ImmQuant: a user-friendly tool for inferring immune cell-type composition from gene-expression data | Zendy

Amit Frishberg | Zendy; Avital Brodt | Zendy; Yael Steuerman | Zendy; Irit GatViks | Zendy

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ImmQuant: a user-friendly tool for inferring immune cell-type composition from gene-expression data

Author(s) -

Amit Frishberg,

Avital Brodt,

Yael Steuerman,

Irit GatViks

Publication year - 2016

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btw535

Subject(s) - upload , deconvolution , computer science , visualization , computational biology , transcriptome , cell type , software , immune system , data mining , cell , bioinformatics , biology , gene , gene expression , algorithm , genetics , world wide web , programming language

: The composition of immune-cell subsets is key to the understanding of major diseases and pathologies. Computational deconvolution methods enable researchers to investigate immune cell quantities in complex tissues based on transcriptome data. Here we present ImmQuant, a software tool allowing immunologists to upload transcription profiles of multiple tissue samples, apply deconvolution methodology to predict differences in cell-type quantities between the samples, and then inspect the inferred cell-type alterations using convenient visualization tools. ImmQuant builds on the DCQ deconvolution algorithm and allows a user-friendly utilization of this method by non-bioinformatician researchers. Specifically, it enables investigation of hundreds of immune cell subsets in mouse tissues, as well as a few dozen cell types in human samples.

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