Statistical Bioinformatics Seminar: Jia Li

Speaker: Dr Jia Li (Vanderbilt University Medical Center) 

Abstract: Single-cell and spatial transcriptomics have been widely used to characterize
cellular landscape in complex tissues.  To understand cellular heterogeneity, one
essential step is to define cell types through unsupervised clustering.  While typical
clustering methods have difficulty in identifying rare cell types, approaches
specifically tailored to detect rare cell types gain their ability at the cost of poorer
performance for grouping abundant ones.  Here, we developed aKNNO, a method to identify
abundant and rare cell types simultaneously based on an adaptive k-nearest neighbor
graph with optimization.  Benchmarked on 38 simulated and 20 single-cell and spatial
transcriptomics datasets, aKNNO identified both abundant and rare cell types
accurately.  Without sacrificing performance for clustering abundant cell types, aKNNO
discovered known and novel rare cell types that those typical and even specifically
tailored methods failed to detect.  aKNNO, using transcriptome alone, stereotyped
fine-grained anatomical structures more precisely than those integrative approaches
combining expression with spatial locations and histology image.  

About the speaker: Dr Jia Li is currently a Postdoctoral Fellow in the Department of
Biostatistics in Vanderbilt University Medical Center.  Her research is focused on the
analysis and method development for single cell RNA sequencing and spatial
transcriptomics data.