Normalization of metatranscriptomic and metaproteomic data for differential gene expression analyses: The importance of accounting for organism abundance
Bibliographic record
Abstract
Metatranscriptomics and metaproteomics make it possible to measure gene expression in microbial communities. So far these approaches were mostly used to get a general overview of the dominant metabolism and physiologies of community members. Recently, environmental microbiologists have started using metatranscriptomics and metaproteomics to look at gene expression differences between different environments or conditions. This has been mostly done by using makeshift adaptations of pure culture focused differential transcriptomics and proteomics approaches. However, since meta-omics data has many more variables attached to it as compared to pure culture derived data, such makeshift adaptations are problematic at best. One particular challenge is posed by the data normalization strategies used to account for technical and biological variables in meta-omic data. Here I discuss the most common normalization strategy for transcriptomic and proteomic data and why it is not valid by itself for meta-omic data. I provide logical proof that variation in species abundances between samples is an additional variable that must be accounted for during normalization of meta-omic data. Finally, I show how the existing normalization methods for transcriptomic and proteomic data can be augmented to be applicable to meta-omic data.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".