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.
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:
Install the latest release on PyPI:
pip install visionpy-sc
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",
)