pygsva 0.1.6

GSVA package for Python

pyGSVA: Gene Set Variation Analysis in Python

Gene Set Variation Analysis (GSVA) is a powerful gene set enrichment method designed for single-sample analysis. It enables pathway-centric analyses of molecular data by shifting the functional unit of analysis from individual genes to gene sets. This approach is particularly useful for bulk microarray, RNA-seq, and other molecular profiling data types, providing a pathway-level view of biological activity.

Overview

GSVA transforms an input gene-by-sample expression data matrix into a gene-set-by-sample expression data matrix, representing pathway activities. This transformed data can then be utilized with classical analytical methods such as:

• Differential Expression
• Classification
• Survival Analysis
• Clustering
• Correlation Analysis

Additionally, GSVA enables pathway comparisons with other molecular data types, such as microRNA expression, binding data, copy-number variation (CNV), or single nucleotide polymorphisms (SNPs).

Methods

The pyGSVA package provides Python implementations of four single-sample gene set enrichment methods:

1. PLAGE (Pathway Level Analysis of Gene Expression)

• Reference: Tomfohr, Lu, and Kepler (2005)
• Standardizes expression profiles over the samples.
• Performs Singular Value Decomposition (SVD) on each gene set.
• The coefficients of the first right-singular vector are returned as pathway activity estimates.
• Note: The sign of singular vectors is arbitrary due to the nature of SVD.

2. Z-Score Method

• Reference: Lee et al. (2008)
• Standardizes expression profiles over the samples.
• Combines standardized values for each gene in a gene set using the formula: [ Z_\gamma = rac{\sum_{i=1}^{k} z_i}{\sqrt{k}} ] where ( z_i ) are the standardized values of genes in a specific sample, and ( \gamma ) is the gene set.

3. ssGSEA (Single-Sample Gene Set Enrichment Analysis)

• Reference: Barbie et al. (2009)
• Calculates enrichment scores as the normalized difference in empirical cumulative distribution functions (CDFs) of gene expression ranks inside and outside the gene set.
• By default, the pathway scores are normalized by dividing them by the range of calculated values. This normalization can be switched off.

4. GSVA (Default Method)

• Reference: Hänzelmann, Castelo, and Guinney (2013)
• A non-parametric method using empirical CDFs of gene expression ranks inside and outside the gene set.
• Calculates an expression-level statistic to bring gene expression profiles with varying dynamic ranges to a common scale.

Applications

• Estimate pathway activity for individual samples.
• Integrate pathway activity scores with traditional statistical analyses.
• Compare pathway activity across different molecular data types.

Installation

git clone https://github.com/guokai8/pygsva
cd pygsva
python setup.py install --user

Usage

from pygsva import *

# Example usage with default GSVA method
pbmc=pd.read_csv('pbmc_exp.csv',index_col=0)
hsko=pd.read_csv('hsko.csv',index_col=0)
gene_sets = {key: group.iloc[:, 0].tolist() for key, group in hsko.groupby(hsko.iloc[:, 2])}
##ssgsea
params=ssgseaParam(pbmc,gene_sets=gene_sets,remove_constant=False,remove_nz_constant=False,use_sparse=True)
res=ssgsea(pbmc,gene_sets,use_sparse=True,remove_constant=False,remove_nz_constant=False)
##gsva
param=gsvaParam(pbmc,gene_sets=gene_sets,use_sparse=True,kcdf="Poisson",n_jobs=1)
res=gsva(param)
##plage
paramp=plageParam(pbmc,gene_sets=gene_sets,use_sparse=True,min_size=2)
res=gsva(paramp)
##zscore
paramz=zscoreParam(pbmc,gene_sets,use_sparse=True)
res=gsva(paramz)

References

If you use any of the methods in this package, please cite the corresponding articles:

1. Tomfohr, Lu, and Kepler (2005) - Pathway Level Analysis of Gene Expression (PLAGE)
2. Lee et al. (2008) - Z-Score Method
3. Barbie et al. (2009) - Single Sample Gene Set Enrichment Analysis (ssGSEA)
4. Hänzelmann, Castelo, and Guinney (2013) - Gene Set Variation Analysis (GSVA)

License

This project is licensed under the MIT License. See the LICENSE file for details.

Contributing

We welcome contributions! Feel free to submit issues, fork the repository, and make pull requests.