Using TIPC to discern spatial patterns of immune activity in an HCC cohort and their correlation with patient treatment response. by Mai Chan Lau (3135303)

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Characterization of CD3⁺ T-cell spatial distribution in the CRC tumor microenvironment using TIPC. by Mai Chan Lau (3135303)

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Demonstration of the limitations of existing spatial analysis methods, namely nearest neighbor distance (NND), G-cross function and Morisita-Horn index. by Mai Chan Lau (3135303)

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Validation of TIPC spatial subtypes which were first determined in NHS/ HPFS cohort and later recapitulated in TCGA cohort, using eosinophils identified morphologically in H&E imag... by Mai Chan Lau (3135303)

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Implementation of TIPC, a computational method utilizing hexagonal tessellation and a classifier that evaluates multiple spatial parameters against a tumor region-specific null mod... by Mai Chan Lau (3135303)

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Comparison of TIPC performance with existing analysis methods, using CD3⁺ T-cell data. by Mai Chan Lau (3135303)

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TIPC application on three different immune cell types, namely (a-c) cytotoxic memory T cells, (d-f) eosinophils, and (g-i) neutrophils in CRC (NHS/ HPFS). by Mai Chan Lau (3135303)

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Prognostic and molecular associations TIPC subtypes identified using CD3⁺ T-cells in CRC. by Mai Chan Lau (3135303)

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Code by Baoqiang Chen (21099509)

“…We implemented machine learning algorithms using the following R packages: rpart for Decision Trees, gbm for Gradient Boosting Machines (GBM), ranger for Random Forests, the glm function for Generalized Linear Models (GLM), and xgboost for Extreme Gradient Boosting (XGB). …”

Core data by Baoqiang Chen (21099509)

“…We implemented machine learning algorithms using the following R packages: rpart for Decision Trees, gbm for Gradient Boosting Machines (GBM), ranger for Random Forests, the glm function for Generalized Linear Models (GLM), and xgboost for Extreme Gradient Boosting (XGB). …”