ARBOLpy 0.0.2

python implementation of ARBOL scRNAseq iterative tiered clustering https://github.com/jo-m-lab/ARBOL

ARBOLpy

python implementation of the R package ARBOL, scRNAseq iterative tiered clustering

Iteratively cluster single cell datasets using a scanpy anndata object as input. Identifies and uses optimum clustering parameters at each tier of clustering. Current build includes SCtransform normalization. Outputs QC and visualization plots for each clustering event.

Install

By github:

pip install git+https://github.com/jo-m-lab/ARBOLpy.git

from PyPI

pip install arbolpy

or clone the repository and source the functions directly from the script

git clone https://github.com/jo-m-lab/ARBOLpy.git

import "path/to/cloned/git/repo/ARBOLpy/ARBOL")

there is a docker image available with ARBOL and dependencies preinstalled https://hub.docker.com/r/kkimler/arbolpy

Recommended Usage

ARBOL was developed and used in the paper, "A treatment-naïve cellular atlas of pediatric Crohn’s disease predicts disease severity and therapeutic response" Currently, a tutorial is only available for the R version, where the FGID atlas figure is reproduced: https://jo-m-lab.github.io/ARBOL/ARBOLtutorial.html

This package is meant as a starting point for the way that we approached clustering and and is meant to be edited/customized through community feedback through users such as yourself!

We have dedicated effort to choosing reasonable defaults, but there is no certainty that they are the best defaults for your data.

The main function of ARBOLpy is ARBOL() - here is an example call. The helper function write_ARBOL_output writes the anytree object's endclusters to a csv file.

tree = ARBOL.ARBOL(adata)

ARBOL.write_ARBOL_output(tree)

Note This script can take a long time to run. Running on 20K cells could take an hour. Running on 100k+ cells could take 5 hours. This timing varies based on the heterogeneity of your data.

Note The script requires approximately 1.2 GB RAM per 1k cells, meaning on a local machine with 16GB RAM, one could reasonably run 12k cells. The current RAM/time bottleneck is the silhouette analysis, which runs

ARBOL() Parameters

• adata scanpy anndata object
• normalization_method normalization method, defaults to "Pearson", scanpy's experimental implementation of SCTransform
• tier starting tier, defaults to 0
• clustN starting cluster, defaults to 0
• min_cluster_size minimum number of cells to allow further clustering
• tree anytree object to attach arbol to. Shouldn't be changed unless building onto a pre-existing tree.
• parent parent node of current clustering event, defaults to None. As with tree, shouldn't be changed unless building onto a pre-existing anytree object
• max_tiers maximum number of tiers to allow further clustering
• min_silhouette_res lower bound of silhouette analysis leiden clustering resolution parameter scan
• max_silhouette_res upper bound
• h5dir where to save h5 objects for each tier and cluster, if None, does not save
• figdir where to save QC and viz figures for each tier and cluster, if None does not save

Returns

• anytree object based on iterative tiered clustering