scholarly journals flowEMMi: an automated model-based clustering tool for microbial cytometric data

2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Joachim Ludwig ◽  
Christian Höner zu Siederdissen ◽  
Zishu Liu ◽  
Peter F. Stadler ◽  
Susann Müller
Keyword(s):  
Microbial Community ◽  
Gaussian Mixture Models ◽  
Gaussian Mixture ◽  
Medical Diagnostics ◽  
Main Concern ◽  
Accurate Identification ◽  
Technical Noise ◽  
Running Time ◽  
Model Based Clustering ◽  
Model Based

Abstract Background Flow cytometry (FCM) is a powerful single-cell based measurement method to ascertain multidimensional optical properties of millions of cells. FCM is widely used in medical diagnostics and health research. There is also a broad range of applications in the analysis of complex microbial communities. The main concern in microbial community analyses is to track the dynamics of microbial subcommunities. So far, this can be achieved with the help of time-consuming manual clustering procedures that require extensive user-dependent input. In addition, several tools have recently been developed by using different approaches which, however, focus mainly on the clustering of medical FCM data or of microbial samples with a well-known background, while much less work has been done on high-throughput, online algorithms for two-channel FCM. Results We bridge this gap with , a model-based clustering tool based on multivariate Gaussian mixture models with subsampling and foreground/background separation. These extensions provide a fast and accurate identification of cell clusters in FCM data, in particular for microbial community FCM data that are often affected by irrelevant information like technical noise, beads or cell debris. outperforms other available tools with regard to running time and information content of the clustering results and provides near-online results and optional heuristics to reduce the running-time further. Conclusions is a useful tool for the automated cluster analysis of microbial FCM data. It overcomes the user-dependent and time-consuming manual clustering procedure and provides consistent results with ancillary information and statistical proof.

scholarly journals flowEMMi: An automated model-based clustering tool for microbial cytometric data

2019 ◽  
Author(s):  
Joachim Ludwig ◽  
Christian Höner zu Siederdissen ◽  
Zishu Liu ◽  
Peter F Stadler ◽  
Susann Müller
Keyword(s):  
Microbial Community ◽  
Gaussian Mixture Models ◽  
Gaussian Mixture ◽  
Medical Diagnostics ◽  
Main Concern ◽  
Accurate Identification ◽  
Technical Noise ◽  
Running Time ◽  
Model Based Clustering ◽  
Model Based

AbstractBackgroundFlow cytometry (FCM) is a powerful single-cell based measurement method to ascertain multidimensional optical properties of millions of cells. FCM is widely used in medical diagnostics and health research. There is also a broad range of applications in the analysis of complex microbial communities. The main concern in microbial community analyses is to track the dynamics of microbial subcommunities. So far, this can be achieved with the help of time-consuming manual clustering procedures that require extensive user-dependent input. In addition, several tools have recently been developed by using different approaches which, however, focus mainly on the clustering of medical FCM data or of microbial samples with a well-known background, while much less work has been done on high-throughput, online algorithms for two-channel FCM.ResultsWe bridge this gap with flowEMMi, a model-based clustering tool based on multivariate Gaussian mixture models with subsampling and foreground/background separation. These extensions provide a fast and accurate identification of cell clusters in FCM data, in particular for microbial community FCM data that are often affected by irrelevant information like technical noise, beads or cell debris. flowEMMi outperforms other available tools with regard to running time and information content of the clustering results and provides near-online results and optional heuristics to reduce the running-time further.ConclusionsflowEMMi is a useful tool for the automated cluster analysis of microbial FCM data. It overcomes the user-dependent and time-consuming manual clustering procedure and provides consistent results with ancillary information and statistical proof.

Gaussian mixture copulas for high-dimensional clustering and dependency-based subtyping

2019 ◽  
Author(s):  
Siva Rajesh Kasa ◽  
Sakyajit Bhattacharya ◽  
Vaibhav Rajan
Keyword(s):  
Survival Rates ◽  
Gaussian Mixture Models ◽  
Gaussian Mixture ◽  
Supplementary Information ◽  
High Dimensional ◽  
Clustering Methods ◽  
Mortality And Morbidity ◽  
Model Based Clustering ◽  
Cancer Data ◽  
Model Based

Abstract Motivation The identification of sub-populations of patients with similar characteristics, called patient subtyping, is important for realizing the goals of precision medicine. Accurate subtyping is crucial for tailoring therapeutic strategies that can potentially lead to reduced mortality and morbidity. Model-based clustering, such as Gaussian mixture models, provides a principled and interpretable methodology that is widely used to identify subtypes. However, they impose identical marginal distributions on each variable; such assumptions restrict their modeling flexibility and deteriorates clustering performance. Results In this paper, we use the statistical framework of copulas to decouple the modeling of marginals from the dependencies between them. Current copula-based methods cannot scale to high dimensions due to challenges in parameter inference. We develop HD-GMCM, that addresses these challenges and, to our knowledge, is the first copula-based clustering method that can fit high-dimensional data. Our experiments on real high-dimensional gene-expression and clinical datasets show that HD-GMCM outperforms state-of-the-art model-based clustering methods, by virtue of modeling non-Gaussian data and being robust to outliers through the use of Gaussian mixture copulas. We present a case study on lung cancer data from TCGA. Clusters obtained from HD-GMCM can be interpreted based on the dependencies they model, that offers a new way of characterizing subtypes. Empirically, such modeling not only uncovers latent structure that leads to better clustering but also meaningful clinical subtypes in terms of survival rates of patients. Availability and implementation An implementation of HD-GMCM in R is available at: https://bitbucket.org/cdal/hdgmcm/. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Massive MIMO Codebook Design Using Gaussian Mixture Model Based Clustering

2022 ◽  
Vol 32 (1) ◽  
pp. 361-375
Author(s):  
S. Markkandan ◽  
S. Sivasubramanian ◽  
Jaison Mulerikkal ◽  
Nazeer Shaik ◽  
Beulah Jackson ◽  
...  
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Massive Mimo ◽  
Gaussian Mixture ◽  
Model Based Clustering ◽  
Codebook Design ◽  
Model Based

scholarly journals Regularized Parameter Estimation in High-Dimensional Gaussian Mixture Models

2011 ◽  
Vol 23 (6) ◽  
pp. 1605-1622 ◽  
Author(s):  
Lingyan Ruan ◽  
Ming Yuan ◽  
Hui Zou
Keyword(s):  
Parameter Estimation ◽  
Mixture Models ◽  
Gaussian Mixture Models ◽  
Expectation Maximization Algorithm ◽  
Real Data ◽  
Gaussian Mixture ◽  
High Dimensional ◽  
Model Based Clustering ◽  
Text Type ◽  
Effective Dimensionality

Finite gaussian mixture models are widely used in statistics thanks to their great flexibility. However, parameter estimation for gaussian mixture models with high dimensionality can be challenging because of the large number of parameters that need to be estimated. In this letter, we propose a penalized likelihood estimator to address this difficulty. The [Formula: see text]-type penalty we impose on the inverse covariance matrices encourages sparsity on its entries and therefore helps to reduce the effective dimensionality of the problem. We show that the proposed estimate can be efficiently computed using an expectation-maximization algorithm. To illustrate the practical merits of the proposed method, we consider its applications in model-based clustering and mixture discriminant analysis. Numerical experiments with both simulated and real data show that the new method is a valuable tool for high-dimensional data analysis.

scholarly journals BinSanity: unsupervised clustering of environmental microbial assemblies using coverage and affinity propagation

PeerJ ◽  
2017 ◽  
Vol 5 ◽  
pp. e3035 ◽  
Author(s):  
Elaina D. Graham ◽  
John F. Heidelberg ◽  
Benjamin J. Tully
Keyword(s):  
Neural Networks ◽  
Microbial Community ◽  
Mixture Models ◽  
Clustering Algorithm ◽  
Gaussian Mixture Models ◽  
Gc Content ◽  
Gaussian Mixture ◽  
Affinity Propagation ◽  
Adjusted Rand Index ◽  
Low Coverage

Metagenomics has become an integral part of defining microbial diversity in various environments. Many ecosystems have characteristically low biomass and few cultured representatives. Linking potential metabolisms to phylogeny in environmental microorganisms is important for interpreting microbial community functions and the impacts these communities have on geochemical cycles. However, with metagenomic studies there is the computational hurdle of ‘binning’ contigs into phylogenetically related units or putative genomes. Binning methods have been implemented with varying approaches such as k-means clustering, Gaussian mixture models, hierarchical clustering, neural networks, and two-way clustering; however, many of these suffer from biases against low coverage/abundance organisms and closely related taxa/strains. We are introducing a new binning method, BinSanity, that utilizes the clustering algorithm affinity propagation (AP), to cluster assemblies using coverage with compositional based refinement (tetranucleotide frequency and percent GC content) to optimize bins containing multiple source organisms. This separation of composition and coverage based clustering reduces bias for closely related taxa. BinSanity was developed and tested on artificial metagenomes varying in size and complexity. Results indicate that BinSanity has a higher precision, recall, and Adjusted Rand Index compared to five commonly implemented methods. When tested on a previously published environmental metagenome, BinSanity generated high completion and low redundancy bins corresponding with the published metagenome-assembled genomes.

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