Abstract
Summary: 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.Availability and implementation: ImmQuant is available for download at http://csgi.tau.ac.il/ImmQuant/.Contact: iritgv@post.tau.ac.ilSupplementary information: Supplementary data are available at Bioinformatics online.
Highlights
The repertoire of changes in immune-cell types between different physiological states and the accurate determination of these changes can facilitate biomedical research, diagnosis and treatment
Most deconvolution methods were proven useful in human applications, where the reference datasets are of intermediate sizes [22 and 38 cell types in the IRIS and DMAP reference datasets, respectively (Abbas et al, 2005; Novershtern et al, 2011)]
digital cell quantifier (DCQ) takes as input (i) a relative expression profile, calculated as the transcription foldchange between two heterogeneous samples; (ii) a reference dataset, consisting of transcriptional signatures of immune-cell subsets and (iii) a list of informative marker genes
Summary
The repertoire of changes in immune-cell types between different physiological states and the accurate determination of these changes can facilitate biomedical research, diagnosis and treatment Despite this important attribute, experimental quantification of cell types has remained relatively low throughput. Most deconvolution methods were proven useful in human applications, where the reference datasets are of intermediate sizes [22 and 38 cell types in the IRIS and DMAP reference datasets, respectively (Abbas et al, 2005; Novershtern et al, 2011)]. These methods are typically not scalable to the substantially larger number of reference signatures that are available in mouse [207 cell types in the ImmGen dataset (Heng and Painter, 2008)]
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