Mango: unearthing patterns in large-scale biological data through interactive correlation analysis

Integrating different types of biological data is often challenging due to the presence of both numerical and categorical data. This complexity makes it harder to evaluate causal biological effects, especially when confounders like population structure, sampling methods, or multi-omics integration can lead to incorrect conclusions.

We introduce Mango, an interactive correlation browser designed for visually exploring any tabular data type, regardless of their distribution. Our results on genomic and transcriptomic datasets demonstrate that these correlations can effectively distinguish between biases and causal relationships in large-scale data.

Mango uses a novel algorithm for non-parametric correlation of numeric to categorical data called Median-Ranked Label Encoding and is implemented in C++/Java and supported on Linux. Source code is available under the GNU GPLv3 license from [1].