Gene-level read counts from bulk RNA-seq data for 38 follicular lymphoma diagnostic biopsies
Bibliographic record
Abstract
Conventional (bulk) RNA-sequencing was performed on unfractionated cell suspension or snap frozen whole tissue material. Total RNA was isolated with TRIzol reagent followed by purification over PureLink RNA Mini Kit columns (Invitrogen). RNA-seq was performed using a polyA-enriched strand-specific library construction protocol (doi: 10.1016/j.ccell.2016.02.009) and paired-end 75bp sequencing on an Illumina HiSeq 2500 instrument. Raw reads were aligned to the reference human genome assembly GRCh37 (hg19) using STAR (v2.5.2.a). To improve spliced alignment, STAR was provided with exon junction coordinates from the reference annotations (Gencode v19). We applied a modified version of a bioinformatics workflow for normalization of raw read counts and differential gene expression analysis (doi: 10.12688/f1000research.9005.3). Gene-level read counts were quantified using HTSEQ-count (v0.11.0; intersection-strict, reverse mode) (doi: 10.1093/bioinformatics/btu638). Genes showing low read counts (i.e., genes not showing counts per million (cpm) > 1.0 in at least 10% of samples) were removed from further analysis. Raw counts from expressed genes were then TMM-normalized and scaled to counts per million (CPM) using the edgeR (v3.22.2) package (doi: 10.1093/bioinformatics/btp616). Sample IDs correspond to those referenced in Wang X et al, Nature Communications (2022).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".