bigsur 0.0.3

Basic Informatics and Gene Statistics from Unnormalized Reads, a feature selection tool for scRNAseq

BigSur

BigSur is a package for principled, robust scRNAseq normalization. Currently we can perform feature selection, see BigSurR for correlations.

What is BigSur?

Basic Informatics and Gene Statistics from Unnormalized Reads (BigSur) is a principled pipeline allowing for feature selection, correlation and clustering in scRNAseq.

• The feature selection derivations are detailed in the BioRxiv preprint Dollinger et al. 2023.
• The correlation are detailed in Silkwood et al. 2023.

Installation

The only way to install BigSur currently is to clone the GitHub repo. We've included a environment file for conda environment installation; the only package we require that isn't installed with scanpy is mpmath and numexpr. For example:

In terminal:

cd bigsur_dir #directory to clone to

git clone https://github.com/landerlabcode/BigSur.git

conda create -f environment.yml -n bigsur

A note about the virtual environment

This environment contains all packages that are required to reproduce any result of the paper. If you want a lightweight conda enviroment (or alternatively, if the environment file is causing issues), you can create a sufficient conda environment as follows:

In terminal:

conda create -n bigsur -c conda-forge scanpy mpmath numexpr ipykernel python-igraph leidenalg

Usage

Usage for feature selection is detailed in the example notebook.

TL;DR:

import sys

sys.path.append(bigsur_dir) # directory where git repo was cloned

from BigSur.feature_selection import mcfano_feature_selection as mcfano

Replace sc.pp.highly_variable_genes(adata) in your pipeline with mcfano(adata, layer='counts'), where the UMI counts are in adata.layers['counts'].

And that's it! You can read more about how to use BigSur for feature selection, and in particular how to optimize cutoffs for a given dataset, in the example notebook.