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Demystifying "drop-outs" in single-cell UMI data.

ABSTRACT: Many existing pipelines for scRNA-seq data apply pre-processing steps such as normalization or imputation to account for excessive zeros or "drop-outs." Here, we extensively analyze diverse UMI data sets to show that clustering should be the foremost step of the workflow. We observe that most drop-outs disappear once cell-type heterogeneity is resolved, while imputing or normalizing heterogeneous data can introduce unwanted noise. We propose a novel framework HIPPO (Heterogeneity-Inspired Pre-Processing tOol) that leverages zero proportions to explain cellular heterogeneity and integrates feature selection with iterative clustering. HIPPO leads to downstream analysis with greater flexibility and interpretability compared to alternatives.

SUBMITTER: Kim TH

PROVIDER: S-EPMC7412673 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature

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Demystifying "drop-outs" in single-cell UMI data.

Kim Tae Hyun TH Zhou Xiang X Chen Mengjie M

Genome biology 20200806 1

Many existing pipelines for scRNA-seq data apply pre-processing steps such as normalization or imputation to account for excessive zeros or "drop-outs." Here, we extensively analyze diverse UMI data sets to show that clustering should be the foremost step of the workflow. We observe that most drop-outs disappear once cell-type heterogeneity is resolved, while imputing or normalizing heterogeneous data can introduce unwanted noise. We propose a novel framework HIPPO (Heterogeneity-Inspired Pre-Pr ...[more]

PMID: 32762710

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