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ScMC learns biological variation through the alignment of multiple single-cell genomics datasets.

ABSTRACT: Distinguishing biological from technical variation is crucial when integrating and comparing single-cell genomics datasets across different experiments. Existing methods lack the capability in explicitly distinguishing these two variations, often leading to the removal of both variations. Here, we present an integration method scMC to remove the technical variation while preserving the intrinsic biological variation. scMC learns biological variation via variance analysis to subtract technical variation inferred in an unsupervised manner. Application of scMC to both simulated and real datasets from single-cell RNA-seq and ATAC-seq experiments demonstrates its capability of detecting context-shared and context-specific biological signals via accurate alignment.

SUBMITTER: Zhang L

PROVIDER: S-EPMC7784288 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature

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scMC learns biological variation through the alignment of multiple single-cell genomics datasets.

Zhang Lihua L Nie Qing Q

Genome biology 20210104 1

Distinguishing biological from technical variation is crucial when integrating and comparing single-cell genomics datasets across different experiments. Existing methods lack the capability in explicitly distinguishing these two variations, often leading to the removal of both variations. Here, we present an integration method scMC to remove the technical variation while preserving the intrinsic biological variation. scMC learns biological variation via variance analysis to subtract technical va ...[more]

PMID: 33397454

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