A Hybrid Clustering Algorithm for Identifying Cell Types from Single-Cell RNA-Seq Data
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Single-cell RNA sequencing (scRNA-seq) has recently brought new insight into cell differentiation processes and functional variation in cell subtypes from homogeneous cell populations. A lack of prior knowledge makes unsupervised machine learning methods, such as clustering, suitable for analyzing scRNA-seq . However, there are several limitations to overcome, including high dimensionality, clustering result instability, and parameter adjustment complexity. In this study, we propose a method by combining structure entropy and k nearest neighbor to identify cell subpopulations in scRNA-seq data. In contrast to existing clustering methods for identifying cell subtypes, minimized structure entropy results in natural communities without specifying the number of clusters. To investigate the performance of our model, we applied it to eight scRNA-seq datasets and compared our method with three existing methods (nonnegative matrix factorization, single-cell interpretation via multikernel learning, and structural entropy minimization principle). The experimental results showed that our approach achieves, on average, better performance in these datasets compared to the benchmark methods.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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