/spruceTopic

Single-cell Pairwise Relationships Untangled by Composite Topic models

Primary LanguagePythonMIT LicenseMIT

SPRUCE: Single-cell Pairwise Relationships Untangled by Composite Embedding model

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This is a project repository for our paper-

Summary

In multi-cellular organisms, cell identity and functions are primed and refined through interactions with other surrounding cells. Here, we propose a scalable machine learning method, termed SPURCE, which is designed to systematically ascertain common cell-cell communication patterns embedded in single-cell RNA-seq data. We applied our approach to investigate tumour microenvironments consolidating multiple breast cancer data sets and found seven frequently-observed interaction signatures and underlying gene-gene interaction networks. Our results implicate that a part of tumour heterogeneity, especially within the same subtype, is better understood by differential interaction patterns rather than the static expression of known marker genes.

Prerequisites

  • python - numpy, pandas, scipy, sklearn, annoy, pytorch, igraph, seaborn
  • R - celldex, SingleR, SingleCellExperiment, ggplot2, pheatmap, circlize, bipartite

Dataset

Installation

  • Clone the repo
    git clone https://github.com/causalpathlab/spruceTopic.git