Lotfollahi-lab/mintflow
Generation of disentangled microenvironment-induced and intrinsic gene expression vectors from spatial transcriptomics data
This tool helps biologists and medical researchers understand complex spatial transcriptomics data. It takes raw spatial gene expression data and separates it into two components: gene activity driven by the cell's local environment and gene activity inherent to the cell itself. This disentanglement provides clearer insights into cell states and microenvironment influences within tissues.
Available on PyPI.
Use this if you need to precisely distinguish between intrinsic cell properties and environmental impacts on gene expression from your spatial transcriptomics experiments.
Not ideal if you are working with single-cell RNA sequencing data without spatial context, or if your primary goal is general clustering without disentangling environmental factors.
Stars
26
Forks
4
Language
Python
License
BSD-3-Clause
Category
Last pushed
Mar 11, 2026
Commits (30d)
0
Dependencies
18
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