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RNA-seq

RNA-seq

RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases.
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...

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Related Experiment Video

Updated: Mar 8, 2026

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

Single-cell mRNA quantification and differential analysis with Census.

Xiaojie Qiu^1,2, Andrew Hill¹, Jonathan Packer¹

¹Department of Genome Sciences, University of Washington, Seattle, Washington, USA.

|January 24, 2017

Summary

This summary is machine-generated.

The Census algorithm improves single-cell RNA sequencing analysis by converting expression levels into transcript counts, enhancing accuracy for identifying gene expression and cell states. This method aids in discovering rare cell types and developmental patterns without spike-ins.

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Last Updated: Mar 8, 2026

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Area of Science:

Genomics
Computational Biology
Molecular Biology

Background:

Single-cell RNA sequencing (scRNA-seq) is crucial for identifying rare cell types and cellular states.
High measurement variability in scRNA-seq complicates the accurate assessment of transcriptional differences between cells.

Purpose of the Study:

To introduce the Census algorithm for converting relative RNA-seq expression levels into relative transcript counts.
To enhance the accuracy of scRNA-seq data analysis without requiring experimental spike-in controls.
To enable robust analysis of gene expression, splicing, and allelic imbalance across multiple regulatory layers.

Main Methods:

Developed the Census algorithm to transform normalized read counts into relative transcript counts.
Applied Census to reanalyze existing scRNA-seq datasets from developmental and disease studies.
Integrated Census counts with established regression techniques for statistical analysis.

Main Results:

Census analysis demonstrated significantly improved accuracy compared to standard normalized read counts.
The algorithm enabled the development of novel statistical tests for identifying developmentally regulated genes.
Robust analysis of cell-fate-dependent gene expression, splicing patterns, and allelic imbalances was achieved.

Conclusions:

The Census algorithm provides a powerful tool for overcoming scRNA-seq variability and enabling more accurate biological insights.
Census facilitates deeper understanding of gene regulation at multiple levels in various biological contexts.
The Census algorithm is integrated into the Monocle 2 toolkit, making it accessible for broader research applications.