RNA-Seq Reveals Spliceosome and Proteasome Genes as Most Consistent Transcripts in Human Cancer Cells
Bibliographic record
Abstract
Accurate quantification of gene expression by qRT-PCR relies on normalization against a consistently expressed control gene. However, control genes in common use often vary greatly between samples, especially in cancer. The advent of Next Generation Sequencing technology offers the possibility to better select control genes with the least cell to cell variability in steady state transcript levels. Here we analyze the transcriptomes of 55 leukemia samples to identify the most consistent genes. This list is enriched for components of the proteasome (ex. PSMA1) and spliceosome (ex. SF3B2), and also includes the translation initiation factor EIF4H, and many heterogeneous nuclear ribonucleoprotein genes (ex. HNRNPL). We have validated the consistency of our new control genes in 1933 cancer and normal tissues using publically available RNA-seq data, and their usefulness in qRT-PCR analysis is clearly demonstrated.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".