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Related Experiment Video

Updated: May 15, 2026

Single-cell Gene Expression Profiling Using FACS and qPCR with Internal Standards
10:50

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

Published on: February 25, 2017

Data exploration, quality control and testing in single-cell qPCR-based gene expression experiments.

Andrew McDavid1, Greg Finak, Pratip K Chattopadyay

  • 1Department of Statistics, University of Washington, Seattle, WA 98195, USA.

Bioinformatics (Oxford, England)
|December 26, 2012
PubMed
Summary
This summary is machine-generated.

This study introduces a new statistical framework for analyzing single-cell gene expression data from microfluidic quantitative polymerase chain reactions (qPCR). The developed methods improve the analysis of cellular heterogeneity and identify differential gene expression more powerfully.

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Last Updated: May 15, 2026

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

Published on: January 19, 2017

Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Cell populations exhibit inherent heterogeneity due to varying biochemical states.
  • High-throughput single-cell gene expression measurement technologies are emerging.
  • Limited analytical tools exist for single-cell quantitative polymerase chain reactions (qPCR) data.

Purpose of the Study:

  • To develop a statistical framework for analyzing single-cell qPCR data.
  • To address challenges in quality control and statistical analysis of cellular heterogeneity.
  • To create robust methods for differential gene expression analysis in single cells.

Main Methods:

  • Developed a statistical framework for single-cell gene expression data exploration and quality control.
  • Proposed a statistical model for genes that can be 'on' (continuous expression) or 'off' (zero expression).
  • Derived a combined likelihood ratio test for differential expression incorporating both discrete and continuous gene expression components.

Main Results:

  • Established quality control criteria to filter unreliable single-cell measurements.
  • Demonstrated that the combined differential expression test is more powerful than tests using only discrete or continuous components.
  • Showcased improved power compared to a t-test on zero-inflated data.

Conclusions:

  • The presented statistical framework effectively analyzes single-cell gene expression data from microfluidic arrays.
  • The developed methods enhance the assessment of cellular heterogeneity and differential expression.
  • The tools are generalizable beyond the specific platform used in the study.