PMart Web Application: Marketplace for Interactive Analysis of Panomics Data.

PMart is a web-based tool for reproducible quality control, exploratory data analysis, statistical analysis, and interactive visualization of 'omics data, based on the functionality of the pmartR R package. The newly improved user interface supports more 'omics data types, additional statistical capabilities, and enhanced options for creating downloadable graphics. PMart supports the analysis of label-free and isobaric-labeled (e.g., TMT, iTRAQ) proteomics, nuclear magnetic resonance (NMR) and mass-spectrometry (MS)-based metabolomics, MS-based lipidomics, and ribonucleic acid sequencing (RNA-seq) transcriptomics data. At the end of a PMart session, a report is available that summarizes the processing steps performed and includes the pmartR R package functions used to execute the data processing. In addition, built-in safeguards in the backend code prevent users from utilizing methods that are inappropriate based on omics data type. PMart is a user-friendly interface for conducting exploratory data analysis and statistical comparisons of omics data without programming.

Missing data in multi-omics integration: Recent advances through artificial intelligence.

Biological systems function through complex interactions between various 'omics (biomolecules), and a more complete understanding of these systems is only possible through an integrated, multi-omic perspective. This has presented the need for the development of integration approaches that are able to capture the complex, often non-linear, interactions that define these biological systems and are adapted to the challenges of combining the heterogenous data across 'omic views. A principal challenge to multi-omic integration is missing data because all biomolecules are not measured in all samples. Due to either cost, instrument sensitivity, or other experimental factors, data for a biological sample may be missing for one or more 'omic techologies. Recent methodological developments in artificial intelligence and statistical learning have greatly facilitated the analyses of multi-omics data, however many of these techniques assume access to completely observed data. A subset of these methods incorporate mechanisms for handling partially observed samples, and these methods are the focus of this review. We describe recently developed approaches, noting their primary use cases and highlighting each method's approach to handling missing data. We additionally provide an overview of the more traditional missing data workflows and their limitations; and we discuss potential avenues for further developments as well as how the missing data issue and its current solutions may generalize beyond the multi-omics context.