# CCC-GPU: a graphics processing unit (GPU)-accelerated nonlinear correlation coefficient for large-scale transcriptomic analyses

## TL;DR

This paper introduces CCC-GPU, a fast GPU-based tool for analyzing complex relationships in large biological datasets.

## Contribution

A GPU-accelerated nonlinear correlation coefficient implementation for large-scale transcriptomic data.

## Key findings

- CCC-GPU efficiently computes nonlinear correlations for mixed data types.
- It provides significant speed improvements over previous methods.
- The tool is suitable for handling increasingly large biological datasets.

## Abstract

Identifying meaningful patterns in complex biological data necessitates correlation coefficients capable of capturing diverse relationship types beyond simple linearity. Furthermore, efficient computational tools are crucial for handling the ever-increasing scale of biological datasets.

We introduce CCC-GPU, a high-performance, GPU-accelerated implementation of the Clustermatch Correlation Coefficient (CCC). CCC-GPU computes correlation coefficients for mixed data types, effectively detects nonlinear relationships, and offers significant speed improvements over its predecessor.

The source code of CCC-GPU is openly available on GitHub (https://github.com/pivlab/ccc-gpu) and archived on Zenodo (https://doi.org/10.5281/zenodo.18310318), distributed under the BSD-2-Clause Plus Patent License.