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Related Experiment Video

Updated: Jul 26, 2025

Single-cell Gene Expression Profiling Using FACS and qPCR with Internal Standards

Single-cell Gene Expression Profiling Using FACS and qPCR with Internal Standards

Published on: February 25, 2017

A COMPOSITIONAL MODEL TO ASSESS EXPRESSION CHANGES FROM SINGLE-CELL RNA-SEQ DATA.

Xiuyu Ma¹, Keegan Korthauer², Christina Kendziorski³

¹Department of Statistics, University of Wisconsin-Madison.

The Annals of Applied Statistics

|June 19, 2023

Summary

This summary is machine-generated.

This study introduces an empirical Bayesian mixture model to detect changes in single-cell gene expression distributions. The novel approach improves sensitivity by integrating cell subtype information for more accurate gene scoring.

Keywords:

Local false discovery rate clustering double Dirichlet mixture empirical Bayes mixture model

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Last Updated: Jul 26, 2025

Single-cell Gene Expression Profiling Using FACS and qPCR with Internal Standards

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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
Genomics
Biostatistics

Background:

Accurately scoring genes for expression changes in single-cell data is challenging.
Existing methods may not fully leverage cell-subtype heterogeneity.
Detecting subtle shifts in gene expression distributions requires robust statistical approaches.

Purpose of the Study:

To develop and evaluate a novel empirical Bayesian mixture approach for scoring gene expression changes.
To enhance the detection of differential gene expression distributions by incorporating cell-subtype information.
To improve sensitivity in identifying genes with altered expression patterns across cellular conditions.

Main Methods:

An empirical Bayesian mixture model was developed.
Cell subtype structure from cluster analysis was leveraged.
A prior distribution over multinomial probability vectors was constructed.
Posterior probabilities for gene distribution changes were derived.

Main Results:

The proposed approach demonstrated improved sensitivity in numerical experiments.
Integration of cell clustering enhanced gene-level information on expression changes.
The model allows for gene-specific mixtures over subtypes.
Novel information sharing between cell clustering and gene scoring was achieved.

Conclusions:

The empirical Bayesian mixture approach offers a sensitive method for detecting differential gene expression distributions.
Leveraging cell-subtype structure significantly boosts the power of gene-level analysis.
This method provides a robust framework for analyzing single-cell expression data.