Julia package for
Wang, L., Lin, Y., & Zhao, H. (2024). False Discovery Rate Control via Data Splitting for Testing-after-Clustering (arXiv:2410.06451). arXiv. https://doi.org/10.48550/arXiv.2410.06451
The proposed approach addresses the double-dipping issue in testing-after-clustering tasks, particularly in single-cell data analysis, where the same data is used both for clustering (to identify cell types) and for testing (to select differentially expressed genes), which can inflate false positives.
The xkcd-style cartoon is drawn with the help of R package xkcd
- R package: https://github.com/szcf-weiya/SplitClusterTest
- For the comparison between data splitting and data fission, check https://github.com/szcf-weiya/fission_vs_splitting.