scGeneRythm: Using Neural Networks and Fourier Transformation to Cluster Genes by Time-Frequency Patterns in Single-Cell Data
Bibliographic record
Abstract
Abstract Clustering genes in single-cell RNA sequencing plays a pivotal role in unraveling a plethora of biological processes, from cell differentiation to disease progression and metabolic pathways. Traditional time-domain methods are instrumental in certain analyses, yet they may overlook intricate relationships. For instance, genes that appear distinct in the time domain might exhibit striking similarities in the frequency domain. Recognizing this, we present scGeneRhythm, an innovative deep learning technique that employs Fourier transformation. This approach captures the rich tapestry of gene expression from both the time and frequency domains. When evaluated across a spectrum of single-cell datasets, scGeneRhythm consistently outperforms conventional approaches. The gene clusters it identifies not only demonstrate heightened statistical significance in enriched pathways but also bring to light underlying gene relationships previously obscured. Through integrating frequency-domain data, scGeneRhythm not only refines gene grouping but also uncovers pivotal biological insights, such as nuanced gene rhythmicity. By deploying scGeneRhythm, we foster a richer, multi-dimensional understanding of gene expression dynamics, enriching the potential avenues of cellular and molecular biology research.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".