Supplementary data for "Evaluation of methods to assign cell type labels to cell clusters from single-cell RNA-sequencing data" (Diaz-Mejia JJ et al, 2019)
Bibliographic record
Abstract
Five scRNA-seq datasets from human liver cells (MacParland et al, 2018), mouse retinal neurons (Shekhar et al, 2016), Tabula Muris (Tabula Muris Consortium, 2018) and two peripheral blood mononuclear cell (PBMCs) datasets (Zheng et al, 2017) and (Gierahn et al., 2017) were processed and curated as described by Diaz-Mejia et al 2019 to use them as inputs for benchmarking of cell cluster labeling methods. Five types of files are provided: a) Single-cell RNA-sequencing (scRNA-seq) cell cluster average gene expression matrices (*E_xy_matrix.tsv) b) Cell type gene expression signatures in the form of gene sets (*gmt), binary (*binary_profile.tsv) and continuous profiles (*continuous_profile.tsv) c) Reference cell type gold standard annotations (*gold_standards.tsv) d) Supplementary Table 1, containing ROC AUC, PR AUC and computing times reported by Diaz-Mejia et al 2019 in Figure 6. e) Supplementary Table 2, containing ROC AUC, PR AUC analysis of the PBMC datasets using either the LM22 (Newman et al 2015) or the Monaco et al (2019) cell type signatures. Note: as described in Diaz-Mejia et al 2019, the PBMC and Tabula Muris datasets were analyzed in two ways each: i) pbmc-22-10x and pbmc-6-10x, ii) pbmc-22-seqwell and pbmc-6-seqwell, and iii) tabula_muris_11 and tabula_muris_6.
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.
How this classification was reachedexpand
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 | 0.002 | 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 | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".