Single-cell RNA-sequencing co-expression analysis with CFTR and CF modifier genes in lung tissue
Bibliographic record
Abstract
While cystic fibrosis is caused by loss-of-function variants in the Cystic Fibrosis Transmembrane Conductance Regulator (CFTR), other modifier genes have been shown to associate with disease severity. Co-expression of modifiers with CFTR in normal tissue indicates a cooperative relationship and suggests the potential for compensation in the presence of CFTR dysfunction. We examined the co-expression relationships with CFTR in the lung using single cell RNA sequencing to pinpoint cell types and their modifiers involved in the cystic fibrosis lung phenotype and support target prioritization for therapy. SmartSeq2 single cell RNA sequencing data on non-cystic fibrosis lung tissue was used for evaluation of co-expression with CFTR and modifier genes. A zero-inflated negative binomial model was used to formally test the co-expression association. 10X Chromium based single cell RNA sequencing data from both cystic fibrosis and non-cystic fibrosis studies were assessed graphically to confirm conclusions from the SmartSeq2 primary analysis. Differentiating basal, club and alveolar epithelial type 2 cells were found to have high proportions of cells expressing CFTR as well as the greatest number of significant co-expression relationships with the modifiers. In particular, among alveolar epithelial type 2 cells, we observed a significant co-expression trio relationship between CFTR, SLC6A14 and SLC26A9 (p < 0.05). CFTR-modifier gene co-expression suggests basal, club and alveolar epithelial type 2 cells show coordinated expression. Alveolar epithelial type 2 cells showed strong co-expression evidence with two of the most established cystic fibrosis modifier genes.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".