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• W3045794610 abstract "Abstract With the rapid advancement of single-cell RNA-seq (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 networks 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 impact on inferred gene-gene associations. Our results suggested that a considerable amount of spurious correlations was introduced during the data preprocessing steps due to over-smoothing 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 gene co-expression network and successfully revealed several known immune cell modules." @default.
• W3045794610 created "2020-08-03" @default.
• W3045794610 creator A5034697076 @default.
• W3045794610 creator A5046789479 @default.
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• W3045794610 date "2020-07-30" @default.
• W3045794610 modified "2023-09-27" @default.
• W3045794610 title "Noise regularization removes correlation artifacts in single-cell RNA-seq data preprocessing" @default.
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• W3045794610 doi "https://doi.org/10.1101/2020.07.29.227546" @default.
• W3045794610 hasPublicationYear "2020" @default.
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