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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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Updated: Jul 4, 2025

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

Semi-supervised integration of single-cell transcriptomics data.

Massimo Andreatta^1,2,3, Léonard Hérault^1,2,3, Paul Gueguen^1,2,3

¹Department of Oncology, Lausanne Branch, Ludwig Institute for Cancer Research, CHUV and University of Lausanne, 1011, Lausanne, Switzerland.

Nature Communications

|January 29, 2024

Summary

This summary is machine-generated.

STACAS, a new method for single-cell RNA sequencing (scRNA-seq) data, corrects batch effects by using cell type information. It preserves biological variability better than other methods, even with imperfect labels.

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Last Updated: Jul 4, 2025

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

Transcriptome Analysis of Single Cells

Transcriptome Analysis of Single Cells

Published on: April 25, 2011

Single-cell RNA Sequencing and Analysis of Human Pancreatic Islets

Single-cell RNA Sequencing and Analysis of Human Pancreatic Islets

Published on: July 18, 2019

Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Batch effects are a major challenge in single-cell RNA sequencing (scRNA-seq) data integration.
Existing batch correction methods can lead to overcorrection and loss of crucial biological variability.

Purpose of the Study:

To introduce STACAS, a novel semi-supervised batch correction method for scRNA-seq data.
To preserve biological variability during data integration by leveraging prior cell type knowledge.

Main Methods:

Developed STACAS, a semi-supervised approach for batch effect correction in scRNA-seq.
Evaluated STACAS against unsupervised and supervised methods using an open-source benchmark.

Main Results:

STACAS demonstrated superior performance compared to state-of-the-art unsupervised methods and supervised methods like scANVI and scGen.
The method shows scalability for large datasets and robustness to incomplete or imprecise cell type labels.

Conclusions:

Incorporating prior cell type information is crucial for effective single-cell data integration.
STACAS provides a flexible and high-performing framework for semi-supervised batch effect correction in scRNA-seq.