Multiomics and Ecological Spatial Analysis for Quantitative Decoding of Cellular Neighborhoods and Tissue Compartments
Project description
MESA: Multiomics and Ecological Spatial Analysis for Quantitative Decoding of Tissue Disease States
MESA is a novel pipeline that brings ecological principles together with multiomics data integration thereby enabling deeper and more quantitative decoding of the functional and spatial shifts of tissue remodeling across disease states.
- Drawing inspiration from ecological studies, MESA adapts diversity metrics traditionally used to gauge biodiversity for spatial omics data, creating tools for systematic quantification of cellular diversity. Specifically, we introduce a multi-scale diversity index, alongside global and local diversity indices, to capture not only the overarching diversity of a tissue but also the localized patterns and dependencies.
- Furthermore, MESA employs a multi-omics approach to spatial omics analyses. MESA in silico amalgamates cross-modality single-cell data to enrich the context of spatial omics observations. With the additional layers of information brought to bear by multiomics, MESA facilitates an extended view of cellular neighborhoods and their spatial interactions within tissue microenvironments. MESA's approach, incorporating differential expression, gene set enrichment, and ligand-receptor interaction analyses within these spatially defined cellular assemblies, further enhances a mechanistic understanding of tissue remodeling across disease states.
Installation
MESA is hosted on pypi
and can be installed via pip
.
pip install mesa-py
Visit our documentation to see examples and tutorials!
License
MESA
is under the Academic Software License Agreement, please use accordingly.
Project details
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