pgcor

pgcor is a Python library designed to facilitate the process of analyzing and visualizing genomic correlation data. It provides a streamlined approach for researchers to compute and interpret correlation matrices derived from genomic datasets, such as gene expression levels or single-nucleotide polymorphism (SNP) frequencies. The library offers functions for calculating various correlation coefficients, including Pearson, Spearman, and Kendall's tau, allowing users to choose the most appropriate method for their data.

A key feature of pgcor is its integrated visualization capabilities. It can generate heatmaps and other graphical representations of correlation matrices, making it easier to identify patterns, clusters, and significant associations within large genomic datasets. The library also includes tools for filtering and selecting specific correlations based on defined thresholds, aiding in the identification of biologically relevant relationships.

pgcor aims to simplify complex genomic analyses by providing a user-friendly interface and efficient computational methods. Its modular design allows for integration with other bioinformatics tools and workflows. The library is particularly useful for studies investigating gene-gene interactions, pathway analysis, and the identification of biomarkers. By abstracting away some of the more intricate computational details, pgcor enables researchers to focus more on the biological interpretation of their findings.