Fatecode: Cell fate regulator prediction using classification autoencoder perturbation
Bibliographic record
Abstract
Abstract Cell reprogramming, which guides the conversion between cell states, is a promising technology for tissue repair and regeneration. Typically, a group of key genes, or master regulators, are manipulated to control cell fate, with the ultimate goal of accelerating recovery from diseases or injuries. Of importance is the ability to correctly identify the master regulators from single-cell transcriptomics datasets. To accomplish that goal, we propose Fatecode, a computational method that combines in silico perturbation experiments with cell trajectory modeling using deep learning to predict master regulators and key pathways controlling cell fate. Fatecode uses only scRNA-seq data from wild-type samples to learn and predict how cell type distribution changes following a perturbation. We assessed Fatecode’s performance using simulations from a mechanistic gene regulatory network model and diverse gene expression profiles covering blood and brain development. Our results suggest that Fatecode can detect known master regulators of cell fate from single-cell transcriptomics datasets. That capability points to Fatecode’s potential in accelerating the discovery of cell fate regulators that can be used to engineer and grow cells for therapeutic use in regenerative medicine applications.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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 | 0.001 | 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 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".