Using random forests to uncover the predictive power of distance-varying cell interactions in tumor microenvironments Article Swipe
Jeremy VanderDoes
,
Claire Marceaux
,
Kenta Yokote
,
Marie-Liesse Asselin-Labat
,
Gregory Rice
,
Jack Hywood
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1371/journal.pcbi.1011361
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1371/journal.pcbi.1011361
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 between patient groups, as well as determining the extent to which this information can predict outcomes such as patient survival or treatment success with emerging immunotherapies, is of great interest. Moreover, in the face of a large number of 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 at both identifying important spatial interactions while also controlling the false discovery rate. 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 .
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1371/journal.pcbi.1011361
- https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1011361&type=printable
- OA Status
- gold
- Cited By
- 1
- References
- 62
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4399666047
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- OpenAlex ID
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https://openalex.org/W4399666047Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1371/journal.pcbi.1011361Digital Object Identifier
- Title
-
Using random forests to uncover the predictive power of distance-varying cell interactions in tumor microenvironmentsWork title
- Type
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articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-06-14Full publication date if available
- Authors
-
Jeremy VanderDoes, Claire Marceaux, Kenta Yokote, Marie-Liesse Asselin-Labat, Gregory Rice, Jack HywoodList of authors in order
- Landing page
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https://doi.org/10.1371/journal.pcbi.1011361Publisher landing page
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https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1011361&type=printableDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1011361&type=printableDirect OA link when available
- Concepts
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Spatial analysis, Computer science, Predictive power, Random forest, Medicine, Computational biology, Biology, Machine learning, Statistics, Mathematics, Epistemology, PhilosophyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1Per-year citation counts (last 5 years)
- References (count)
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62Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.composition | 14 |
| abstract_inverted_index.controlling | 146 |
| abstract_inverted_index.determining | 36 |
| abstract_inverted_index.experiments | 137 |
| abstract_inverted_index.identifying | 140 |
| abstract_inverted_index.information | 7, 42 |
| abstract_inverted_index.interrogate | 158 |
| abstract_inverted_index.patient’s | 9 |
| abstract_inverted_index.summarizing | 97 |
| abstract_inverted_index.distribution | 18 |
| abstract_inverted_index.interactions | 70, 79, 143 |
| abstract_inverted_index.populations. | 25 |
| abstract_inverted_index.predictions. | 86 |
| abstract_inverted_index.relationships | 99 |
| abstract_inverted_index.relationships. | 127 |
| abstract_inverted_index.immunotherapies, | 55 |
| abstract_inverted_index.microenvironments | 1 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 91 |
| corresponding_author_ids | https://openalex.org/A5021654852, https://openalex.org/A5101772965, https://openalex.org/A5052634349, https://openalex.org/A5037872605, https://openalex.org/A5039377539, https://openalex.org/A5064354517 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 6 |
| corresponding_institution_ids | https://openalex.org/I1320473756, https://openalex.org/I151746483, https://openalex.org/I165779595, https://openalex.org/I196021976 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/15 |
| sustainable_development_goals[0].score | 0.5699999928474426 |
| sustainable_development_goals[0].display_name | Life in Land |
| citation_normalized_percentile.value | 0.59039013 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |