The curses of performing differential expression analysis using single-cell data

Wu, C.-H.; Zhou, X.; Chen, M.

doi:10.1101/2024.05.28.596315

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bioRxiv · 10.1101/2024.05.28.596315

The curses of performing differential expression analysis using single-cell data

Wu, C.-H.·Zhou, X.·Chen, M.

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Abstract

Differential expression analysis is pivotal in single-cell transcriptomics for unraveling cell-type- specific responses to stimuli. While numerous methods are available to identify differentially expressed genes in single-cell data, recent evaluations of both single-cell-specific methods and methods adapted from bulk studies have revealed significant shortcomings in performance. In this paper, we dissect the four major challenges in single-cell DE analysis: normalization, excessive zeros, donor effects, and cumulative biases. These "curses" underscore the limitations and conceptual pitfalls in existing workflows. In response, we introduce a novel paradigm addressing several of these issues.

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Wu, C.-H., Zhou, X., Chen, M.. 2024-06-02. The curses of performing differential expression analysis using single-cell data. https://doi.org/10.1101/2024.05.28.596315

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