A network enhancement-based method for clustering of single cell RNA-seq data
Why this work is in the frame
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Bibliographic record
Abstract
Single cell RNA sequencing (scRNA-seq) provides a more granular description of gene expression in a single cell. Many clustering methods for scRNA-seq data have been developed to understand cell development and cell differentiation. However, the high dimension and the strong noise make clustering scRNA-seq data challenging. To overcome this problem, we propose a method for clustering scRNA-seq data, called network enhancement-based similarity combined with Louvain (NES-Louvain). In NES-Louvain, the initial similarity matrix is denoised by using a network enhancement method. Then, a path-based similarity measurement is designed to introduce the nodes in high-order paths based on the assumption that including more relevant nodes would improve the similarity of node pairs. Finally, the Louvain community detection method is improved to clustering single cells. The experimental results show that NES and NES-Louvain achieve better performance than other methods. Furthermore, NES-Louvain shows robust to perturbation.
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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) | 0.000 | 0.000 |
| 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 | 0.000 |
| Research integrity | 0.000 | 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 it