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Accurate cell type annotation for single-cell chromatin accessibility data via contrastive learning and reference guidance

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Quantitative Biology 2024年第1期12卷 85-99页

作者： Siyu Li Songming Tang Yunchang Wang Sijie Li Yuhang Jia Shengquan Chen School of Statistics and Data Science Nankai UniversityTianjinChina School of Mathematical Sciences and LPMC Nankai UniversityTianjinChina

Recent advances in single-cell chromatin accessibility sequencing(scCAS)technologies have resulted in new insights into the characterization of epigenomic heterogeneity and have increased the need for automatic cell type ***,existing automatic annotation methods for scCAS data fail to incorporate the reference data and neglect novel cell types,which only exist in a test ***,we propose RAINBOW,a reference-guided automatic annotation method based on the contrastive learning framework,which is capable of effectively identifying novel cell types in a test *** utilizing contrastive learning and incorporating reference data,RAINBOW can effectively characterize the heterogeneity of cell types,thereby facilitating more accurate *** extensive experiments on multiple scCAS datasets,we show the advantages of RAINBOW over state-of-the-art methods in known and novel cell type *** also verify the effectiveness of incorporating reference data during the training *** addition,we demonstrate the robustness of RAINBOW to data sparsity and number of cell ***,RAINBOW provides superior performance in newly sequenced data and can reveal biological implication in downstream *** the results demonstrate the superior performance of RAINBOW in cell type annotation for scCAS *** anticipate that RAINBOW will offer essential guidance and great assistance in scCAS data *** source codes are available at the GitHub website(BioX-NKU/RAINBOW).

关键词： cell type annotation chromatin accessibility novel type reference-guided single-cell

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deCS:A Tool for Systematic cell type annotations of Single-cell RNA Sequencing Data among Human Tissues

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Genomics, Proteomics & Bioinformatics 2023年第2期21卷 370-384页

作者： Guangsheng Pei Fangfang Yan Lukas M.Simon Yulin Dai Peilin Jia Zhongming Zhao Center for Precision Health School of Biomedical InformaticsThe University of Texas Health Science Center at HoustonHoustonTX 77030USA Therapeutic Innovation Center Baylor College of MedicineHoustonTX 77030USA Human Genetics Center School of Public HealthThe University of Texas Health Science Center at HoustonHoustonTX 77030USA MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences HoustonTX 77030USA Department of Biomedical Informatics Vanderbilt University Medical CenterNashvilleTN 37203USA CAS Key Laboratory of Genomic and Precision Medicine Beijing Institute of GenomicsChinese Academy of Sciences and China National Center for BioinformationBeijing 100101China

Single-cell RNA sequencing(scRNA-seq)is revolutionizing the study of complex and dynamic cellular ***,cell type annotation remains a main challenge as it largely relies on a priori knowledge and manual curation,which is cumbersome and *** increasing number of scRNA-seq datasets,as well as numerous published genetic studies,has motivated us to build a comprehensive human cell type reference ***,we present decoding cell type Specificity(deCS),an automatic cell type annotation method augmented by a comprehensive collection of human cell type expression profiles and marker *** used deCS to annotate scRNAseq data from various tissue types and systematically evaluated the annotation accuracy under different conditions,including reference panels,sequencing depth,and feature selection *** results demonstrate that expanding the references is critical for improving annotation *** to many existing state-of-the-art annotation tools,deCS significantly reduced computation time and increased *** can be integrated into the standard scRNA-seq analytical pipeline to enhance cell type ***,we demonstrated the broad utility of deCS to identify trait-cell type associations in 51 human complex traits,providing deep insights into the cellular mechanisms underlying disease pathogenesis.

关键词： cell type-specific enrichment analysis scRNA-seq cell type annotation Trait-cell type association cell typemarkergene

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