scGMM-VGAE: a Gaussian mixture model-based variational graph autoencoder algorithm for clustering single-cell RNA-seq data
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Cell type identification using single-cell RNA sequencing data is critical for understanding disease mechanisms and drug discovery. Cell clustering analysis has been widely studied in health research for rare tumor cell detection. In this study, we propose a Gaussian mixture model-based variational graph autoencoder on scRNA-seq data (scGMM-VGAE) that integrates a statistical clustering model to a deep learning algorithm to significantly improve the cell clustering performance. This model feeds a cell-cell graph adjacency matrix and a gene feature matrix into a graph variational autoencoder (VGAE) to generate latent data. These data are then used for cell clustering by the Gaussian mixture model (GMM) module. To optimize the algorithm, a designed loss function is derived by combining parameter estimates from the GMM and VGAE. We test the proposed method on four publicly available and three simulated datasets which contain many biological and technical zeros. The scGMM-VGAE outperforms four selected baseline methods on three evaluation metrics in cell clustering. By successfully incorporating GMM into deep learning VGAE on scRNA-seq data, the proposed method shows higher accuracy in cell clustering on scRNA-seq data. This improvement has a significant impact on detecting rare cell types in health research. All source codes used in this study can be found at https://github.com/ericlin1230/scGMM-VGAE .
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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