Mango: unearthing patterns in large-scale biological data through interactive correlation analysis
- 1Department of Biotechnology, University of Inland Norway, Holsetgata 22, Hamar, 2317 Norway
Abstract
Motivation: 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.
Results: 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.
Availability and implementation: 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].
Supplementary Information: The online version contains supplementary material available at https://doi.org/10.1186/s12864-026-13249-x.
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BibTeX
@article{branders2026mango,
title = {{Mango: unearthing patterns in large-scale biological data through interactive correlation analysis}},
author = {Sverre Branders and Manfred G. Grabherr and Rafi Ahmad},
journal = {BMC Genomics},
year = {2026},
month = aug,
doi = {10.1186/s12864-026-13249-x},
url = {https://doi.org/10.1186/s12864-026-13249-x},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Mango: unearthing patterns in large-scale biological data through interactive correlation analysis
AU - Sverre Branders
AU - Manfred G. Grabherr
AU - Rafi Ahmad
PY - 2026
DA - 2026/08/06
JO - BMC Genomics
DO - 10.1186/s12864-026-13249-x
UR - https://doi.org/10.1186/s12864-026-13249-x
AB - Motivation: 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. Results: 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. Availability and implementation: 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]. Supplementary Information: The online version contains supplementary material available at https://doi.org/10.1186/s12864-026-13249-x.
SN - 1471-2164
ER -