JOINTLY

pyJOINTLY is an Python package for interpretable joint clustering of single-cell and single-nucleus RNA-seq, using kernel-based joint non-negative matrix factorization. JOINTLY effectively captures shared cell states, while robustly accounting for dataset-specific states. It works directly with Scanpy, a major single-cell genomics framework.

JOINTLY is also available as a R package here

Scripts for reproducing the analyses in the manuscript are available here. Please note, that all analyses for the manuscript was performed using the R version of JOINTLY. The R and the Python versions although very similar does not produce identical results.

For the white adipose tissue atlas (WATLAS), the model weights are availiable here and the atlas can be explored and downloaded here

Installation

JOINTLY can be installed directly from GitHub.

pip install git+https://github.com/madsen-lab/pyJOINTLY

Basic usage

JOINTLY uses a list of Scanpy objects or raw expression matrices as numpy arrays. Here we demonstrate how to make a JOINTLY object and how to run the clustering algorithm.

## Load libraries
import scanpy as sc
from jointly import jointly, CreateJointlyObject_from_scanpyList


## Load test data
data_list = [sc.read_h5ad('pancdata/panc1.h5ad'), 
             sc.read_h5ad('pancdata/panc2.h5ad'), 
             sc.read_h5ad('pancdata/panc3.h5ad')]


## Make JOINTLY object
jointlyObject = CreateJointlyObject_from_scanpyList(data_list)

## Running JOINTLY with default parameters
jointly(jointlyObject)

## Clustering matrix is availible through
jointlyObject.anndata.obsm['X_Jointly']

## Individual clustering matrices is availible through
ds = 0
jointlyObject.adata_list[ds].obsm['X_Jointly']

Citation

If you use JOINTLY in your work, please consider citing our manuscript:

Møller AF, Madsen JGS et al. Interpretable joint clustering of single-cell transcriptomes (2023) Unpublished