grst / visionpy

Functional interpretation of single cell similarity maps

Functional Interpretation for
scRNA-seq Data

NOTE: THIS PACKAGE IS UNDER ACTIVE DEVELOPMENT AND IS NOT YET READY FOR USE.

This is a Python port of the VISION R package. VISION aids in the interpretation of single-cell RNA-seq (scRNA-seq) data by selecting for gene signatures which describe coordinated variation between cells. While the software only requires an expression matrix and a signature library (available in online databases), it is also designed to integrate into existing scRNA-seq analysis pipelines by taking advantage of precomputed dimensionality reductions, trajectory inferences or clustering results. The results of this analysis are made available through a dynamic web-app which can be shared with collaborators without requiring them to install any additional software.

• Nature Communications publication
• Full R Documentation
• R package

Installing visionpy

You need to have Python 3.8 or newer installed on your system. If you don't have Python installed, we recommend installing Miniconda.

There are several alternative options to install visionpy:

1. Install the latest release on PyPI:

pip install visionpy-sc

2. Install the latest development version:

pip install git+https://github.com/yoseflab/visionpy.git@main

How to run visionpy

From the command line

visionpy --adata ./my_adata.h5ad --norm_data_key use_raw --compute_neighbors_on_key X_scvi --name Test Vision

From Python

from visionpy.api import start_vision
from visionpy import signatures_from_gmt

adata.varm["signatures"] = signatures_from_gmt(["./signatures.gmt"], adata)
start_vision(
    adata=adata,
    name="Test Session",
    norm_data_key="log1pcp10k",
    compute_neighbors_on_key="X_pca",
    signature_varm_key="signatures",
)