Spanve: A Statistical Method for Downstream-friendly Spatially Variable Genes in Large-scale Data
Why this work is in the frame
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Bibliographic record
Abstract
Depicting gene expression in a spatial context through spatial transcriptomics is beneficial for inferring cellular mechanisms. Identifying spatially variable genes is a crucial step in leveraging spatial transcriptome data to understand intricate spatial dynamics. In this study, we developed Spanve, a nonparametric statistical method for detecting spatially variable genes in large-scale spatial transcriptomics datasets by quantifying expression differences between each spot or cell and its local neighbors. This method offers a nonparametric approach for identifying spatial dependencies in gene expression without distributional assumptions. Compared with existing methods, Spanve yields fewer false positives, leading to more accurate identification of spatially variable genes. Furthermore, Spanve improves the performance of downstream spatial transcriptomics analyses including spatial domain detection and cell type deconvolution. These results show the broad application potential of Spanve in advancing our understanding of spatial gene expression patterns within complex tissue microenvironments. Spanve is publicly available at https://github.com/zjupgx/Spanve and https://ngdc.cncb.ac.cn/biocode/tool/BT7724.
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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 | 0.001 | 0.001 |
| 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