SMS scnews item created by Dario Strbenac at Tue 10 Aug 2021 1010
Type: Seminar
Distribution: World
Expiry: 31 Aug 2021
Calendar1: 16 Aug 2021 1300-1400
CalLoc1: Zoom videoconferencing
Auth: dario@ (dstr7320) in SMS-SAML

Special Event: Statistical Bioinformatics Webinar by Associate Professor Jingyi Li: Li -- Applications of Generalized Additive Models (GAMs) and Copulas to Single-cell RNA-seq

Presented by Assoc.  Prof.  Jessica Jingyi Li (University of California, Los Angeles) 

Part 1: PseudotimeDE: Inference of differential gene expression along cell pseudotime
with well-calibrated p-values from single-cell RNA sequencing data.  To investigate
molecular mechanisms underlying cell state changes, a crucial analysis is to identify
differentially expressed (DE) genes along the pseudotime inferred from single-cell
RNA-sequencing data.  However, existing methods do not account for pseudotime inference
uncertainty, and they have either ill-posed p-values or restrictive models.  Here we
propose PseudotimeDE, a DE gene identification method that adapts to various pseudotime
inference methods, accounts for pseudotime inference uncertainty, and outputs
well-calibrated p-values.  Comprehensive simulations and real-data applications verify
that PseudotimeDE outperforms existing methods in false discovery rate control and

Part 2: scDesign2: A transparent simulator that generates high-fidelity single-cell gene
expression count data with gene correlations captured.  A pressing challenge in
single-cell transcriptomics is to benchmark experimental protocols and computational
methods.  A solution is to use computational simulators, but existing simulators cannot
simultaneously achieve three goals: preserving genes, capturing gene correlations, and
generating any number of cells with varying sequencing depths.  To fill this gap, we
propose scDesign2, a transparent simulator that achieves all three goals and generates
high-fidelity synthetic data for multiple single-cell gene expression count-based
technologies.  In particular, scDesign2 is advantageous in its transparent use of
probabilistic models and its ability to capture gene correlations via copulas.

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