ParticleMDI: particle Monte Carlo methods for the cluster analysis of multiple datasets with applications to cancer subtype identification

Cunningham, Nathan; Griffin, Jim E.; Wild, David L.

doi:10.1007/s11634-020-00401-y

Cited by 6 publications

(4 citation statements)

References 30 publications

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“…We adopt an overall structure based on the ‘common process model – dual observation model’ structure (Figure 1) used by Rasmussen et. al (31) and validated by many particle filtering methods outside of the context of infectious disease (41,42). The data inputs to this model are case incidence, a time-scaled phylogenetic tree constructed from virus genomic sequences, or both datasets together.…”

Section: Methodsmentioning

confidence: 99%

EpiFusion: Joint inference of the effective reproduction number by integrating phylodynamic and epidemiological modelling with particle filtering

Judge,

Vaughan,

Russell

et al. 2023

Preprint

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Accurately estimating the effective reproduction number (Rt) of a circulating pathogen is a fundamental challenge in the study of infectious disease. The fields of epidemiology and pathogen phylodynamics both share this goal, but to date, methodologies and data employed by each remain largely distinct. Here we present EpiFusion: a joint approach that can be used to harness the complementary strengths of each field to improve estimation of outbreak dynamics for large and poorly sampled epidemics, such as arboviral or respiratory outbreaks, and validate it for retrospective analysis. We propose a model of Rt that estimates outbreak trajectories conditional upon both phylodynamic (time-scaled trees estimated from genetic sequences) and epidemiological (case incidence) data. We simulate stochastic outbreak trajectories that are weighted according to epidemiological and phylodynamic observation models and fit using particle Markov Chain Monte Carlo. To assess performance, we test EpiFusion on simulated outbreaks in which transmission and/or surveillance rapidly changes and find that using EpiFusion to combine epidemiological and phylodynamic data maintains accuracy and increases certainty in trajectory and Rt estimates, compared to when each data type is used alone. Finally, we benchmark EpiFusion’s performance against existing methods to estimate Rt and demonstrate advances in efficiency and accuracy. Importantly, our approach scales efficiently with dataset size, including the use of phylogenetic trees generated from large genomic datasets. EpiFusion is designed to accommodate future extensions that will improve its utility, such as introduction of population structure, accommodations for phylogenetic uncertainty, and the ability to weight the contributions of genomic or case incidence to the inference.Author SummaryUnderstanding infectious disease spread is fundamental to protecting public health, but can be challenging as disease spread is a phenomenon that cannot be directly observed. So, epidemiologists use data in conjunction with mathematical models to estimate disease dynamics. Often, combinations of different models and data can be used to answer the same questions – for example ‘traditional’ epidemiology commonly uses case incidence data (the number of people who have tested positive for a disease at a certain time) whereas phylodynamic models use pathogen genomic sequence data and our knowledge of their evolution to model disease population dynamics. Each of these approaches have strengths and limitations, and data of each type can be sparse or biased, particularly in rapidly developing outbreaks or lower-middle income countries. An increasing number of approaches attempt to fix this problem by incorporating diverse concepts and data types together in their models. We aim to contribute to this movement by introducing EpiFusion, a modelling framework that makes improvements on efficiency and temporal resolution. EpiFusion uses particle filtering to simulate epidemic trajectories over time and weight their likelihood according to both case incidence data and a phylogenetic tree using separate observation models, resulting in the inference of trajectories in agreement with both sets of data. Improvements in our ability to accurately and confidently model pathogen spread help us to respond to infectious disease outbreaks and improve public health.

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Section: Methodsmentioning

confidence: 99%

EpiFusion: Joint inference of the effective reproduction number by integrating phylodynamic and epidemiological modelling with particle filtering

Judge,

Vaughan,

Russell

et al. 2023

Preprint

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“…Among computational tools, cluster analysis plays a key role, identifying subgroups of similar patients [6,7], investigating risk factors within each subgroup [8], and analyzing the heterogeneity of patients' response to the therapeutic treatments [9]. In particular, cluster analysis was recently adopted to study type 2 diabetes [10], chronic kidney disease (CKD) patients [11], and diabetic kidney disease (DKD) patients [12].…”

Section: Introductionmentioning

confidence: 99%

Exploring Heterogeneity with Category and Cluster Analyses for Mixed Data

Distefano

Mannone

Poli

2023

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Precision medicine aims to overcome the traditional one-model-fits-the-whole-population approach that is unable to detect heterogeneous disease patterns and make accurate personalized predictions. Heterogeneity is particularly relevant for patients with complications of type 2 diabetes, including diabetic kidney disease (DKD). We focus on a DKD longitudinal dataset, aiming to find specific subgroups of patients with characteristics that have a close response to the therapeutic treatment. We develop an approach based on some particular concepts of category theory and cluster analysis to explore individualized modelings and achieving insights onto disease evolution. This paper exploits the visualization tools provided by category theory, and bridges category-based abstract works and real datasets. We build subgroups deriving clusters of patients at different time points, considering a set of variables characterizing the state of patients. We analyze how specific variables affect the disease progress, and which drug combinations are more effective for each cluster of patients. The retrieved information can foster individualized strategies for DKD treatment.

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“…In addition, due to the accumulation of multi‐omics dataset (e.g. RNA‐Seq, DNA methylation and copy number variation) in public data repositories, other methods (Cunningham et al, 2020; Huo et al, 2017; Shen et al, 2009; Wang et al, 2014) sought to identify disease subtypes via integrating multi‐omics data.…”

Section: Introductionmentioning

confidence: 99%

Outcome-Guided Sparse K-Means for Disease Subtype Discovery via Integrating Phenotypic Data with High-Dimensional Transcriptomic Data

Meng

Avram

Tseng

et al. 2022

Journal of the Royal Statistical Society Series C: Applied Statistics

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The discovery of disease subtypes is an essential step for developing precision medicine, and disease subtyping via omics data has become a popular approach. While promising, subtypes obtained from existing approaches are not necessarily associated with clinical outcomes.With the rich clinical data along with the omics data in modern epidemiology cohorts, it is urgent to develop an outcome-guided clustering algorithm to fully integrate the phenotypic data with the high-dimensional omics data. Hence, we extended a sparse K-means method to an outcome-guided sparse K-means (GuidedSparseKmeans) method. An unified objective function was proposed, which was comprised of (i) weighted K-means to perform sample clusterings; (ii) lasso regularizations to perform gene selection from the high-dimensional omics data; and (iii) incorporation of a phenotypic variable from the clinical dataset to facilitate biologically meaningful clustering results. By iteratively optimizing the objective function, we will simultaneously obtain a phenotype-related sample clustering results and gene selection results. We demonstrated the superior performance of the GuidedSparseKmeans by comparing with existing clustering methods in simulations and applications of high-dimensional transcriptomic data of breast cancer and Alzheimer's disease. Our 352

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ParticleMDI: particle Monte Carlo methods for the cluster analysis of multiple datasets with applications to cancer subtype identification

Cited by 6 publications

References 30 publications

EpiFusion: Joint inference of the effective reproduction number by integrating phylodynamic and epidemiological modelling with particle filtering

EpiFusion: Joint inference of the effective reproduction number by integrating phylodynamic and epidemiological modelling with particle filtering

Exploring Heterogeneity with Category and Cluster Analyses for Mixed Data

Outcome-Guided Sparse K-Means for Disease Subtype Discovery via Integrating Phenotypic Data with High-Dimensional Transcriptomic Data

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