MutClust 0.1.4

MutClust: Mutual rank-based coexpression and clustering.

MutClust: Efficient and Scalable Mutual Rank-Based Coexpression Clustering

MutClust is a Python tool for efficient and scalable mutual rank-based gene coexpression analyses. The clustering analysis is conducted using ClusterONE, as described in Wisecaver et al. 2017. MutClust is still under development.

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Features

• Mutual Rank Analysis: Compute mutual rank (MR) from Pearson correlations on your gene expression matrix.
• ClusterONE Clustering: Identify gene coexpression clusters from filtered/weighted MR networks.
• Fast: Multi-threaded, sparse matrix operations for speed on large datasets.

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Installation

Recommended

Install MutClust: Create the recommended conda environment:

conda env create -f environment.yml
conda activate mutclust

Alternative

Step 1: Make sure that ClusterONE is available from the command line:

conda install bioconda::clusterone

Step 2a: Install MutClust from PyPI:

pip install mutclust

Step 2b: Or clone the repository from GitHub:

git clone https://github.com/eporetsky/mutclust.git
cd mutclust
pip install .

Usage

1. Calculate Mutual Rank (MR)

mutclust mr -i expr.tsv -o results.mrs.tsv.gz --mr-threshold 100 --threads 4 [--log2]

Argument	Short	Description	Default
--input	-i	Path to the RNA-seq dataset (.tsv/.tsv.gz)	Required
--output	-o	Output file for mutual rank pairs	Required
--mr-threshold	-m	MR threshold for reporting gene pairs	100
--threads	-t	Number of CPU threads (correlation)	4
--log2		If set, applies log2(x+1) before calculation	OFF by default

• Input: Genes as rows, samples as columns (TSV, row index 'geneID').
• Output: Gzipped tab-separated file containing Gene1, Gene2, MR.

2. Cluster Genes (with ClusterONE)

mutclust cls -i results.mrs.tsv.gz -o results.cls.tsv --e_value 10

Argument	Short	Description	Default
--input	-i	Path to Mutual Rank (MR) pairs (.tsv/.tsv.gz)	Required
--output	-o	Output file for clusters (.tsv)	Required
--e_value	-e	Exponential decay constant for edge weighting	10

• The tool filters/weights MR pairs and calls ClusterONE for clustering.
• Output: clusters.tsv, listing clusters with p-value < 0.1. Tab-separated file containing clusterID, geneID, pval.

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Example Workflow

mutclust mr -i data/myexpr.tsv -o out.mrs.tsv.gz --mr-threshold 100 --threads 72 --log2
mutclust cls -i out.mrs.tsv.gz -o out.clusters.tsv --e_value 10

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Input Format

Expression file:

geneID\tSample1\tSample2\n...
GeneA \t1.1    \t2.2
GeneB \t4.2    \t3.7

Note: MutClust might be limited to linux because of dependency on pynetcor.

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Coming Soon

• Generate cluster gene annotation
• Calculate cluster GO term enrichment
• Calculate clusteer eigen-gene data
• Add a MutClust Dockerfile
• Add unit testing

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License

MIT License. See LICENSE file for details.

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Contributing

Suggestions, pull requests, and issues welcome!