126321

3.2 Derive the Proportions of Different TME Cell Types

 You can use the function deconv_TME() to derive the proportions of different tumor microenvironment cell types from gene expression data with these tools.

devtools::install_github('dviraran/xCell')
devtools::install_github("GfellerLab/EPIC")
devtools::install_github("cansysbio/ConsensusTME")
devtools::install_github("federicomarini/quantiseqr")

## TIMER
frac1 <- deconv_TME(MEL_GSE78220,method="TIMER")

## CIBERSORT
frac2 <- deconv_TME(MEL_GSE78220,method="CIBERSORT")

## MCPCounter
frac3 <- deconv_TME(MEL_GSE78220,method="MCPCounter")

## xCell
frac4 <- deconv_TME(MEL_GSE78220,method="xCell")

## IPS
frac5 <- deconv_TME(MEL_GSE78220,method="IPS")

## EPIC
frac6 <- deconv_TME(MEL_GSE78220,method="epic")

## ESTIMATE
frac7 <- deconv_TME(MEL_GSE78220,method="ESTIMATE")

## ABIS
frac8 <- deconv_TME(MEL_GSE78220,method="ABIS")

## ConsensusTME
frac9 <- deconv_TME(MEL_GSE78220,method="ConsensusTME")

## quanTIseq
frac10 <- deconv_TME(MEL_GSE78220,method="quanTIseq")

3.3 Visualization and Comparing the Cell Proportions

cell1 <- c("T cells CD4","Neutrophil", "Macrophage","mDCs","B cells", "T cells CD8")
pie1 <- fraction_pie(cell_name_filter(frac1),feature=factor(cell1, levels = cell1))

cell2 <- c("DCs resting", "T cells CD8", "T cells CD4 naive", "Macrophages M2", "Yd T cells", "Monocytes","Mast cells resting", "Neutrophils", "Tregs","B cells naive")
pie2 <- fraction_pie(cell_name_filter(frac2[[1]][1:22,]),feature=factor(cell2, levels = cell2))

3.4 Searching for Key Cell Types Influencing Immune Therapy Response

## TIMER
TM <- deconv_TME(MEL_GSE91061,method = "TIMER")
TM_SE <- SummarizedExperiment(assays=SimpleList(TM),
                               colData=colData(MEL_GSE91061))
browse_biomk(SE=TM_SE)

📝 More Details about TME Analysis

 TIMER is a comprehensive tool for systematical analysis of immune infiltrates across diverse cancer types.

 CIBERSORT is an analytical tool from the Alizadeh Lab and Newman Lab to impute gene expression profiles and provide an estimation of the abundances of member cell types in a mixed cell population, using gene expression data.

 xCell is a gene signatures-based method learned from thousands of pure cell types from various sources. xCell applies a novel technique for reducing associations between closely related cell types.

 ConsensusTME a consensus approach to generating cancer specific signatures for multiple cell types found within the tumour microenvironment.