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科博尔特:多模态单细胞测序数据的综合分析。

Cobolt: integrative analysis of multimodal single-cell sequencing data.

机构信息

Division of Biostatistics, University of California, Berkeley, Berkeley, CA, USA.

Department of Statistics, University of California, Berkeley, Berkeley, CA, USA.

出版信息

Genome Biol. 2021 Dec 28;22(1):351. doi: 10.1186/s13059-021-02556-z.

Abstract

A growing number of single-cell sequencing platforms enable joint profiling of multiple omics from the same cells. We present Cobolt, a novel method that not only allows for analyzing the data from joint-modality platforms, but provides a coherent framework for the integration of multiple datasets measured on different modalities. We demonstrate its performance on multi-modality data of gene expression and chromatin accessibility and illustrate the integration abilities of Cobolt by jointly analyzing this multi-modality data with single-cell RNA-seq and ATAC-seq datasets.

摘要

越来越多的单细胞测序平台能够从同一细胞中联合分析多个组学。我们提出了 Cobolt 方法,它不仅允许分析联合模式平台的数据,而且为整合不同模式下测量的多个数据集提供了一个连贯的框架。我们在基因表达和染色质可及性的多模式数据上展示了其性能,并通过联合分析单细胞 RNA-seq 和 ATAC-seq 数据集来展示 Cobolt 的整合能力。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ba2b/8715620/d932ef5e4ece/13059_2021_2556_Fig1_HTML.jpg

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