Abstract
We demonstrate the application and comparative interpretations of three tree-based algorithms for the analysis of data arising from flow cytometry: classification and regression trees (CARTs), random forests (RFs), and logic regression (LR). Specifically, we consider the question of what best predicts CD4 T-cell recovery in HIV-1 infected persons starting antiretroviral therapy with CD4 count between 200 and 350 cell/μL. A comparison to a more standard contingency table analysis is provided. While contingency table analysis and RFs provide information on the importance of each potential predictor variable, CART and LR offer additional insight into the combinations of variables that together are predictive of the outcome. In all cases considered, baseline CD3-DR-CD56+CD16+ emerges as an important predictor variable, while the tree-based approaches identify additional variables as potentially informative. Application of tree-based methods to our data suggests that a combination of baseline immune activation states, with emphasis on CD8 T-cell activation, may be a better predictor than any single T-cell/innate cell subset analyzed. Taken together, we show that tree-based methods can be successfully applied to flow cytometry data to better inform and discover associations that may not emerge in the context of a univariate analysis.
Highlights
Advances in flow cytometry, and technological developments that facilitate acquisition of multiparameter defined phenotypes, present new and exciting opportunities for predicting patient outcomes based on individual specific cell subset changes
We present three tree-based methods that are designed for discovery of complex structures of association in high-dimensional data settings: (1) classification and regression trees (CARTs) [1]; (2) random forests (RFs) [2]; (3) logic regression (LR) [3, 4]
Earlier studies from our group have demonstrated that pre-antiretroviral therapy (ART) CD95 expression on CD8+ T cells is negatively associated with the frequency of plasmacytoid Dendritic Cells (PDCs) after 52 weeks of treatment [23]
Summary
Technological developments that facilitate acquisition of multiparameter defined phenotypes, present new and exciting opportunities for predicting patient outcomes based on individual specific cell subset changes. This is relevant in the context of studying human immunodeficiency virus (HIV), where there exists a great potential to draw from the rich array of data on host cell-mediated response to infection and drug exposures, to inform and discover patient level determinants of disease progression and/or response to antiretroviral therapy (ART). Based on the mutually exclusive expression of CD8 and CD4 in the vast majority of T cells (as assessed in staining 7), in all remaining T cell stainings (2, 3, 4, and 6) CD4+ T cells were defined as CD3+ cells lacking expression of CD8
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