Using random forests to uncover the predictive power of distance–varying cell interactions in tumor microenvironments
Bibliographic record
Abstract
Abstract Tumor microenvironments (TMEs) contain vast amounts of information on patient’s cancer through their cellular composition and the spatial distribution of tumor cells and immune cell populations. Exploring variations in TMEs among patients and cancer types, as well as determining the extent to which this information can predict variables such as patient survival or treatment success with emerging immunotherapies, is of great interest. Moreover, in the face of a large number of potential spatial cell interactions to consider, we often wish to identify specific interactions that are useful in making such predictions. We present an approach to achieve these goals based on summarizing spatial relationships in the TME using spatial K functions, and then applying functional data analysis and random forest models to both predict outcomes of interest and identify important spatial relationships. This approach is shown to be effective in simulation experiments. We further used the proposed approach to interrogate two real data sets of Multiplexed Ion Beam Images of TMEs in triple negative breast cancer and lung cancer patients. The methods proposed are publicly available in a companion R package funkycells . Author summary Spatial data on the tumor microenvironment (TME) are becoming more prevalent. Existing methods to interrogate such data often have several deficiencies: (1) they rely on estimating the spatial relationships among cells by examining simple counts of cells within a single radius, (2) they do not come with ways to evaluate the statistical significance of any findings, or (3) they consider multiple individual interactions resulting in overly optimistic estimates of interaction importances. Our approach, which leverages techniques in spatial statistics and uses a benchmark ensemble machine learning method addresses (1), since the K functions used encode the relative densities of cells over all radii up to a user-selected maximum radius, and (2) we have developed a custom approach based on permutation and cross-validation to evaluate the statistical significance of any findings of significant spatial interactions in the TME, (3) over potentially multiple interactions. Our approach is also freely available with an R implementation called funkycells . In the analysis of two real data sets, we have seen that the method performs well, and gives the expected results. We think this will be a robust tool to add to the toolbox for researchers looking to interrogate, what can be sometimes unwieldy, TME data.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".