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Updated: Mar 16, 2026

A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations

A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations

Published on: October 25, 2018

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

Amit Frishberg¹, Avital Brodt¹, Yael Steuerman¹

¹Department of Cell Research and Immunology, George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv 6997801, Israel.

Bioinformatics (Oxford, England)

|August 18, 2016

Summary

This summary is machine-generated.

ImmQuant is a new software tool that helps researchers analyze immune cell quantities in tissues using transcriptome data. It simplifies complex computational deconvolution for better understanding of diseases.

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Area of Science:

Immunology
Computational Biology
Bioinformatics

Background:

Immune cell composition is crucial for understanding diseases.
Computational deconvolution of transcriptome data estimates immune cell quantities in tissues.
Existing methods can be complex for non-bioinformaticians.

Purpose of the Study:

To present ImmQuant, a user-friendly software tool for immune cell deconvolution.
To enable researchers to analyze immune cell-type quantities from transcriptome data.
To facilitate the investigation of cell-type alterations in various tissue samples.

Main Methods:

ImmQuant utilizes the DCQ deconvolution algorithm.
Researchers upload transcription profiles of multiple tissue samples.
The tool predicts cell-type quantities and visualizes inferred alterations.

Main Results:

ImmQuant allows for the investigation of hundreds of immune cell subsets in mouse tissues.
It enables the analysis of dozens of cell types in human samples.
The software provides convenient visualization tools for inspecting cell-type changes.

Conclusions:

ImmQuant offers a user-friendly approach to computational deconvolution for immunologists.
It empowers non-bioinformatician researchers to study immune cell composition.
The tool aids in understanding disease-related changes in immune cell populations.