Table 1.

Bioinformatics tools developed to assess tumor purity, compute cell proportions, and identifying specific cell-type subsets

In silico tools for determining tissue composition	Description	References
UNDO	Identify cell type-specific marker genes, compute sample-wise cellular proportions, and deconvolute mixed expressions into cell-specific expression profiles	[306]
contamDE	Estimate cell proportions and perform differential gene expression analysis from RNA-seq data considering tumor-infiltrating normal cells as contaminants	[260]
ISOpureR	Cancer cells fraction estimation, and personalized patient-specific mRNA abundance profiling from a mixed tumor profile	[261]
ISOLATE	Primary site of origin prediction, sample heterogeneity effect removal and deconvolution, and determination of differentially expressed genes of tumor purity	[307]
ESTIMATE	Gene set enrichment analysis method that uses expression profile of immune, stromal, and tumor cells signature genes to give tumor purity scores	[259]
DeMix	Maximum likelihood-based statistical approach for computing cell fractions, and differential gene expression analysis of tumor purity	[263]
PurBayes	Bayesian statistics modelling approach that uses RNAseq data to estimate sub-clonality and tumor purity	[265]
DeconRNASeq	Deconvolution of heterogeneous tissues using mRNA-seq data. Estimates proportions of distinct immune cell subsets	[308]
PSEA	Computes cell fractions from marker genes expression profiles	[309]
csSAM	Differential gene expression analysis using microarray data for each cell type in the sample and their relative frequencies of occurrence	[254]
NMF	Computes cell-type-specific expression profiles and their proportions without any a-priori information	[310]
DSA	Probabilistic model-based approach that uses RNA-seq data from heterogeneous samples to estimate cell-type-specific transcript abundances	[311]
MMAD	Simultaneous calculation of cell proportions and cell-specific expression profiles; prior knowledge of cell fractions and reference expression profiles are required	[312]
PERT	Probabilistic gene expression deconvolution strategy that corrects perturbations in reference expression profiles of different cell populations of a heterogeneous sample	[313]
LLSR	Computes different cells proportions from reference microarray expression profiles	[314]
CIBERSORT	Estimates cell proportions from complex tissues using their gene expression profiles	[271]
Nanodissection	Computes gene expression profiles of specific cells/tissues using reference expression profiles as training data for this genome-scale machine-learning based approach	[269]
Dsection	Probabilistic model using reference expression profiles and predicted cell proportions information. Estimate cell proportions and cell-specific expression profiles with better accuracy	[268]
MCP-counter	Estimates abundance of two stromal and eight immune cell types of populations in bulk tissues	[251]
EPIC	Computes absolute fractions of tumor and different immune cell types using transcriptomic data	[315]
xCell	Infers abundance of 64 stromal and immune cell types based on cell-specific gene signatures enrichment	[316]
TIMER	Six immune cell-types infiltration quantification across different cancer types based on RNA-seq data	[317]
MethylCIBERSORT	CIBERSORT-based deconvolution method. Uses DNA methylation data from bulk to infer tumor cell fractions	[318]
DeMixT	Extract component-specific proportions and gene expression profiles for every sample	[252]
MuSiC	Single cell RNA sequencing data derived cell type specific expression profiles are used to define cell compositions from bulk RNA sequencing data in complex tissues	[319]
CPM	Deconvolution algorithm that uses single cell RNA sequencing reference expression profiles to infer cellular heterogeneity in complex tissues from bulk transcriptome data	[320]
CIBERSORTx	Estimates sample-wise cell type frequencies from bulk RNA sequencing data using single cell RNA sequencing or bulk-sorted gene expression reference profiles data, and minimizes platform-specific variations	[249]
quanTIseq	Using bulk RNA sequencing data, this method quantitates proportions of 10 types of immune cells	[321]