/gssnng

Gene Set Scoring on the Nearest Neighbor Graph (gssnng) for Single Cell RNA-seq (scRNA-seq) using AnnData h5ad files.

Primary LanguageJupyter NotebookMIT LicenseMIT

gssnng

Gene Set Scoring on the Nearest Neighbor Graph (gssnng) for Single Cell RNA-seq (scRNA-seq).

This package is part of the scverse ecosystem and works with Scanpy AnnData objects stored as h5ad files.

  • Read the Docs!

  • Notebook using gmt files ===>>> Open In Colab

  • Notebook using Decoupler/Omnipath style API ===>>> Open In Colab

  • **Notebook for smoothing counts. COMING SOON! For now, see the example script in test.

  • See the paper ===>>> gssnng

The GSSNNG method is based on using the nearest neighbor graph of cells for data smoothing. This essentially creates mini-pseudobulk expression profiles for each cell, which can be scored by using single sample gene set scoring methods often associated with bulk RNA-seq.

Nearest neighbor graphs (NNG) are constructed based on user defined groups (see the 'groupby' parameter below). The defined groups can be processed in parallel, speeding up the calculations. For example, a NNG could be constructed within each cluster or jointly by cluster and sample. Smoothing can be performed using either the adjacency matrix (all 1s) or the weighted graph to give less weight to more distant cells.