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SC-MEB: spatial clustering with hidden Markov random field using empirical Bayes

BRIEFINGS IN BIOINFORMATICS 2022年第1期23卷 bbab466-bbab466页

作者： Yang, Yi Shi, Xingjie Liu, Wei Zhou, Qiuzhong Lau, Mai Chan Lim, Jeffrey Chun Tatt Sun, Lei Ng, Cedric Chuan Young Yeong, Joe Liu, Jin Duke NUS Med Sch Hlth Serv & Syst Res Program Singapore 16985 Singapore East China Normal Univ Acad Stat & Interdisciplinary Sci Shanghai Peoples R China Duke NUS Med Sch Vasc & Metab Disorder Program Singapore Singapore ASTAR IMCB Singapore Singapore Duke NUS Med Sch Cardiovasc & Metab Disorders Program Singapore Singapore Natl Canc Ctr Singapore Canc Discovery Hub Singapore Singapore

Spatial transcriptomics has been emerging as a powerful technique for resolving gene expression profiles while retaining tissue spatial information. These spatially resolved transcriptomics make it feasible to examine the complex multicellular systems of different microenvironments. To answer scientific questions with spatial transcriptomics and expand our understanding of how cell types and states are regulated by microenvironment, the first step is to identify cell clusters by integrating the available spatial information. Here, we introduce SC-MEB, an empirical Bayes approach for spatial clustering analysis using a hidden Markov random field. We have also derived an efficient expectation-maximization algorithm based on an iterative conditional mode for SC-MEB. In contrast to BayesSpace, a recently developed method, SC-MEB is not only computationally efficient and scalable to large sample sizes but is also capable of choosing the smoothness parameter and the number of clusters. We performed comprehensive simulation studies to demonstrate the superiority of SC-MEB over some existing methods. We applied SC-MEB to analyze the spatial transcriptome of human dorsolateral prefrontal cortex tissues and mouse hypothalamic preoptic region. Our analysis results showed that SC-MEB can achieve a similar or better clustering performance to BayesSpace, which uses the true number of clusters and a fixed smoothness parameter. Moreover, SC-MEB is scalable to large 'sample sizes'. We then employed SC-MEB to analyze a colon dataset from a patient with colorectal cancer (CRC) and COVID-19, and further performed differential expression analysis to identify signature genes related to the clustering results. The heatmap of identified signature genes showed that the clusters identified using SC-MEB were more separable than those obtained with BayesSpace. Using pathway analysis, we identified three immune-related clusters, and in a further comparison, found the mean expression of COVID-19

关键词： spatial transcriptomics cell phenotype empirical Bayes hidden Markov random field expectation-maximization algorithm

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Robust EM Continual Reassessment Method in Oncology Dose Finding

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JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION 2011年第495期106卷 818-831页

作者： Yuan, Ying Yin, Guosheng Univ Texas MD Anderson Canc Ctr Dept Biostat Unit 1411 Houston TX 77230 USA Univ Hong Kong Dept Stat & Actuarial Sci Hong Kong Hong Kong Peoples R China

The continual reassessment method (CRM) is a commonly used dose-finding design for phase I clinical trials. Practical applications of this method have been restricted by two limitations: (1) the requirement that the toxicity outcome needs to be observed shortly after the initiation of the treatment;and (2) the potential sensitivity to the prespecified toxicity probability at each dose. To overcome these limitations, we naturally treat the unobserved toxicity outcomes as missing data, and use the expectation-maximization (EM) algorithm to estimate the dose toxicity probabilities based on the incomplete data to direct dose assignment. To enhance the robustness of the design, we propose prespecifying multiple sets of toxicity probabilities, each set corresponding to an individual CRM model. We carry out these multiple CRMs in parallel, across which model selection and model averaging procedures are used to make more robust inference. We evaluate the operating characteristics of the proposed robust EM-CRM designs through simulation studies and show that the proposed methods satisfactorily resolve both limitations of the CRM. Besides improving the MTD selection percentage, the new designs dramatically shorten the duration of the trial, and are robust to the prespecification of the toxicity probabilities.

关键词： Adaptive design expectation-maximization algorithm Late-onset toxicity Maximum tolerated dose Missing data Model averaging Model selection

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Variable selection via penalized quasi-maximum likelihood method for spatial autoregressive model with missing response

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SPATIAL STATISTICS 2024年 59卷

作者： Wang, Yuanfeng Song, Yunquan China Univ Petr Coll Sci Qingdao 266580 Peoples R China

Spatial autoregressive model is widely concerned in the economic field, whereas when the data is missing, variable selection and parameter estimation of the model is quite challenging. Based on this, we discuss the variable selection in spatial autoregressive model with missing data. Under the condition that errors are independent and identically distributed, we have developed a penalized quasi -maximum likelihood method to achieve variable selection and parameter estimation simultaneously in the presence of missing responses. The method's theoretical properties, including consistency and asymptotical normality, are established under certain assumptions. Meanwhile, an improved expectation-maximization algorithm is provided for optimizing the penalized quasi -maximum likelihood function. Simulations are conducted to examine the proposed method and assess the finite -sample performance. Additionally, we present a practical example to illustrate the method's application.

关键词： Spatial autoregressive models Quasi-maximum likelihood function expectation-maximization algorithm Missing data Variable selection

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A NEW MULTILEVEL THRESHOLDING APPROACH BASED ON THE ANT COLONY SYSTEM AND THE EM algorithm

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INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL 2013年第1期9卷 319-337页

作者： Liang, Yun-Chia Yin, Yueh-Chuan Yuan Ze Univ Dept Ind Engn & Management 135 Yuan Tung Rd Chungli 320 Taoyuan County Taiwan

Thresholding, an important approach for image segmentation, is the first step in the image processing for many industrial applications. The efficiency and effectiveness of the thresholding method is the key to the success of the consecutive process steps. This study proposed an optimization algorithm (named as AOE) combining parametric and non-parametric approaches. An ant colony system (ACS-Otsu) algorithm considers the non-parametric objective between-class variance while the expectation maximization (EM) algorithm focuses on the parametric objective overall fitting error of probability distributions. Since the performance of the EM method is sensitive to the initial solution, the ACS-Otsu algorithm is employed as a robust and efficient initialization strategy. Experimental results of the nine test images show that the AOE algorithm is efficient and effective in the multilevel thresholding problems. Comparisons between the AOE algorithm and the PSO+EM algorithm in the literature also verify that the AOE algorithm not only provides competitive thresholding results but also outperforms PSO+EM in computational expense.

关键词： Image segmentation Multilevel thresholding expectation-maximization algorithm Ant colony system

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Stochastic Short-term High-resolution Prediction of Solar Irradiance and Photovoltaic Power Output

Stochastic Short-term High-resolution Prediction of Solar Ir...

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North American Power Symposium (NAPS)

作者： Olama, Mohammed Melin, Alex Dong, Jin Djouadi, Seddik Zhang, Yichen Oak Ridge Natl Lab Computat Sci & Engn Div Oak Ridge TN 37831 USA Oak Ridge Natl Lab Elect & Elect Syst Res Div Oak Ridge TN 37831 USA Oak Ridge Natl Lab Energy & Transportat Sci Div Oak Ridge TN 37831 USA Univ Tennessee Dept Elect Engn & Comp Sci Knoxville TN 37996 USA

ISBN: (纸本)9781538626993

The increased penetration of solar photovoltaic (PV) energy sources into electric grids has increased the need for accurate modeling and prediction of solar irradiance and power production. Existing modeling and prediction techniques focus on long-term low-resolution prediction over minutes to years. This paper examines the stochastic modeling and short-term high-resolution prediction of solar irradiance and PV power output. We propose a stochastic state-space model to characterize the behaviors of solar irradiance and PV power output. This prediction model is suitable for the development of optimal power controllers for PV sources. A filter-based expectation-maximization and Kalman filtering mechanism is employed to estimate the parameters and states in the statespace model. The mechanism results in a finite dimensional filter which only uses the first and second order statistics. The structure of the scheme contributes to a direct prediction of the solar irradiance and PV power output without any linearization process or simplifying assumptions of the signal's model. This enables the system to accurately predict small as well as large fluctuations of the solar signals. The mechanism is recursive allowing the solar irradiance and PV power to be predicted online from measurements. The mechanism is tested using solar irradiance and PV power measurement data collected locally in our laboratory.

关键词： Photovoltaics solar variability distributed energy resources stochastic prediction state-space model Kalman filter expectation-maximization algorithm

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Semi-supervised logistic learning based on exponential tilt mixture models

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STAT 2020年第1期9卷

作者： Zhang, Xinwei Tan, Zhiqiang Rutgers State Univ Dept Stat Piscataway NJ 08854 USA

Consider semi-supervised learning for classification, where both labelled and unlabelled data are available for training. The goal is to exploit both datasets to achieve higher prediction accuracy than just using labelled data alone. We develop a semi-supervised logistic learning method based on exponential tilt mixture models by extending a statistical equivalence between logistic regression and exponential tilt modelling. We study maximum nonparametric likelihood estimation and derive novel objective functions that are shown to be Fisher probability consistent. We also propose regularized estimation and construct simple and highly interpretable expectation-maximization (EM) algorithms. Finally, we present numerical results that demonstrate the advantage of the proposed methods compared with existing methods.

关键词： empirical likelihood expectation-maximization algorithm exponential tilt model Fisher consistency logistic regression semi-supervised learning

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MULTIPLICATIVE UPDATES FOR MODELING MIXTURES OF NON-STATIONARY SIGNALS IN THE TIME-FREQUENCY DOMAIN

MULTIPLICATIVE UPDATES FOR MODELING MIXTURES OF NON-STATIONA...

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European Signal Processing Conference

作者： Roland Badeau Alexey Ozerov Institut Mines-Telecom Telecom ParisTech CNRS LTCI Technicolor Research & Innovation

We recently introduced the high-resolution nonnegative matrix factorization (HR-NMF) model for representing mixtures of non-stationary signals in the time-frequency domain, and we highlighted its capability to both reach a high spectral resolution and reconstruct high quality audio signals. An expectation-maximization (EM) algorithm was also proposed for estimating its parameters. In this paper, we replace the maximization step by multiplicative update rules (MUR), in order to improve the convergence rate. We also introduce general MUR that are not limited to nonnegative parameters, and we propose a new insight into the EM algorithm, which shows that MUR and EM actually belong to the same family. We thus introduce a continuum of algorithms between them. Experiments confirm that the proposed approach permits to overcome the convergence rate of the EM algorithm.

关键词： Nonnegative Matrix Factorization High Resolution methods expectation-maximization algorithm Multiplicative update rules

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Applying Training Hidden Features to Joint Curve Evolution for Brain MRI Segmentation

Applying Training Hidden Features to Joint Curve Evolution f...

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International Conference on Control, Automation, Robotics and Vision

作者： Mahshid Farzinfar Eam Khwang Teoh Zhong Xue School of Electrical Electronic Engineering Nanyang Technological University Singapore The Methodist Hospital Research Institute Weill Medical College of Cornell University Houston TX USA

ISBN: (纸本)9781424478149

According to the level of information provided in images, segmentation techniques can be categorized into two groups. One is region-labeling, which obeys the intensity-based classification methods. Although modeling the tissue intensity is straightforward by applying local statistical methods and spatial dependencies, the results might suffer from noise and incomplete data. The second group of techniques applies active contour models, in which the objective is to find the optimal partition of the image domain using a closed or open curve by using prior constraints on the shape variation. However, estimating optimal curve is intractable due to the incomplete observation data. This paper extends a previously reported joint active contour model for medical image segmentation in a new expectation-maximization (EM) framework, wherein the evolution curve is constrained not only by a shape-based statistical model but also by applying a hidden variable model from the image observation. In this approach, the hidden variable model is defined by the local voxel labeling computed from its likelihood function, depended on the image functions and the prior anatomical knowledge. Comparative results on segmenting putamen and caudate shapes in MR brain images confirmed both robustness and accuracy of the proposed curve evolution algorithm.

关键词： Statistical shape model active contours level set methods expectation-maximization algorithm

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A Generic Statistical Approach for Emission Computed Tomography Reconstruction

A Generic Statistical Approach for Emission Computed Tomogra...

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IEEE International Conference on Intelligent Computer Communication and Processing

作者： Anca Ciurte Sergiu Nedevschi Ioan Rasa Department of Computer Science Technical University of Cluj-Napoca Department of Mathematics Technical University of Cluj-Napoca

ISBN: (纸本)9781479914920

Nowadays, nuclear imaging is increasingly used for non-invasive diagnosis. The image modalities in nuclear imaging suffer of worse statistics, in comparison with computed tomography, since they are based on emission transition tomography. Thus, precise reconstruction methods that can deal with incomplete or missing measurements are needed in order to improve the quality of nuclear images. In this paper we present a generalization of the state of the art EMML and ISRA algorithms for emission computed tomography reconstruction. The proposed method was tested and validated in comparison with the mentioned state of the art methods on a set of synthetic data. Better results (in terms of speed of convergence) were obtained for certain parameter settings.

关键词： expectation-maximization algorithm Image reconstruction Positron emission tomography Kullback- Leibler distances Log-likelihood functions Least-squares Linear systems

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Statistical inference on group Rasch mixture network models

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STAT 2022年第1期11卷

作者： Long, Yuhang Huang, Tao Shanghai Univ Finance & Econ Sch Stat & Management Shanghai 200433 Peoples R China

In a two-mode network, the nodes are divided into two types (primary nodes and secondary nodes), and connections exist only between nodes of different types. In reality, in such a two-mode network, one-mode network connections may also exist among primary nodes, and these two kinds of networks are usually not independent and coexistent. In this paper, we first propose a group Rasch mixture network model that focuses on the connections between primary nodes and secondary nodes, while incorporating the group structure and linkage information of primary nodes. We then develop a modified expectation-maximization algorithm to estimate the proposed model with a lambda-BIC method for selecting the tuning parameter. Additionally, we provide a likelihood-ratio test statistic to examine whether the two kinds of networks are independent and implement the leave-one-out method to construct a network prediction rule. Finally, we establish asymptotic results and demonstrate the numerical performance of the proposed methods using both simulations and the *** dataset.

关键词： expectation-maximization algorithm likelihood-ratio test mixture model Rasch model

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