Computational Spatial -omics Lab

Single-cell and Spatial understanding of the tissue structure and immune context of disease.

Join Us

Research Projects

From the sequencing of the Human genome in 2004, high-throughput sequencing has revolutionized biology and biomedical research. New technologies enable the measurement of gene expression at the level of individual cells and across entire tissue sections. Our group develops the computational tools and pipelines required to analyze and interpret the wealth of new data generated by these technologies. We collaborate extensively with biomedical researchers generating novel data to elucidate the causes and consequences of disease.

Single-cell -omics

Return to Top

Spatial -omics

In multicellular organisms, the location of a cell within the organism or within a specific tissue can determine its function. Gradients in signalling molecules, nutrients, and even physical forces contribute to determining the complex organization of cells and structures within organ tissues. Novel technologies are enabling the measurement of DNA, RNA, and proteins within their native tissue context. However, analyzing these data currently involves indepth statistical analysis of the molecular data which is then layed on top of imaging data. Tissue structures from images if considered at all are typically manually annotated by expert pathologists. We will be developing a suite of machine learning tools to enable the joint analysis of molecular and imaging data.

Return to Top

Collaborations

Return to Top

Publications

Full Publication List

Single-Cell, Single-Nucleus, and Spatial RNA Sequencing of the Human Liver Identifies Cholangiocyte and Mesenchymal Heterogeneity (2021) TS Andrews, J Atif, JC Liu, CT Perciani, XZ Ma, C Thoeni, M Slyper, ... Hepatology Communications

Tutorial: guidelines for annotating single-cell transcriptomic maps using automated and manual methods (2021) ZA Clarke, TS Andrews, J Atif, D Pouyabahar, BT Innes, SA MacParland, ... Nature protocols 16 (6), 2749-2764

Tutorial: guidelines for the computational analysis of single-cell RNA sequencing data (2021) TS Andrews, VY Kiselev, D McCarthy, M Hemberg Nature protocols 16 (1), 1-9

EmptyDrops: distinguishing cells from empty droplets in droplet-based single-cell RNA sequencing data (2019) ATL Lun, S Riesenfeld, T Andrews, T Gomes, JC Marioni Genome biology 20 (1), 1-9

The Malaria Cell Atlas: Single parasite transcriptomes across the complete Plasmodium life cycle (2019) VM Howick, AJC Russell, T Andrews, H Heaton, AJ Reid, K Natarajan, ... Science 365 (6455), eaaw2619

M3Drop: dropout-based feature selection for scRNASeq (2019) TS Andrews, M Hemberg Bioinformatics 35 (16), 2865-2867

Statistical Methods for Single-Cell RNA-Sequencing (2019) TS Andrews, V Yu. Kiselev, M Hemberg Handbook of Statistical Genomics: Two Volume Set, 735-20
Single-cell transcriptomics reveals a new dynamical function of transcription factors during embryonic hematopoiesis (2018) I Bergiers, T Andrews, ÖV Bölükbasi, A Buness, E Janosz, ... Elife 7, e29312

False signals induced by single-cell imputation (2018) TS Andrews, M Hemberg F1000Research 7

The clustering of functionally related genes contributes to CNV-mediated disease (2015) T Andrews, F Honti, R Pfundt, N De Leeuw, J Hehir-Kwa, ... Genome research 25 (6), 802-813

Gene networks underlying convergent and pleiotropic phenotypes in a large and systematically-phenotyped cohort with heterogeneous developmental disorders (2015) T Andrews, S Meader, A Vulto-van Silfhout, A Taylor, J Steinberg, ... PLoS genetics 11 (3), e1005012

Return to Top

People

Tallulah Andrews, DPhil

Dr. Tallulah Andrews is an Assistant Professor in the Department of Biochemistry at the University of Western Ontario. Her group focuses on the integration of biological imaging and multiple -omics technologies to understand the structure of diseased tissues. She is a long-term member of the Human Cell Atlas developing computational tools for single-cell RNAseq data while a post-doc at the Wellcome Sanger Institute in Cambridge, UK and analyzing the Healthy Liver atlas in the MacParland group at UHN Research. She holds a PhD from the University of Oxford where she used systems biology approaches to identify biological pathways underlying rare genetic diseases.

Alex Tse

Alex is a research assistant for the team. He has previously worked as a data analyst for BMO finical group and the AIA group, responsible for data mapping and writing programs to manage data. Alex is currently studying in University of Waterloo, pursing his bachelor of computer science.

Return to Top