Gene Signal Pattern Analysis

Mapping the gene space at single-cell resolution

Gene Signal Pattern Analysis is a Python package for mapping the gene space from single-cell data. For a detailed explanation of GSPA and potential downstream application, see:

Mapping the gene space at single-cell resolution with Gene Signal Pattern Analysis. Aarthi Venkat, Sam Leone, Scott E. Youlten, Eric Fagerberg, John Attanasio, Nikhil S. Joshi, Michael Perlmutter, Smita Krishnaswamy.

By considering gene expression values as signals on the cell-cell graph, GSPA enables complex analyses of gene-gene relationships, including gene cluster analysis, cell-cell communication, and patient manifold learning from gene-gene graphs.

Installation

pip install gspa

Requirements

GSPA requires Python >= 3.6. All other requirements are automatically installed by pip (see also requirements.txt).

The following have been tested: Python 3.6.15 (graphtools 1.5.3, tensorflow 2.6.2, keras 2.6.0, numpy 1.19.5, sklearn 0.24.2, scipy 1.5.4, tqdm 4.64.1, scanpy 1.7.2, phate 1.0.11) and Python 3.8.18 (graphtools 1.5.3, tensorflow 2.13.0, keras 2.13.1, numpy 1.22.4, sklearn 1.3.2, scipy 1.10.1, tqdm 4.66.4, scanpy 1.9.3, phate 1.0.11)

Usage example

import numpy as np
import gspa

# Create toy data
n_cells = 1000
n_genes = 50
data = np.random.normal(size=(n_cells, n_genes))

# GSPA operator constructs wavelet dictionary
gspa_op = gspa.GSPA()
gspa_op.construct_graph(data)
gspa_op.build_diffusion_operator()
gspa_op.build_wavelet_dictionary()

# Embed gene signals from wavelet dictionary
gene_signals = data.T # embed all measured genes
gene_ae, gene_pc = gspa_op.get_gene_embeddings(gene_signals)
gene_localization = gspa_op.calculate_localization()

See GSPA_example.ipynb above for test run on simulated single-cell data. More notebooks to generate paper figures available at https://github.com/KrishnaswamyLab/GSPA-manuscript-analyses.