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

Area of Science:

• Bioinformatics
• Computational Biology
• Genomics

Background:

• Single-cell RNA sequencing (scRNA-seq) data often exhibit high dropout rates, necessitating pre-processing steps like normalization and imputation.
• Existing workflows typically apply these pre-processing steps before clustering, potentially introducing noise into heterogeneous datasets.

Purpose of the Study:

• To challenge the conventional workflow by proposing clustering as the primary step in scRNA-seq data analysis.
• To introduce a novel framework, HIPPO, for effective pre-processing guided by cellular heterogeneity.

Main Methods:

• Analysis of diverse UMI-based scRNA-seq datasets to identify patterns in zero proportions.
• Development of HIPPO (Heterogeneity-Inspired Pre-Processing tOol), integrating feature selection with iterative clustering based on zero proportions.
• Comparative analysis of HIPPO against traditional pre-processing methods.

Main Results:

• Clustering as the initial step effectively resolves cell-type heterogeneity, significantly reducing dropout artifacts.
• Pre-processing heterogeneous data via imputation or normalization can introduce artificial noise, confounding downstream analyses.
• HIPPO demonstrates superior performance in handling zero proportions and identifying cellular heterogeneity.

Conclusions:

• Reordering the scRNA-seq workflow to prioritize clustering over initial normalization or imputation is crucial for accurate analysis.
• HIPPO provides a robust and flexible framework for scRNA-seq data pre-processing, enhancing downstream analysis interpretability.
• Leveraging zero-proportion data to guide clustering offers a powerful strategy for understanding cellular heterogeneity.