SMS scnews item created by Dario Strbenac at Thu 28 Oct 2021 1500
Type: Seminar
Modified: Thu 28 Oct 2021 1500
Distribution: World
Expiry: 30 Nov 2021
Calendar1: 1 Nov 2021 1300-1400
CalLoc1: Zoom videoconferencing
Auth: dario@ (dstr7320) in SMS-SAML

Statistical Bioinformatics Webinar: Jin -- Non-negative Matrix Factorisation and Networks Analysis for Bioinformatics

Presented by Associate Professor Suoqin Jin, School of Mathematics and Statistics (Wuhan
University, China) 

Title: Dissecting Cellular Heterogeneity and Communication via Integration of
Single-cell Genomics Data 

Abstract: Recent advances of single-cell technologies, in particular single-cell RNA and
ATAC sequencing, provide an unprecedented opportunity to dissect cellular heterogeneity
and communication more comprehensively.  To deconvolute heterogeneous single cells from
both transcriptomic and epigenomic profiles, we developed a matrix factorization-based
method, scAI, for integrating single cell RNA-seq data and ATAC-seq or DNA methylation
data obtained from the same individual cells.  To address the extremely sparse and
binary nature of the epigenomic data, scAI aggregates sparse epigenomic signals in
similar cells learned in an unsupervised manner, allowing coherent fusion with
transcriptomic measurements.  Simulation studies and applications to real datasets
demonstrate its capability of dissecting cellular heterogeneity within both
transcriptomic and epigenomic layers and understanding transcriptional regulatory
mechanisms.  In addition, single cell RNA-seq data also offers a great opportunity for
probing underlying intercellular communications that often drive heterogeneity and cell
state transitions in tissues.  We developed an integrated method CellChat for systematic
inference and quantitative analysis of cell-cell communication by integrating scRNA-seq
data and prior knowledge of the interactions between signaling molecules.  I will show
how we can quantitatively build and analyze cell-cell communication networks in an
easily interpretable way by applying systems biology and machine learning approaches.
Applying CellChat to real datasets shows its ability to extract complex signaling
patterns.  Our versatile and easy-to-use toolkit CellChat will help discover novel
intercellular communications and build cell-cell communication atlases in diverse

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