epiregulon - Gene regulatory network inference from single cell epigenomic data
Gene regulatory networks model the underlying gene regulation hierarchies that drive gene expression and observed phenotypes. Epiregulon infers TF activity in single cells by constructing a gene regulatory network (regulons). This is achieved through integration of scATAC-seq and scRNA-seq data and incorporation of public bulk TF ChIP-seq data. Links between regulatory elements and their target genes are established by computing correlations between chromatin accessibility and gene expressions.
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singlecellgeneregulationnetworkinferencenetworkgeneexpressiontranscriptiongenetargetcpp
7.41 score 29 stars 1 dependents 33 scripts 340 downloadsepiregulon.extra - Companion package to epiregulon with additional plotting, differential and graph functions
Gene regulatory networks model the underlying gene regulation hierarchies that drive gene expression and observed phenotypes. Epiregulon infers TF activity in single cells by constructing a gene regulatory network (regulons). This is achieved through integration of scATAC-seq and scRNA-seq data and incorporation of public bulk TF ChIP-seq data. Links between regulatory elements and their target genes are established by computing correlations between chromatin accessibility and gene expressions.
Last updated
generegulationnetworkgeneexpressiontranscriptionchiponchipdifferentialexpressiongenetargetnormalizationgraphandnetwork
4.86 score 18 scripts 336 downloadsscMultiome - Collection of Public Single-Cell Multiome (scATAC + scRNAseq) Datasets
Single cell multiome data, containing chromatin accessibility (scATAC-seq) and gene expression (scRNA-seq) information analyzed with the ArchR package and presented as MultiAssayExperiment objects.
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packagetypedataexperimenthubsinglecelldataexpressiondatasequencingdatahomo_sapiens_datacellculturetissuegeodatapkg
4.22 score 11 scripts