PCLDA: A cell annotation tool using scRNA-seq data based on simple statistics methods
Bibliographic record
Abstract
Abstract With the rise of single-cell transcriptome sequencing technology, more and more studies are focusing on single-cell-based disease diagnosis and treatment. Cell type annotation is the first and most critical step in analyzing single-cell genomic data. Traditional marker-genes-based annotation approaches require a lot of domain knowledge and subjective human decisions, which makes annotation time-consuming and generate inconsistent cell identities. In the past few years, multiple automated cell type identification tools have been developed, leveraging large amounts of accumulated reference cells. All these methods are extensions or revisions of vanilla supervised machine learning methods. However, complex models have four potential disadvantages (1) they may require more model assumptions which may not hold in real-world problems, (2) they may involve many model parameters to be tuned, (3) they may be harder to interpret, (4) they may require more computational resources. In this work, we propose PCLDA, a method based on the simplest statistical models, including principal component analysis and linear discriminant analysis, which do not suffer the problems mentioned above. We show PCLDA’s performance is not inferior to the fancier methods in real data. The key message we promote in this work is to use simple statistics if it can solve the problem, avoiding unnecessary complications.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.008 |
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".