Cell type identification via convolutional neural networks and self-organizing maps on single-cell RNA-seq data
Bibliographic record
Abstract
Recent studies on using single-cell RNA sequencing (scRNA-seq) technology have been widely applied in biological studies such as drug discovery. Prior to in-depth investigations of the functionality of single cells for pathological goals, identification of cell types is an essential step that can be sped up using computational methods. Recently, supervised learning methods have been developed to automatically identify cell types. Due to the lack of sufficient annotated datasets, these methods have not been commonly used in scRNA-seq studies. Classification methods can simply take advantage of feature selection techniques to improve cell type prediction while identifying the most informative genes among a high number of genes in high-dimensional scRNA-seq datasets. In this regard, we introduce a combination of two powerful techniques for representation learning and unsupervised feature selection to automatically achieve cell type identification in two steps. Average prediction accuracy of 98% obtained on six different cell types in a Human Pancreas scRNA-seq dataset. In addition, we found that 11 out of 13 selected genes are biologically related to two cell types in the Human Pancreas, which confirms the effectiveness of the proposed approach.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".