A Novel Approach to Single Cell RNA-Sequence Analysis Facilitates In Silico Gene Reporting of Human Pluripotent Stem Cell-Derived Retinal Cell Types
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
Cell type-specific investigations commonly use gene reporters or single-cell analytical techniques. However, reporter line development is arduous and generally limited to a single gene of interest, while single-cell RNA (scRNA)-sequencing (seq) frequently yields equivocal results that preclude definitive cell identification. To examine gene expression profiles of multiple retinal cell types derived from human pluripotent stem cells (hPSCs), we performed scRNA-seq on optic vesicle (OV)-like structures cultured under cGMP-compatible conditions. However, efforts to apply traditional scRNA-seq analytical methods based on unbiased algorithms were unrevealing. Therefore, we developed a simple, versatile, and universally applicable approach that generates gene expression data akin to those obtained from reporter lines. This method ranks single cells by expression level of a bait gene and searches the transcriptome for genes whose cell-to-cell rank order expression most closely matches that of the bait. Moreover, multiple bait genes can be combined to refine datasets. Using this approach, we provide further evidence for the authenticity of hPSC-derived retinal cell types. Stem Cells 2018;36:313-324.
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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 | 0.000 |
| 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