Authors. Martin Becker, Huda Nassar, Camilo Espinosa, Ina A. Stelzer, Dorien Feyaerts, Eloise Berson , Neda H. Bidoki, Alan L. Chang,
Geetha Saarunya, Anthony Culos, Davide De Francesco, Ramin Fallahzadeh, Qun Liu, Yeasul Kim, Ivana Maric, Samson J. Mataraso, Seyedeh Neelufar Payrovnaziri, Thanaphong Phongpreecha, Neal G. Ravindra, Natalie Stanley, Sayane Shome, Yuqi Tan, Melan Thuraiappah, Maria Xenochristou, Lei Xue, Gary Shaw, David Stevenson, Martin S. Angst, Brice Gaudilliere, Nima Aghaeepour. Under review, 2023.

Abstract:Advanced measurement and data storage technologies have enabled high-dimensional profiling of complex biological systems. For this, modern multiomics studies regularly produce datasets with hundreds of thousands of measurements per sample enabling a new era of precision medicine. Correlation analysis is an important first step to gain deeper insights into the coordination and underlying processes of such complex systems. However, the construction of large correlation networks in modern high-dimensional datasets remains a major computational challenge due to rapidly growing runtime and memory requirements. We address this challenge by introducing CorALS, an open-source framework for the construction and analysis of large-scale parametric as well as nonparametric correlation networks for high-dimensional biological data. It features off-the-shelf algorithms suitable for both personal and high-performance computers enabling workflows and downstream analysis approaches. We illustrate the broad scope and potential of CorALS by exploring perspectives on complex biological processes in large-scale multiomics and single-cell studies.

CorALS Python package: CorALS is available as a Python package. with the source code on GitHub.

Code and Data Availa: CorALS code and reference to the data for reproducing the results and generating the figures are available on GitHub.