iSODA: A Comprehensive Tool for Integrative Omics Data Analysis in Single- and Multi-Omics Experiments
Bibliographic record
Abstract
Abstract Omics technologies including genomics, proteomics, metabolomics, and lipidomics allow profound insights into health and disease. Thanks to plummeting costs of continuously evolving omics analytical platforms, research centers collect multi-omics data more routinely. They are, however, confronted with the lack of a versatile software solution to harmoniously analyze single-omics data and merge and interpret multi-omics data. We have developed iSODA, an interactive web-based application for the analysis of single-as well as multi-omics omics data. The software tool emphasizes intuitive, interactive visualizations designed for user-driven data exploration. Researchers can filter and normalize their datasets and access a variety of functions ranging from simple data visualization like volcano plots and PCA, to advanced functional analyses like enrichment analysis for proteomics and saturation analysis for lipidomics. For insights from integrated multi-omics, iSODA incorporates Multi-Omics Factor Analysis – MOFA, and Similarity Network Fusion – SNF. All results are presented in interactive plots with the possibility of downloading plots and associated data. The ability to adapt the imported data on-the-fly allows for tasks such as removal of outlier samples or failed features, various imputation strategies, or data normalization. The modular design allows for extensions with new analyses and plots. The software is accessible under http://isoda.online/ . Abstract Figure Graphical summary for iSODA showcasing some application examples, the data import, the single-omics and multi-omics modules.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.057 | 0.027 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".