W30: Statistical Methods for Enhancing the Rigor in Single-cell RNA-seq Data Analysis

In this workshop, we will go over four R/Python packages designed to help biologists in the statistical analysis of their RNA-seq data:

• scDesign2, with Tianyi Sun. scDesign2 is a transparent simulator that generates high-fidelity single-cell gene expression count data with gene correlations captured. Due to its ability to generate synthetic data of different sequencing depths and cell numbers, it can be used to guide single-cell gene expression experimental design and benchmark computational methods. Learn more in Sun et al. 2021, Genome Biology.
• scReadSim, with Guan’ao Yan. scReadSim is a single cell multimodal omics data simulator that generates synthetic reads directly in BAM formats and could help benchmark low-level bioinformatic computation tools with raw reads as an input.
• PseudotimeDE, with Dongyuan Song. We proposed PseudotimeDE, a differentially expressed (DE) gene identification method that adapts to various pseudotime inference methods, accounts for pseudotime inference uncertainty, and outputs well-calibrated p-values. Learn more in Song et al. 2021, Genome Biology. 
• scPNMF, with Kexin Li. scPNMF is an unsupervised gene selection and data projection method to guide single-cell targeted gene profiling experimental design. It enjoys interpretability, selects informative genes that can better distinguish cell types, and enables new data alignment. Learn more in Song et al. 2021, Bioinformatics.

This workshop is addressed to computational biologists interested in RNA-seq data and  ATAC-seq data analysis.