Wilson, J., Palmeri, J., Pappin, D. (August 2020) SimpliFi: a data-to-meaning analytics engine to bring omics understanding to all. J Biomol Tech, 31 (Suppl). S1. ISSN 1524-0215 (Print)1524-0215
Abstract
Because of (and despite) our ever-increasing amounts of omics data, limitations in our ability to translate data into human-understandable, actionable meaning represent a fundamental bottleneck. Indeed, the burgeoning number of analytics tools - from myriad custom scripts and free tools to exorbitant platforms - hinders rather than helps our attempts to understand. The problem worsens geometrically in multiomics studies which can be interrogated with factorially growing combinations of analytical tools made for different omics analyses. Additionally, once successfully navigated, often this digital labyrinth extorts time yet again as the analyses and their meaning must be painstakingly explained to others in attempt to impart their meaning. Working from exactly this experience, we created SimpliFi, an online, browser-accessible data-to-meaning engine. At its core, SimpliFi allows users of all skill levels to explore, visualize and 'touch and feel' their data to understand and form hypotheses of what experimental observations might mean. In SimpliFi, users from new-to-omics biomedical researchers to career bioanalysis data experts can rapidly and intuitively access statistically solid approaches, including nonparametric and resampling techniques. Mono- or integrated multiomics data are simplified into clean interactive displays of pathways, states of tissues, disease, cells, and molecular-level classifications. Importantly, results from fold-changes to p-values are always presented with their confidence intervals, informing end-user decisions of the potential risks of the next experimental choices. SimpliFi is, to our knowledge, the first omics analytics engine designed from the ground up from fundamental first-principles of mathematics, data analysis, visualization, and user design. As Simplify enables exploration of data from expert to inexperienced end-user levels, we anticipate it will ease data analysis and help bring meaning to omics data.
| Item Type: | Paper |
|---|---|
| Subjects: | diseases & disorders > cancer bioinformatics > computational biology |
| CSHL Authors: | |
| Communities: | CSHL labs > Pappin lab CSHL Cancer Center Program > Cellular Communication in Cancer Program |
| Depositing User: | Matthew Dunn |
| Date: | August 2020 |
| Date Deposited: | 30 Nov 2020 19:44 |
| Last Modified: | 08 Jun 2021 13:12 |
| PMCID: | PMC7424641 |
| URI: | https://repository.cshl.edu/id/eprint/39755 |
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