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Related Experiment Video

Updated: Dec 28, 2025

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Published on: August 3, 2018

scTSSR: gene expression recovery for single-cell RNA sequencing using two-side sparse self-representation.

Ke Jin¹, Le Ou-Yang², Xing-Ming Zhao^3,4

¹School of Mathematics and Statistics, Hubei Key Laboratory of Mathematical Sciences, Central China Normal University, Wuhan 430079, China.

Bioinformatics (Oxford, England)

|February 20, 2020

Summary

Single-cell RNA sequencing (scRNA-seq) data often contains noise from dropout events. Our scTSSR method effectively imputes gene expression, improving downstream analysis accuracy for scRNA-seq studies.

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Targeted RNA Sequencing Assay to Characterize Gene Expression and Genomic Alterations

Targeted RNA Sequencing Assay to Characterize Gene Expression and Genomic Alterations

Published on: August 4, 2016

Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Single-cell RNA sequencing (scRNA-seq) provides high-resolution gene expression data.
Technical noise, particularly dropout events, complicates scRNA-seq data analysis.
Accurate gene expression recovery is crucial for reliable downstream interpretation.

Purpose of the Study:

To develop and evaluate scTSSR, a novel imputation method for scRNA-seq data.
To address the challenge of noise introduced by dropout events in gene expression matrices.
To enhance the accuracy of gene expression level recovery in scRNA-seq.

Main Methods:

scTSSR utilizes a two-side sparse self-representation model.
It simultaneously leverages information from similar genes and similar cells.
The method was evaluated using down-sampling experiments and comparisons with existing imputation techniques.

Main Results:

scTSSR accurately captures gene-specific Gini coefficients and gene-to-gene correlations.
It outperforms existing methods in recovering true gene expression levels.
scTSSR demonstrates competitive performance in differential expression analysis, cell clustering, and trajectory inference.

Conclusions:

scTSSR is an effective imputation method for scRNA-seq data.
The approach improves the reliability of downstream analyses.
The R package for scTSSR is publicly available for broader application.