Multi-omic approach to identify phenotypic modifiers underlying cerebral demyelination in X-linked adrenoleukodystrophy
Bibliographic record
Abstract
These are the data tables used to produce results in the publication: "Multi-omic approach to identify phenotypic modifiers underlying cerebral demyelination in X-linked adrenoleukodystrophy." Phillip A. Richmond & Frans van der Kloet et al. Submitting to Frontiers in Cellular and Developmental Biology, 2020, Peroxisomal Special Issue. These tables include normalized measurements from four omics technologies, with no identifying information included. For details on processing, see the manuscript or contact: prichmond (at) cmmt (dot) ubc (dot) ca. Description of Files Sample mapping 20180314_sib_pairs.xlsx Excel sheet describing family numbering, etc. used as a mapping table within the sheets below. Methylation: DMRs_5_Families_ALL_0.10DB_Dec2019.csv Significant methylated regions with delta beta at least 10 percent when a single family is left out ALD_Deconvoluted_Betas_Dec2019.csv All fitted betas for every subject (single CpG) ALD_Limma_Final_Dec2019_CHR.csv All fitted effects using limma modeling per CpG RNA: Count_data.txt The raw count table summed at the gene level using featureCounts. Pvalues_all_23_01_2019.csv All pvalues and log fold changes for the genes included in the modeling process (also with family left out) Tmm_norm_counts_5_2_2020.csv Tmm normalized RNA count data Proteomic Report_Precursor_Peptides.xls The proteomic data as an excel spreadsheet Pvalues_prot_13_3_2019.xlsx The pvalues and log fold changes (also with family left out) Lipids: Lipid_data.csv The lipid data (metabolites with missings are removed) Pvalues_lipids.csv Pvalues for the lipid data (also with family left out) NOTE: For use of these data files for processing and reproducing results of the manuscript, please see https://github.com/Phillip-a-richmond/ALD_Modifier_Project.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.170 | 0.036 |
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".