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.

• 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.