RnaXtract, a tool for extracting gene expression, variants, and cell-type composition from bulk RNA sequencing
Bibliographic record
Abstract
RNA sequencing (RNA-seq) is a widely used method in transcriptomics research, offering insights into gene expression, variant discovery, and, when deconvoluted, the cellular composition of complex tissues. However, existing RNA-seq pipelines frequently emphasize gene expression analysis and often lack cell deconvolution and variant calling. To address these limitations, we present RnaXtract, a comprehensive and user-friendly pipeline designed to maximize extraction of valuable information from bulk RNA-seq data. RnaXtract automates an entire workflow, encompassing quality control, gene expression quantification, variant calling, and the cell-type deconvolution. Built on the Snakemake framework, RnaXtract ensures robust reproducibility, efficient resource management, and flexibility to adapt to diverse research needs. The pipeline integrates state-of-the-art tools, from quality control to the new updates on variant calling and cell-type deconvolution tools such as EcoTyper and CIBERSORTx, enabling researchers to extract biological insights with precision. By providing an end-to-end solution for bulk RNA-seq, RnaXtract addresses critical gaps in existing workflows, empowering researchers to explore gene expression, genetic variation, and cellular heterogeneity within a single cohesive framework.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.014 | 0.015 |
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".