ArticlePatterns (New York, N.Y.)2021
Noise regularization removes correlation artifacts in single-cell RNA-seq data preprocessing.
Article in Patterns (New York, N.Y.), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- A unified framework for selecting and evaluating cell-type-specific gene co-expressions in single-cell data.Briefings in bioinformatics · 2026Article
- Network based simultaneous embedding of cells and marker genes from scRNA-seq studies.Briefings in bioinformatics · 2025Article
- Sepsis-trained macrophages promote antitumoral tissue-resident T cells.Nature immunology · 2024Article
- Cell-type-specific co-expression inference from single cell RNA-sequencing data.Nature communications · 2023Article
- Debiased personalized gene coexpression networks for population-scale scRNA-seq data.Genome research · 2023Article
- Dozer: Debiased personalized gene co-expression networks for population-scale scRNA-seq data.bioRxiv : the preprint server for biology · 2023Article
- scAN1.0: A reproducible and standardized pipeline for processing 10X single cell RNAseq data.In silico biology · 2023Article
- Cell-type-specific co-expression inference from single cell RNA-sequencing data.bioRxiv : the preprint server for biology · 2022Article
- Sprod for de-noising spatially resolved transcriptomics data based on position and image information.Nature methods · 2022Article
- Protocol for hybrid flux balance, statistical, and machine learning analysis of multi-omic data from the cyanobacteriumSTAR protocols · 2021Article
- What do cellular responses to acidity tell us about cancer?Cancer metastasis reviews · 2021Review
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
With the rapid advancement of single-cell RNA-sequencing (scRNA-seq) technology, many data-preprocessing methods have been proposed to address numerous systematic errors and technical variabilities inherent in this technology. While these methods have been demonstrated to be effective in recovering individual gene expression, the suitability to the inference of gene-gene associations and subsequent gene network reconstruction have not been systemically investigated. In this study, we benchmarked five representative scRNA-seq normalization/imputation methods on Human Cell Atlas bone marrow data with respect to their impacts on inferred gene-gene associations. Our results suggested that a considerable amount of spurious correlations was introduced during the data-preprocessing steps due to oversmoothing of the raw data. We proposed a model-agnostic noise-regularization method that can effectively eliminate the correlation artifacts. The noise-regularized gene-gene correlations were further used to reconstruct a gene co-expression network and successfully revealed several known immune cell modules.
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