@article{f3d50f79519e485f85a93ae599825412,
title = "ImMquant: A user-friendly tool for inferring immune cell-type composition from gene-expression data",
abstract = "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.",
author = "Amit Frishberg and Avital Brodt and Yael Steuerman and Irit Gat-Viks",
note = "Publisher Copyright: {\textcopyright} The Author 2016. Published by Oxford University Press.",
year = "2016",
month = dec,
day = "15",
doi = "10.1093/bioinformatics/btw535",
language = "אנגלית",
volume = "32",
pages = "3842--3843",
journal = "Bioinformatics",
issn = "1367-4803",
publisher = "Oxford University Press",
number = "24",
}