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Updated: Nov 9, 2025

Single-cell Gene Expression Using Multiplex RT-qPCR to Characterize Heterogeneity of Rare Lymphoid Populations

Single-cell Gene Expression Using Multiplex RT-qPCR to Characterize Heterogeneity of Rare Lymphoid Populations

Published on: January 19, 2017

MicroCellClust: mining rare and highly specific subpopulations from single-cell expression data.

Alexander Gerniers¹, Orian Bricard², Pierre Dupont¹

¹ICTEAM/INGI/Artificial Intelligence and Algorithms Group, UCLouvain, Louvain-la-Neuve 1348, Belgium.

Bioinformatics (Oxford, England)

|April 8, 2021

Summary

This summary is machine-generated.

This study introduces MicroCellClust, a novel data mining method for identifying rare cell subpopulations in single-cell expression data. The method effectively detects small cell groups with specific gene expression profiles, outperforming existing approaches.

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Last Updated: Nov 9, 2025

Single-cell Gene Expression Using Multiplex RT-qPCR to Characterize Heterogeneity of Rare Lymphoid Populations

Single-cell Gene Expression Using Multiplex RT-qPCR to Characterize Heterogeneity of Rare Lymphoid Populations

Published on: January 19, 2017

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

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Published on: January 10, 2019

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

Area of Science:

Computational Biology
Genomics
Bioinformatics

Background:

Identifying rare cell subpopulations is crucial for single-cell expression data analysis, especially with limited datasets.
Small cell populations often exhibit unique expression profiles that are challenging to detect.

Purpose of the Study:

To present a data mining method, MicroCellClust, for identifying small cell subpopulations with highly specific expression profiles.
To formalize this identification as a constrained optimization problem.

Main Methods:

Developed a method extending the max-sum submatrix problem to identify specific genes within rare cell groups.
Implemented MicroCellClust in R and Scala.

Main Results:

MicroCellClust achieved a high F1 score in identifying artificially planted rare human T cells.
Successfully identified CD4 T cell subpopulations in breast cancer samples and cell cycle-related subpopulations.
Detected three rare subpopulations in mouse embryonic stem cells.

Conclusions:

MicroCellClust effectively identifies small cell subsets with specific expression profiles.
The method outperforms typical alternatives in rare cell subpopulation identification.