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Related Concept Videos

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RNA-seq

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The gene expression in cells is regulated at different stages: (i) transcription, (ii) RNA processing, (iii) RNA localization, and (iv) translation. Transcriptional regulation is mediated by regulatory proteins such as transcription factors, activators, or repressors—these control gene expression by initiating or inhibiting the transcription of genes. Once a precursor or pre-mRNA is produced, it undergoes post-transcriptional modification, including 5' capping, splicing, and the...
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Updated: Jun 28, 2025

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scINRB: single-cell gene expression imputation with network regularization and bulk RNA-seq data.

Yue Kang1, Hongyu Zhang1, Jinting Guan1,2

  • 1Department of Automation, Xiamen University, Xiamen, Fujian, China.

Briefings in Bioinformatics
|April 11, 2024
PubMed
Summary
This summary is machine-generated.

scINRB accurately imputes gene expression in single-cell RNA sequencing data, overcoming dropout events by leveraging network regularization and bulk RNA sequencing data. This improves cell type identification and functional analysis.

Keywords:
bulk RNA-seq dataimputationnetwork regularizationnon-negative matrix factorizationsingle-cell expression data

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Single-cell RNA sequencing (scRNA-seq) is crucial for studying cell heterogeneity and building cell atlases.
  • Dropout events (zero gene expression) in scRNA-seq data introduce bias, hindering accurate cell type and function characterization.
  • Existing imputation methods often show suboptimal performance across diverse datasets and scenarios.

Purpose of the Study:

  • To develop an accurate and robust imputation method for single-cell gene expression data.
  • To preserve original cell-cell and gene-gene correlations.
  • To integrate bulk RNA sequencing (bulk RNA-seq) data information.

Main Methods:

  • Proposed scINRB, a novel imputation method utilizing network-regularized non-negative matrix factorization.
  • Ensured imputed data maintains cell-cell and gene-gene similarities.
  • Incorporated bulk RNA-seq data to approximate average gene expression.

Main Results:

  • scINRB demonstrated accurate gene expression recovery, even with high dropout rates and data dimensions.
  • The method effectively preserved cell-cell and gene-gene similarities.
  • Significant improvements were observed in downstream analyses like visualization, clustering, and trajectory inference.

Conclusions:

  • scINRB offers a robust solution for single-cell gene expression imputation.
  • The method enhances the reliability of scRNA-seq data analysis.
  • scINRB facilitates more accurate cell type identification and functional characterization.