RT Journal Article
SR Electronic
T1 Consensus tissue domain detection in spatial multi-omics data using MILWRM
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2023.02.02.526900
DO 10.1101/2023.02.02.526900
A1 Harsimran Kaur
A1 Cody N. Heiser
A1 Eliot T. McKinley
A1 Lissa Ventura-Antunes
A1 Coleman R. Harris
A1 Joseph T. Roland
A1 Martha J. Shrubsole
A1 Robert J. Coffey
A1 Ken S. Lau
A1 Simon Vandekar
YR 2023
UL http://biorxiv.org/content/early/2023/02/03/2023.02.02.526900.abstract
AB Spatially resolved molecular assays provide high dimensional genetic, transcriptomic, proteomic, and epigenetic information in situ and at various resolutions. Pairing these data across modalities with histological features enables powerful studies of tissue pathology in the context of an intact microenvironment and tissue structure. Increasing dimensions across molecular analytes and samples require new data science approaches to functionally annotate spatially resolved molecular data. A specific challenge is data-driven cross-sample domain detection that allows for analysis within and between consensus tissue compartments across high volumes of multiplex datasets stemming from tissue atlasing efforts. Here, we present MILWRM – multiplex image labeling with regional morphology – a Python package for rapid, multi-scale tissue domain detection and annotation. We demonstrate MILWRM’s utility in identifying histologically distinct compartments in human colonic polyps and mouse brain slices through spatially-informed clustering in two different spatial data modalities. Additionally, we used tissue domains detected in human colonic polyps to elucidate molecular distinction between polyp subtypes. We also explored the ability of MILWRM to identify anatomical regions of mouse brain and their respective distinct molecular profiles.Competing Interest StatementThe authors have declared no competing interest.