Supporting data for “A practical tool for Maximal Information Coefficient analysis”

Dataset type: Software
Data released on March 27, 2018

Albanese D; Riccadonna S; Donati C; Franceschi P (2018): Supporting data for “A practical tool for Maximal Information Coefficient analysis” GigaScience Database. http://dx.doi.org/10.5524/100427

DOI10.5524/100427

The ability of finding complex associations in large omics datasets, assessing their significance, and prioritizing them according to their strength can be of great help in the data exploration phase. Mutual Information based measures of association are particularly promising, in particular after the recent introduction of the TICe and MICe estimators, which coniugate computational efficiency with good bias/variance properties. Despite that, a complete software implementation of these two measures and of a statistical procedure to test the significance of each association is still missing. In this paper we present MICtools, a comprehensive and effective pipeline which combines TICe and MICe into a multi-step procedure that allows the identification of relationships of various degrees of complexity. MICtools calculates their strength assessing statistical significance using a permutation-based strategy. The performances of the proposed approach are assessed by an extensive investigation in synthetic datasets and an example of a potential application on a metagenomic dataset is also illustrated. We show that MICtools, combining TICe and MICe, is able to highlight associations that would not be captured by conventional strategies. MICtools is implemented in Python, and is available for download at https://github.com/minepy/mictools

Additional details

Read the peer-reviewed publication(s):

Albanese, D., Riccadonna, S., Donati, C., & Franceschi, P. (2018). A practical tool for maximal information coefficient analysis. GigaScience, 7(4). doi:10.1093/gigascience/giy032

Additional information:

https://github.com/minepy/mictools

http://ocean-microbiome.embl.de/companion.html





File NameSample IDData TypeFile FormatSizeRelease Date 
GitHub archivearchive140.49 KB2018-03-14
ReadmeTEXT2.93 KB2018-03-14
Tabular dataCSV3.46 KB2018-03-14
Tabular dataCSV140.85 KB2018-03-14
Tabular dataCSV4.29 KB2018-03-14
Tabular dataCSV328.92 KB2018-03-14
Tabular dataCSV18.87 KB2018-03-14
Tabular dataCSV43.41 KB2018-03-14
Tabular dataCSV408.63 KB2018-03-14
Tabular dataCSV372.57 KB2018-03-14
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Funding body Awardee Award ID Comments
Autonomous Province of Trento D Albanese Accordo di Programma 2016-2018 Edmund Mach Foundation
Date Action
March 27, 2018 Dataset publish
July 4, 2018 Manuscript Link added : 10.1093/gigascience/giy032