JarrVis: Visualising Taxa-function relationships from meta-omic data
Bibliographic record
Abstract
Abstract High-throughput sequencing has necessitated and facilitated the development of various computational tools for dissecting the taxonomic structure and molecular function of microbial communities residing in diverse ecological niches. A wide variety of tools and protocols have been developed to process amplicon sequencing as well as meta-omic (metagenomic, metatranscriptomic and genomic) data to generate relative abundances of taxonomic groups or functional categories on a per-sample basis. A key output from many of these protocols is a stratified relative abundance table that specifies the taxonomic contribution to each function. However, there are a very few tools which can effectively visualize the different taxa-function relationships from these stratified outputs. Here we introduce an R Shiny application called JarrVis (Just Another stRatified Rpkm VISualizer) which can visualize the taxa-function relationships resulting from different types of microbiome data. JarrVis can visualize relationships between samples (which can be combined based on metadata categories), the taxa that are detected in these samples (at any given taxonomic level) and the functions encoded by these taxa in an interactive interface. We utilized JarrVis to visualize and examine taxa-function relationships in 1) a 16S amplicon time-series spanning 4 years with samples collected weekly, 2) functions associated with microbial cobalamin biosynthesis and uptake in metagenome-assembled genomes from marine metagenomes and 3) a set of metatrascriptomes from gut samples of patients with Crohn’s Disease, Ulcerative Colitis and non-IBD controls. Our analysis was able recapitulate already published taxa-function relationships as well as discover novel insights from these publicly available datasets. JarrVis and related scripts and data are available at https://github.com/dhwanidesai/JarrVis .
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".