Statistical Bioinformatics Seminar: Dr Ying Ma

Title: Statistical and computational methods for spatial transcriptomics data analysis 

Speaker: Dr Ying Ma (Brown University) 

Abstract: Spatial transcriptomics technologies have enabled gene expression profiling on
complex tissues with spatial localization information.  The majority of these
technologies, however, effectively measure the average gene expression from a mixture of
cells of potentially heterogeneous cell types on each tissue location.  Here, I develop
a deconvolution method, CARD, that combines cell-type-specific expression information
from single-cell RNA sequencing (scRNA-seq) with correlation in cell-type composition
across tissue locations.  Modeling spatial correlation allows us to borrow the cell-type
composition information across locations, improving accuracy of deconvolution even with
a mismatched scRNA-seq reference.  CARD can also impute cell-type compositions and gene
expression levels at unmeasured tissue locations to enable the construction of a refined
spatial tissue map with a resolution arbitrarily higher than that measured in the
original study and can perform deconvolution without a scRNA-seq reference.  In a real
data application on the human pancreatic ductal adenocarcinoma (PDAC) dataset, CARD
identified multiple cell types and molecular markers with distinct spatial localization
that define the progression, heterogeneity, and compartmentalization of pancreatic
cancer.  In addition, if time allows, I will also discuss my other methodological work
on integrative differential expression and gene set enrichment analysis in scRNA-seq
studies, integrative reference-informed tissue segmentation in SRT studies, and
collaborative work on polygenic risk scores for common health-related exposure traits in
the Michigan Genomics Initiative (MGI) cohort.  

About the speaker: Dr Ying Ma is an Assistant Professor at the Department of
Biostatistics and the Center for Computational Molecular Biology at Brown University.
Her research interests focus on developing efficient statistical learning methods to
address a variety of biological problems and computational challenges in genomics and
genetics.  These challenges typically arise with the high-dimensional data generated by
rapidly evolving sequencing technologies, e.g., single-cell RNA-seq (scRNA-seq), and
spatially resolved transcriptomics (SRT).  With the emergence of these large-scale data,
she has been continually motivated to develop tailored statistical models to advance our
understanding in cellular heterogeneity, tissue organization, and the underlying
mechanisms of various types of cancers.  Besides her genomics research, she also works
on genetic risk prediction and polygenic risk score problems in large biobanks such as
UKBiobank, and MGI.