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Updated: Oct 15, 2025

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

Data-driven comparison of multiple high-dimensional single-cell expression profiles.

Daigo Okada¹, Jian Hao Cheng², Cheng Zheng²

¹Center for Genomic Medicine, Graduate School of Medicine, Kyoto University, Nanbusogo-Kenkyu-To-1, 5F, 53 Syogoin-Kawaramachi, Sakyo-ku, Kyoto, 606-8507, Japan. dokada@genome.med.kyoto-u.ac.jp.

Journal of Human Genetics

|November 1, 2021

Summary

This summary is machine-generated.

A novel computational method enables data-driven comparisons of single-cell expression datasets from case and control groups. This approach enhances disease mechanism discovery and downstream machine learning analyses.

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Last Updated: Oct 15, 2025

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

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Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Published on: July 29, 2022

Area of Science:

Computational biology
Immunogenomics
Systems biology

Background:

Single-cell expression data, including cytometry and single-cell RNA sequencing (scRNA-seq), offers deep insights into cellular heterogeneity.
Comparing case and control samples is crucial for understanding disease mechanisms.
Traditional methods often rely on predefined cellular subsets, limiting comprehensive analysis.

Purpose of the Study:

To introduce a data-driven computational method for comparing single-cell expression datasets.
To overcome limitations of conventional subset-based comparisons.
To facilitate advanced analyses like machine learning and gene set analysis.

Main Methods:

Development of a novel, entirely data-driven computational pipeline.
Application to analyze and compare single-cell expression data (cytometry, scRNA-seq) between case and control donors.
Integration with downstream analytical frameworks.

Main Results:

The proposed method provides a more comprehensive comparison of single-cell expression datasets.
It effectively bypasses the need for pre-defined cellular subsets.
Enables seamless integration with machine learning and gene set enrichment analyses.

Conclusions:

This data-driven approach offers a powerful new tool for dissecting disease mechanisms using single-cell data.
It enhances the utility of cytometry and scRNA-seq data for biological discovery.
Facilitates more sophisticated computational analyses of complex biological systems.