Computational Frameworks for Spatial Transcriptomics in Tumor Microenvironment
Bibliographic record
Abstract
The spatial heterogeneity of the tumor microenvironment (TME) has a significant impact on tumor progression and treatment response. The rise of spatial transcriptomics technology has provided a new perspective for the study of TME, but its high-dimensional data characteristics pose challenges to the analytical methods. This paper constructs a computational modeling framework for TME spatial transcriptome data, integrating graph theory and spatial statistical methods to mine spatial patterns and cellular communication networks in tissues. We systematically expounded the spatial heterogeneity of the tumor microenvironment, the mainstream spatial transcriptome techniques and data characteristics, and proposed corresponding algorithms to identify cell subpopulations, cell communications and differential gene patterns in space. Through the case of spatial transcriptome of breast cancer, we verified the effectiveness of this framework and revealed the significant differences in molecular characteristics and immune microenvironment between the core and margin of the tumor. Studies have shown that computational models of spatial transcriptomics can deeply analyze the structure and function of the tumor microenvironment, providing new support for precision medicine.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".