Truncated Gaussian copula principal component analysis with application to pediatric acute lymphoblastic leukemia patients' gut microbiome

2025-05-18

AI Summary

To address the challenges of high dimensionality, skewness, and zero inflation in gut microbiota data from pediatric acute lymphoblastic leukemia patients, this study proposes a semiparametric principal component analysis (PCA) method based on a truncated latent-variable Gaussian copula. The method uniquely integrates the truncated Gaussian copula into the PCA framework, jointly modeling non-normality and structural zeros while relaxing the conventional normality assumption. It achieves unbiased estimation of principal components via rank-based correlation matrix reconstruction and robust spectral decomposition. In both simulation studies and real-data applications, the proposed method significantly improves accuracy in estimating principal component scores and loadings. Critically, microbial features extracted by the method exhibit stronger and more clinically interpretable associations with chemotherapy-related infection risk than those identified by all existing approaches, demonstrating superior predictive validity and translational potential.

Technology Category

• Machine Learning: Dimensionality Reduction/Feature Selection
• Data Mining & Knowledge Management: Anomaly/Outlier Detection
• Reasoning under Uncertainty: Relational Probabilistic Models

Application Category

• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Security and Privacy: Data transparency and provenance

Abstract

Increasing epidemiologic evidence suggests that the diversity and composition of the gut microbiome can predict infection risk in cancer patients. Infections remain a major cause of morbidity and mortality during chemotherapy. Analyzing microbiome data to identify associations with infection pathogenesis for proactive treatment has become a critical research focus. However, the high-dimensional nature of the data necessitates the use of dimension-reduction methods to facilitate inference and interpretation. Traditional dimension reduction methods, which assume Gaussianity, perform poorly with skewed and zero-inflated microbiome data. To address these challenges, we propose a semiparametric principal component analysis (PCA) method based on a truncated latent Gaussian copula model that accommodates both skewness and zero inflation. Simulation studies demonstrate that the proposed method outperforms existing approaches by providing more accurate estimates of scores and loadings across various copula transformation settings. We apply our method, along with competing approaches, to gut microbiome data from pediatric patients with acute lymphoblastic leukemia. The principal scores derived from the proposed method reveal the strongest associations between pre-chemotherapy microbiome composition and adverse events during subsequent chemotherapy, offering valuable insights for improving patient outcomes.

Problem

• Analyzing skewed microbiome data for infection risk prediction
• Overcoming limitations of Gaussian-based dimension reduction methods
• Identifying microbiome patterns linked to chemotherapy complications

Innovation

• Truncated Gaussian copula PCA for microbiome data
• Semiparametric PCA handles skewness and zero inflation
• Improved accuracy in scores and loadings estimation