Demystifying "drop-outs" in Single-cell UMI Data

Overview

Journal Genome Biol

Specialties Biology
Genetics

Date 2020 Aug 9

PMID 32762710

Citations 54

Authors

Tae Hyun Kim

Xiang Zhou

Mengjie Chen

Affiliations

Soon will be listed here.

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.

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