Single-cell RNA-Sequencing Co-Expression Analysis with CFTR in Lung Tissue
Bibliographic record
Abstract
1 Abstract Background 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 forced expiratory volume in 1 second (FEV1)-based cystic fibrosis lung phenotype and support target cell-type prioritization for therapy 1 . Methods SmartSeq2 single cell RNA sequencing data from non-cystic fibrosis lung tissue was used for evaluation of co-expression with CFTR and modifier genes. 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. Results 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 strong co-expression trio relationship between CFTR, SLC6A14 and SLC26A9 (p < 0.05). Conclusions 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) | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".