Deconvolution of bulk RNA-seq reveals cell-type specificity mechanism in Alzheimer’s disease

Date:

Tue, 15 Aug 2023

Time:

11 am-12pm [Singapore [GMT +8]

Location:

Tutorial Room 2, Level 9
Tahir Foundation Building (MD1)
National University of Singapore
12 Science Drive 2, Singapore 117549

Details:

You are cordially invited to the ON-SITE SSHSPH Staff Research Round. We look forward to your attendance and we hope for an interesting discussion.

Refreshments will be provided at 10:30am, do drop by to hang out for a bit before the session starts.

We will also be having a Lucky Draw at the end of the session, register yourself and stand a chance to win a special prize!
*Only Attendees will be included in the Lucky draw*

Abstract:

Bulk tissue transcriptomic profiles cannot reflect the functional heterogeneity across cell types. We here propose a new empirical Bayes method (EPIC-unmix), that integrates sc/snRNA-seq reference and bulk RNA-seq data from target samples to enhance cell-type-specific (CTS) expression inference in target samples. We applied EPIC-unmix to deconvolute ROSMAP bulk RNA-seq data from prefrontal cortex samples. Downstream analysis of CTS gene expression, including identification of CTS differentially expressed (DE) genes, CTS eQTL analysis and functional annotations in the corresponding cell type for DE genes, suggested IKZF1 as a risk gene for Alzheimer’s disease functioning in microglia.

Speakers:

Dr Yun Li
Professor, Genetics and Biostatistics, University of North Carolina, Chapel Hill, USA

Yun Li, PhD is a professor of Genetics and Biostatistics at the University of North Carolina, Chapel Hill. Dr. Li is a statistical geneticist with extensive experiences with method development and application on genotype imputation (developer of MaCH and MaCH-admix), genetic studies of recently admixed population, design and analysis of sequencing-based studies, analyses of multi-omics data including mRNA expression, DNA methylation, chromatin 3D organization, and imaging genetics. Dr. Li has been playing an active role in genetic studies of complex human traits resulting many GWAS and meta-analysis publications, including >30 in Nature, Science, Cell, and Nature Genetics. Dr. Li has been leading multiple NIH projects on statistical method development for complex trait genetics. Dr. Li has also been the Director for the Data Science Core of IDDRC (Intellectual and Developmental Disabilities Research Center). Dr. Li has received many awards and became the Thomson Reuters Highly Cited Researcher due to her high impact scientific work. Specifically, her work has been cited >100,000 times with h-index of 88 and i10-index of 187.