EcoTyper is a machine learning framework for large-scale identification of cell states and cellular ecosystems from gene expression data.
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Updated
Mar 28, 2024 - R
EcoTyper is a machine learning framework for large-scale identification of cell states and cellular ecosystems from gene expression data.
a fully Bayesian approach to deconvolve tumor microenvironment
Expression Weighted Celltype Enrichment. See the package website for up-to-date instructions on usage.
ConsensusTME Gene Sets and R Script
Causal Deconvolution of Networks by Algorithmic Generative Models
Immune Cell Gene Signatures for Profiling the Microenvironment of Solid Tumours
Cell-Centric View of Tissue Transcriptome Measuring Cellular Compositions of Immune Microenvironment From Mouse RNA-Seq Data.
Snakemake pipeline for benchmarking cell-type deconvolution methods and deconvolving real bulk RNA-seq data with the use of scRNA-seq datasets
meta-analysis of new and published human WAT single cell data
Tool for Motif Deconvolution in large HLA-II ligand datasets without the need of prior alignment.
The deconvR is an R package designed for analyzing deconvolution of the bulk sample(s) using an atlas of reference omic signature profiles and a user-selected model.
digitalDLSorteR: An R package to deconvolute bulk RNA-Seq using scRNA-Seq data
Weighted In Silico Pathology: a novel approach to assess intra-tumoral heterogeneity
CimpleG, an R package to find (small) CpG signatures.
Tools for finding Total RNA Expression Genes in single nucleus RNA-seq data
Deconvolution of spatial transcriptomics data based on Deep Learning
Cell type-specific Histone Acetylation Score
A single cell transcriptomics based deconvolution pipeline for Leukemia
Semi-Supervise cellular deconvolution of Bulk RnaSeq using NMF and CiberSortx, DCQ or others
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