Deciphering heterogeneous populations of migrating cells based on the computational assessment of their dynamic properties
Bibliographic record
Abstract
Neuronal migration is a highly dynamic process, and multiple cell movement metrics can be extracted from time-lapse imaging datasets. However, these parameters alone are often insufficient to evaluate the heterogeneity of neuroblast populations. We developed an analytical pipeline based on reducing the dimensions of the dataset by principal component analysis (PCA) and determining sub-populations using k-means, supported by the elbow criterion method and validated by a decision tree algorithm. We showed that neuroblasts derived from the same adult neural stem cell (NSC) lineage as well as across different lineages are heterogeneous and can be sub-divided into different clusters based on their dynamic properties. Interestingly, we also observed overlapping clusters for neuroblasts derived from different NSC lineages. We further showed that genetic perturbations or environmental stimuli affect the migratory properties of neuroblasts in a sub-cluster-specific manner. Our data thus provide a framework for assessing the heterogeneity of migrating neuroblasts.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".