Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Cell Specific Gene Expression

Cell Specific Gene Expression

Multicellular organisms contain a variety of structurally and functionally distinct cell types, but the DNA in all the cells originated from the same parent cells. The differences in the cells can be attributed to the differential gene expression. Liver cells, whose functions include detoxification of blood, production of bile to metabolize fats, and synthesis of proteins essential for metabolism, must express a specific set of genes to perform their functions. Gene expression also varies with...

Cell Specific Gene Expression

Cell Specific Gene Expression

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Replaying germinal center evolution on a quantified affinity landscape.

Cell·2026

Same author

Tree reconstruction guarantees from CRISPR-Cas9 lineage tracing data using Neighbor-Joining.

Genome research·2026

Same author

Hypervariable loop profiling decodes sequence determinants of antibody stability.

Nature structural & molecular biology·2026

Same author

Inference of germinal center evolutionary dynamics via simulation-based deep learning.

eLife·2026

Same author

Separating selection from mutation in antibody language models.

eLife·2026

Same author

Mapping cis-regulatory mutations at scale in sorghum enables modulation of gene expression.

Nature biotechnology·2026

Same journal

Complete sequencing of medaka genomes reveals the architecture of centromeric satellites, giant mobile elements, and sex chromosomes.

Genome research·2026

Same journal

Convergence and conflict among telomere specialized transposons across 60 million years of Drosophilid evolution.

Genome research·2026

Same journal

A unified analysis of cell type- and trajectory-associated pathways in single-cell data using Phoenix.

Genome research·2026

Same journal

Resf1 is required for proper placental development and configuration of trophoblast cell-specific heterochromatin.

Genome research·2026

Same journal

Telomere-driven replicative crisis is driven by large-scale changes in genomic architecture.

Genome research·2026

Same journal

Spatially informed reference-free cell-type deconvolution for spatial transcriptomics with SpatialCD.

Genome research·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Oct 27, 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

Likelihood-based deconvolution of bulk gene expression data using single-cell references.

Dan D Erdmann-Pham¹, Jonathan Fischer^2,3,4, Justin Hong³

¹Department of Mathematics, University of California, Berkeley, California 94720, USA.

Genome Research

|July 24, 2021

Summary

This summary is machine-generated.

RNA-Sieve accurately estimates cell type proportions in bulk RNA sequencing data. This method disentangles gene expression differences from cell composition variations, improving biological insights.

More Related Videos

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

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

Related Experiment Videos

Last Updated: Oct 27, 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 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

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

Area of Science:

Computational Biology
Genomics
Bioinformatics

Background:

Bulk gene expression analysis is confounded by varying cell type compositions.
Single-cell technologies offer higher resolution but introduce noise.
Accurate cell type proportions are crucial for interpreting gene expression data.

Purpose of the Study:

To develop a method for deconvolving bulk RNA sequencing data to estimate cell type proportions.
To disentangle true differential gene expression from cell composition effects.
To provide accurate cell type proportion estimates using single-cell data.

Main Methods:

A generative model and likelihood-based inference method were developed.
Asymptotic statistical theory and a novel optimization procedure were employed.
The method, RNA-Sieve, was applied to deconvolve bulk RNA-seq data.

Main Results:

RNA-Sieve produced accurate cell type proportion estimates.
The method's effectiveness was demonstrated across diverse real-world scenarios.
A probabilistic framework enabled unique extensions, including confidence intervals.

Conclusions:

RNA-Sieve effectively deconvolves bulk RNA-seq data for precise cell type proportion estimation.
The method enhances the ability to distinguish differential expression from cell composition.
The probabilistic approach offers robust confidence intervals and further analytical possibilities.