Workshop Description

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.

  • TBD

Technical Requirements

A computer with R Studio and Python.


Jingyi Jessica Li is an Associate Professor in the Department of Statistics (primary) and the Departments of Biostatistics, Computational Medicine, and Human Genetics (secondary) at UCLA. Prior to joining UCLA in 2013, Jessica obtained Ph.D. from UC Berkeley, where she worked with Profs. Peter J. Bickel and Haiyan Huang, and B.S. (summa cum laude) from Tsinghua University, China. At UCLA, Jessica leads the group “Junction of Statistics and Biology” that comprises students from interdisciplinary backgrounds. On the statistical methodology side, her research interests include association measures, asymmetric classification, p-value-free false discovery rate control, and high-dimensional variable selection. On the biomedical application side, her research interests include bulk and single-cell RNA sequencing, comparative genomics, and information flow in the central dogma. Jessica is the recipient of the Alfred P. Sloan Research Fellowship (2018), the Johnson & Johnson WiSTEM2D Math Scholar Award (2018), the NSF CAREER Award (2019), and the MIT Technology Review 35 Innovators Under 35 China (2020).

Workshop Details

Required Prerequisites: Basic knowledge in Statistics. Experience with R and Python is required.
Length: 1 day, 4 hrs
Level: Introductory
Location: Online steam
Seats Available: N/A

Upcoming Dates


May 31, 2022 (9 AM – 11 AM PT and 1:30PM – 3:30PM)