Methods

• TOAST - 019-1778-0)); tags: bioconductor; transcriptomics; reference-free; partial_reference-free
• TOAST-csDeconv
• SingleR - cell RNA sequencing data, by leveraging reference transcriptomic datasets of pure cell types to infer the cell of origin of each single cell independently (see also: [Aran et al 2019](https://www.nature.com/articles/s41590-018-0276-y)); tags: single-cell; transcriptomics; bioconductor; r
• xCell - 017-1349-1)); tags: bulk; transcriptomics
• DCQ
• seq-ImmuCC - Seq platforms (see also: (Chen et al 2018)[https://www.frontiersin.org/articles/10.3389/fimmu.2018.01286/full]); tags: immune; immune_cell; murine; mouse; transcriptomics
• TIMER
• quanTIseq - seq data (see also: [Finotello et al 2019](https://genomemedicine.biomedcentral.com/articles/10.1186/s13073-019-0638-6)); tags: immune; immune_system; tumor; cancer;
• MethylCIBERSORT - based method using CBS algorithm. Implemented as an R package (see also: [Chakravarthy et al 2018](https://www.nature.com/articles/s41467-018-05570-1#Sec12)); tags: methylation; dna_methylation; dnam; epigenetics;
• RefFreeEWAS - free method for conducting EWAS while deconvoluting DNA methylation arising as mixtures of cell types. This method is similar to surrogate variable analysis (SVA and ISVA), except that it makes additional use of a biological mixture assumption. (see also: [Houseman et al 2016](https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-016-1140-4#Sec10)). tags: methylation; dna_methylation; dnam; epigenetics; r; cran; toast;
• Houseman et al 2012 - based method for deconvolutino of blood cell types from DNA methylation array data. (see also: [minfi](https://www.bioconductor.org/packages/release/bioc/html/minfi.html)). tags: methylation; dna_methylation; dnam; epigenetics; minfi; r; bioconductor
• DeconRNASeq - Seq data. It modeled expression levels from heterogeneous cell populations in mRNA-Seq as the weighted average of expression from different constituting cell types and predicted cell type proportions of single expression profiles. ([Gong et al 2013](https://academic.oup.com/bioinformatics/article/29/8/1083/229442))
• EPIC
• MCP-counter - counter (MCP-counter) method, which allows the robust quantification of the absolute abundance of eight immune and two stromal cell populations in heterogeneous tissues from transcriptomic data. ([Becht et al 2016](https://genomebiology.biomedcentral.com/articles/10.1186/s13059-016-1070-5))
• ESTIMATE
• ISOpure
• DSA - type specific gene expression profiles from mixed tissue samples that is unbiased and does not require prior knowledge of cell type frequencies.
• dtangle - throughput gene profiling data ([Hunt et al 2019](https://academic.oup.com/bioinformatics/article/35/12/2093/5165376?login=false)).
• BayesSpace - 021-00935-2)).
• SPLITR - nucleus and bulk RNA-seq data, enabling phenotype-aware deconvolution and correcting for systematic discrepancies between bulk and single-cell data ([Park et al 2021](https://www.biorxiv.org/content/10.1101/2021.01.21.426000v1); tags: eqtl; gwas; genetics).
• Hippen et al 2023
• TME - Sánchez et al 2018](https://www.biorxiv.org/content/10.1101/437533v2.full)); tags: benchmark; tumor; cancer; microenvironment
• omnideconv.org - friendly tools and resources for the cell-type deconvolution of any organism and tissue profiled with bulk transcriptomics.
• `awesome-awesomeness` - *" style resources.
• mMCP-counter - counter, a tool to estimate the immune and stromal composition of heterogeneous tissue, from transcriptomic data. It is distributed as a R package. (see also: (Petitprez et al 2020)[https://genomemedicine.biomedcentral.com/articles/10.1186/s13073-020-00783-w]); tags: murine; mouse; r; mcp
• MCPcounter - counter (MCP-counter) method, which allows the robust quantification of the absolute abundance of eight immune and two stromal cell populations in heterogeneous tissues from transcriptomic data (see also: [Becht et al 2016](https://genomebiology.biomedcentral.com/articles/10.1186/s13059-016-1070-5)); tags: immune; immune_cell; blood; blood_cell; transcriptomics; microenvironment; human
• SPOTlight - cell reference (see also: [Bayes et al 2021](https://marcelosua.github.io/SPOTlight/)); tags: r; bioconductor; spatial; transcriptomics; spot; spot_deconvolution
• Tangram - cell (or single-nucleus) gene expression data onto spatial gene expression data. (see also: [Biancalani and Scalia et al 2021](https://www.nature.com/articles/s41592-021-01264-7)); tags: python; pytorch; scanpy; spatial; transcriptiomics; spot; spot_deconvolution
• MuSiC - Subject SIngle Cell deconvolution (MuSiC) is an analysis toolkit for single-cell RNA-Seq experiments ([Wang et al 2019](https://www.nature.com/articles/s41467-018-08023-x); tags: single-cell; transcriptomics; rna-seq; bulk).
• MuSiC2 - Subject SIngle Cell deconvolution 2 (MuSiC2). Cell type deconvolution for multi-condition bulk RNA-seq data ([Fan et al 2022](https://academic.oup.com/bib/article-abstract/23/6/bbac430/6751147?redirectedFrom=fulltext); tags: single-cell; transcriptomics; rna-seq; bulk; case_control)
• Bisque - cell information ([Jew et al 2020](https://www.nature.com/articles/s41467-020-15816-6); tags: single-cell; transcriptomics; rna-seq; bulk).
• EpiDISH - Sample-Heterogeneity. (see also: [Teschendorff et al 2017](https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-017-1511-5)); tags: methylation; dna_methylation; dnam; epigenetics;
• MethylResolver - 020-01146-2)); tags: methylation; dna_methylation; dnam; epigenetics; r
• ARIC - oup-com.proxy1.library.jhu.edu/bib/article/23/1/bbab362/6361035)). tags: transcriptomics; methylation; dna_methylation; dnam; epigenetics; python; svr;
• CIBERSORTx - seq transcriptomics data ([Newman et al 2019](https://www.nature.com/articles/s41587-019-0114-2)).