TY - JOUR
T1 - Sparse tree-based clustering of microbiome data to characterize microbiome heterogeneity in pancreatic cancer
AU - Shi, Yushu
AU - Zhang, Liangliang
AU - Do, Kim Anh
AU - Jenq, Robert
AU - Peterson, Christine B.
N1 - Publisher Copyright:
© (RSS) Royal Statistical Society 2023. All rights reserved.
PY - 2023/1
Y1 - 2023/1
N2 - There is a keen interest in characterizing variation in the microbiome across cancer patients, given increasing evidence of its important role in determining treatment outcomes. Here our goal is to discover subgroups of patients with similar microbiome profiles. We propose a novel unsupervised clustering approach in the Bayesian framework that innovates over existing model-based clustering approaches, such as the Dirichlet multinomial mixture model, in three key respects: we incorporate feature selection, learn the appropriate number of clusters from the data, and integrate information on the tree structure relating the observed features. We compare the performance of our proposed method to existing methods on simulated data designed to mimic real microbiome data. We then illustrate results obtained for our motivating dataset, a clinical study aimed at characterizing the tumour microbiome of pancreatic cancer patients.
AB - There is a keen interest in characterizing variation in the microbiome across cancer patients, given increasing evidence of its important role in determining treatment outcomes. Here our goal is to discover subgroups of patients with similar microbiome profiles. We propose a novel unsupervised clustering approach in the Bayesian framework that innovates over existing model-based clustering approaches, such as the Dirichlet multinomial mixture model, in three key respects: we incorporate feature selection, learn the appropriate number of clusters from the data, and integrate information on the tree structure relating the observed features. We compare the performance of our proposed method to existing methods on simulated data designed to mimic real microbiome data. We then illustrate results obtained for our motivating dataset, a clinical study aimed at characterizing the tumour microbiome of pancreatic cancer patients.
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U2 - 10.1093/jrsssc/qlac002
DO - 10.1093/jrsssc/qlac002
M3 - Article
C2 - 37034187
AN - SCOPUS:85182714893
SN - 0035-9254
VL - 72
SP - 20
EP - 36
JO - Journal of the Royal Statistical Society. Series C: Applied Statistics
JF - Journal of the Royal Statistical Society. Series C: Applied Statistics
IS - 1
ER -