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ClusterDE: A Statistical Software Package for Removing Double-Dipping Bias in Post-Clustering Differential Expression
Christy Lee1, Dongyuan Song2, Siqi Chen1
1Department of Statistics and Data Science, University of California, Los Angeles, California, USA.
Summary
The ClusterDE R package prevents false marker genes in single-cell and spatial transcriptomics. It uses synthetic null data to identify and remove spurious differential expression results caused by over-clustering.
Area of Science:
- Single-cell and spatial transcriptomics
- Computational biology
- Bioinformatics
Background:
- Standard transcriptomics analysis involves clustering cells or spatial spots, followed by differential expression (DE) analysis to identify marker genes.
- Using the same dataset for both clustering and DE analysis, known as double-dipping, can lead to the spurious detection of DE genes.
- Over-clustering can generate artificial clusters, leading to misinterpretation of cell types or spatial domains.
