The curses of performing differential expression analysis using single-cell data
Wu, C.-H.; Zhou, X.; Chen, M.
Show 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.
Matching journals
The top 4 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
- A statistical framework for differential pseudotime analysis with multiple single-cell RNA-seq samples 96%
- On the discovery of population-specific state transitions from multi-sample multi-condition single-cell RNA sequencing data 96%
- A Universal Deep Neural Network for In-Depth Cleaning of Single-Cell RNA-Seq Data 96%
Similar papers in this journal
- MarcoPolo: a clustering-free approach to the exploration of differentially expressed genes along with group information in single-cell RNA-seq data 95%
- Disentangling single-cell omics representation with a power spectral density-based feature extraction 95%
- Flexible comparison of batch correction methods for single-cell RNA-seq using BatchBench 95%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.