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Related Concept Videos

What is Gene Expression?01:42

What is Gene Expression?

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Overview
Gene expression is the process in which DNA directs the synthesis of functional products, that is, proteins. Cells can regulate gene expression at various stages. It allows organisms to generate different cell types and enables cells to adapt to internal and external factors.
Genetic Information Flows from DNA to RNA to Protein
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Updated: Feb 8, 2026

Using an Automated Cell Counter to Simplify Gene Expression Studies: siRNA Knockdown of IL-4 Dependent Gene Expression in Namalwa Cells
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Gene expression distribution deconvolution in single-cell RNA sequencing.

Jingshu Wang1, Mo Huang1, Eduardo Torre2

  • 1Department of Statistics, University of Pennsylvania, Philadelphia, PA 19104.

Proceedings of the National Academy of Sciences of the United States of America
|June 28, 2018
PubMed
Summary
This summary is machine-generated.

Single-cell RNA sequencing (scRNA-seq) data is noisy. We developed DESCEND to deconvolve true gene expression distributions, improving estimates of dispersion and nonzero fractions for better downstream analysis.

Keywords:
Gini coefficientRNA sequencingdifferential expressionhighly variable genessingle-cell transcriptomics

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Area of Science:

  • Genomics
  • Computational Biology
  • Biostatistics

Background:

  • Single-cell RNA sequencing (scRNA-seq) provides gene expression distributions across cells, crucial for understanding variation and identifying cell types.
  • Raw scRNA-seq counts are inherently noisy due to low sequencing coverage, complicating accurate inference of expression distribution properties.
  • Existing methods struggle to reliably estimate gene expression distribution parameters beyond the mean from noisy scRNA-seq data.

Purpose of the Study:

  • To develop a robust method for deconvoluting true cross-cell gene expression distributions from noisy scRNA-seq count data.
  • To improve the estimation of key gene expression distribution properties, including dispersion and the fraction of cells with detectable expression (nonzero fraction).
  • To provide a tool that can account for technical noise and biological covariates in scRNA-seq data analysis.

Main Methods:

  • Proposed a simple technical noise model tailored for scRNA-seq data utilizing unique molecular identifiers (UMIs).
  • Developed the Deconvolution of Single-cell Expression Distribution (DESCEND) method to infer true expression distributions from observed counts.
  • Validated DESCEND's noise model and estimation accuracy using RNA FISH data, data splitting, simulations, and batch effect removal assessments.

Main Results:

  • DESCEND accurately deconvolves true cross-cell gene expression distributions, yielding improved estimates of dispersion and nonzero fractions.
  • The method effectively adjusts for cell-level covariates, including cell size, cell cycle status, and batch effects.
  • Demonstrated improved performance in downstream analyses such as differential gene expression, cell type identification, and marker gene selection.

Conclusions:

  • DESCEND offers a significant advancement in analyzing scRNA-seq data by accurately modeling technical noise.
  • Improved estimation of gene expression distribution properties facilitates more reliable biological interpretations and discoveries.
  • DESCEND enhances the clarity and effectiveness of various downstream analyses in single-cell genomics research.