ArticlePLoS computational biology2022
Addressing the mean-correlation relationship in co-expression analysis.
Article in PLoS computational biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Expression ofInternational journal of molecular sciences · 2026Article
- A unified framework for selecting and evaluating cell-type-specific gene co-expressions in single-cell data.Briefings in bioinformatics · 2026Article
- Variants in NR6A1 cause a novel oculo vertebral renal syndrome.Nature communications · 2025Article
- CorrAdjust unveils biologically relevant transcriptomic correlations by efficiently eliminating hidden confounders.Nucleic acids research · 2025Article
- scMultiMap: Cell-type-specific mapping of enhancers and target genes from single-cell multimodal data.Nature communications · 2025Article
- Evolutionary divergence between homologous X-Y chromosome genes shapes sex-biased biology.Nature ecology & evolution · 2025Article
- eQTLs identify regulatory networks and drivers of variation in the individual response to sepsis.Cell genomics · 2024Article
- Massively integrated coexpression analysis reveals transcriptional regulation, evolution and cellular implications of the yeast noncanonical translatome.Genome biology · 2024Article
- Network-based drug repurposing for schizophrenia.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2024Article
- Cell-type-specific co-expression inference from single cell RNA-sequencing data.Nature communications · 2023Article
- Article
- COXPRESdb v8: an animal gene coexpression database navigating from a global view to detailed investigations.Nucleic acids research · 2023Article
- Cell-type-specific co-expression inference from single cell RNA-sequencing data.bioRxiv : the preprint server for biology · 2022Article
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3 authors.
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Abstract
Estimates of correlation between pairs of genes in co-expression analysis are commonly used to construct networks among genes using gene expression data. As previously noted, the distribution of such correlations depends on the observed expression level of the involved genes, which we refer to this as a mean-correlation relationship in RNA-seq data, both bulk and single-cell. This dependence introduces an unwanted technical bias in co-expression analysis whereby highly expressed genes are more likely to be highly correlated. Such a relationship is not observed in protein-protein interaction data, suggesting that it is not reflecting biology. Ignoring this bias can lead to missing potentially biologically relevant pairs of genes that are lowly expressed, such as transcription factors. To address this problem, we introduce spatial quantile normalization (SpQN), a method for normalizing local distributions in a correlation matrix. We show that spatial quantile normalization removes the mean-correlation relationship and corrects the expression bias in network reconstruction.
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