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• W4313237958 abstract "Single-cell ribonucleic acid (RNA) sequencing (scRNA-seq) has been a powerful technology for transcriptome analysis. However, the systematic validation of diverse computational tools used in scRNA-seq analysis remains challenging. Here, we propose a novel simulation tool, termed as Simulation of Cellular Heterogeneity (SimCH), for the flexible and comprehensive assessment of scRNA-seq computational methods. The Gaussian Copula framework is recruited to retain gene coexpression of experimental data shown to be associated with cellular heterogeneity. The synthetic count matrices generated by suitable SimCH modes closely match experimental data originating from either homogeneous or heterogeneous cell populations and either unique molecular identifier (UMI)-based or non-UMI-based techniques. We demonstrate how SimCH can benchmark several types of computational methods, including cell clustering, discovery of differentially expressed genes, trajectory inference, batch correction and imputation. Moreover, we show how SimCH can be used to conduct power evaluation of cell clustering methods. Given these merits, we believe that SimCH can accelerate single-cell research." @default.
• W4313237958 created "2023-01-06" @default.
• W4313237958 creator A5009346772 @default.
• W4313237958 creator A5041630062 @default.
• W4313237958 creator A5090043165 @default.
• W4313237958 date "2022-12-27" @default.
• W4313237958 modified "2023-10-14" @default.
• W4313237958 title "SimCH: simulation of single-cell RNA sequencing data by modeling cellular heterogeneity at gene expression level" @default.
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• W4313237958 doi "https://doi.org/10.1093/bib/bbac590" @default.
• W4313237958 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/36575569" @default.
• W4313237958 hasPublicationYear "2022" @default.
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