The 2018 Annual Meeting of the International Genetic Epidemiology Society
Bibliographic record
Abstract
Normalization of RNA-Seq data is essential to ensure accurate statistical inferences.The goal of this study was to assess the various methods for the normalization of RNA-Seq, including the assessment of known and unknown technical artifacts.Additionally, we assessed the impact of the reduction in degrees of freedom (DF) due to the normalization for known and latent technical artifacts which results in inflated type I error rates in the identification the differentially expressed (DE) genes.Specifically, we compared the remove unwanted variation (RUV), surrogate variable analysis (SVA "BE" and SVA "Leek"), and principal component analysis (PCA) methods for RNA-Seq data using both simulated and publicly available data from The Cancer Genome Atlas (TGCA) cervical cancer study (CESC).Using CESC data, a comparison between the top estimated latent factors between the methods showed that the SVA ("Leek") and RUV estimates were highly correlated (r = -0.88 and p < 2.2e-16), and the RUV and PCA methods produced more similar DE results compared to SVA ("Leek").The simulation study found that the permutation procedure in SVA ("BE") to determine the number of significant surrogate variables outperforms other methods for correctly estimating the number of technical artifacts.As expected, ignoring the loss of DF due to normalization results in an inflated type I error rates.Hence, we recommend to include not only library size corrections but also the assessment of known and unknown technical artifacts, and if needed, across sample normalization.Lastly, we recommend that the known technical artifacts along with the primary factors of interest in the design matrix, as opposed to differential analysis on a post-normalized data set.
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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 | 0.009 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.202 | 0.094 |
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".