Published May 3, 2022
| Version v3
Dataset
Open
Datasets, reproducible codes, and results for evaluating differential expression analysis methods on population-level RNA-seq data
- 1. University of Bordeaux, INSERM Bordeaux Population Health, INRIA SISTM, Vaccine Research Institute
- 2. RAND Corporation, Harvard Medical Schoo
Description
This upload contains the necessary R codes and data to reproduce the FDR and Power results described in our correspondence "Neglecting normalization impact in semi-synthetic RNA-seq data simulation generates artificial false positives" to Li Y, Ge X, Peng F, Li W, Li JJ, Exaggerated false positives by popular differential expression methods when analyzing human population samples, Genome Biology 23, 79, 2022, DOI: 10.1186/s13059-022-02648-4.
Files
Correspondence_LiEtAl_GenomeBiology_ExageratedFDR.zip
Files
(35.6 MB)
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md5:a31307af26ff017bf33a2a0d4d925562
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Additional details
Related works
- Cites
- Journal article: 10.1186/s13059-022-02648-4 (DOI)
- Is derived from
- Dataset: 10.5281/zenodo.6326786 (DOI)
References
- Li Y, Ge X, Peng F, Li W, Li JJ. Exaggerated false positives by popular differential expression methods when analyzing human population samples. Genome Biology 23, 79, 2022. DOI: 10.1186/s13059-022-02648-4
- Li Y, Ge X. Processed datasets for differential expression analysis on polulation-level RNA-seq data (Version 4) [Data set]. Zenodo. 2022. DOI: 10.5281/zenodo.6326786.