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As such, the software is ideally suited for the analysis of experimental data from individualized profiling of subjects, and can facilitate analysis of data from the emerging field of individualized health monitoring, and detecting temporal trends that may be associated to adverse health events.īasic Protocol: Time Series Analysis of Transcriptomics Expression.

MathIOmica’s time series classification methods address common issues including missing data and uneven sampling in measurements. MathIOmica provides spectral methods based on periodograms and autocorrelations for automatically detecting classes of temporal behavior, and allowing the user to visualize collective temporal behavior, as well as assessing biological significance through Gene Ontology and pathway enrichment analyses. MathIOmica uses Mathematica’s notebook interface, wherein users can import longitudinal datasets, carry out quality control and normalization, generate time series and classify temporal trends. MathIOmica is a package for bioinformatics written in the Wolfram Language, that provides multiple utilities to facilitate the analysis of longitudinal data generated from omics experiments, including transcriptomics, proteomics and metabolomics data, as well as any generalized time series.

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