Statistical Rigor in Genomics Data Science

Explore statistical rigor in genomics data science through this 44-minute conference talk by Jingyi Jessica Li at the Computational Genomics Summer Institute (CGSI). Delve into critical topics such as differential expression analysis, pseudotime inference, false discovery rate control, and single-cell gene expression simulation. Examine the challenges of false positives in popular differential expression methods when analyzing human population samples. Learn about innovative approaches like PseudotimeDE for well-calibrated p-values in single-cell RNA sequencing data and Clipper for p-value-free FDR control. Discover the capabilities of scDesign2 in generating high-fidelity single-cell gene expression count data with preserved gene correlations. Gain insights into the distinctions between statistical hypothesis testing and machine learning binary classification in genomics research.

Jingyi Jessica Li | Statistical Rigor in Genomics Data Science