Penalized Clustering of Large-Scale Functional Data With Multiple Covariates

Ma, Ping; Zhong, Wenxuan

doi:10.1198/016214508000000247

Cited by 44 publications

(39 citation statements)

References 37 publications

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“…Another extension would be to take a semi-supervised approach, where some of the group memberships are known; this could be achieved in a very similar fashion to the model-based classification approaches of Dean et al (2006) and McNicholas (2010). Finally, a detailed comparison of the modelbased approach used herein to the penalized clustering method of Ma and Zhong (2008) would be of interest.…”

Section: Discussionmentioning

confidence: 99%

A Pseudo-EM Algorithm for Clustering Incomplete Longitudinal Data

Shaikh¹,

McNicholas²,

Desmond³

2010

The International Journal of Biostatistics

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A method for clustering incomplete longitudinal data, and gene expression time course data in particular, is presented. Specifically, an existing method that utilizes mixtures of multivariate Gaussian distributions with modified Cholesky-decomposed covariance structure is extended to accommodate incomplete data. Parameter estimation is carried out in a fashion that is similar to an expectation-maximization algorithm. We focus on the particular application of clustering incomplete gene expression time course data. In this application, our approach gives good clustering performance when compared to the results when there is no missing data. Possible extensions of this work are also suggested.

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Section: Discussionmentioning

confidence: 99%

A Pseudo-EM Algorithm for Clustering Incomplete Longitudinal Data

Shaikh¹,

McNicholas²,

Desmond³

2010

The International Journal of Biostatistics

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“…To overcome this problem Ma, Castillo-Davis, Zhong, and Liu (2006) model µ c (t) using smoothing splines, incorporating a penalty to the optimization criterion as shown in (4) and cluster the expression profiles using the rejection-controlled EM (RCEM) algorithm. Ma and Zhong (2008) extend this to incorporate additional covariate effects into the clustering algorithm. Wang et al (2008) propose an agglomerative clustering algorithm for functional data based on a new similarity measure and compare the results with many other clustering approaches such as k-means, self-organizing maps, smoothing spline-based clustering using ssclust (see Ma et al, 2006), Gaussian finite mixture model-based clustering using mclust (see Raftery, 2002, Fraley andRaftery, 2006), etc.…”

Section: Clusteringmentioning

confidence: 99%

Analyzing Time-Course Microarray Data Using Functional Data Analysis - A Review

Coffey¹,

Hinde²

2011

Statistical Applications in Genetics and Molecular Biology

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Gene expression over time can be viewed as a continuous process and therefore represented as a continuous curve or function. Functional data analysis (FDA) is a statistical methodology used to analyze functional data that has become increasingly popular in the analysis of time-course gene expression data. Several FDA techniques have been applied to gene expression profiles including functional regression analysis (to describe the relationship between expression profiles and other covariate(s)), functional discriminant analysis (to discriminate and classify groups of genes) and functional principal components analysis (for dimension reduction and clustering). This paper reviews the use of FDA and its associated methods to analyze time-course microarray gene expression data.

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“…different diseases). To account for time dependency of the gene expression measurements over time and the noisy nature of microarray data, standard regression models such as mixed-effects models are normally used in analyzing these data (Luan & Li, 2003;Ng et al, 2006;Archer & Guennel, 2006;Ma et al, 2006;Ma & Zhong, 2008). However, these models have some drawbacks: they depend on strong distributional assumptions, they require a formal specification of the random part of the model and they ignore possible outliers.…”

Section: Introductionmentioning

confidence: 99%

M-quantile Regression Analysis of Temporal Gene Expression Data

Vinciotti¹,

Yu²

2009

Statistical Applications in Genetics and Molecular Biology

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In this paper, we explore the use of M-regression and M-quantile coefficients to detect statistical differences between temporal curves that belong to different experimental conditions. In particular, we consider the application of temporal gene expression data. Here, the aim is to detect genes whose temporal expression is significantly different across a number of biological conditions. We present a new method to approach this problem. Firstly, the temporal profiles of the genes are modelled by a parametric M-quantile regression model. This model is particularly appealing to small-sample gene expression data, as it is very robust against outliers and it does not make any assumption on the error distribution. Secondly, we further increase the robustness of the method by summarising the M-quantile regression models for a large range of quantile values into an M-quantile coefficient. Finally, we employ a Hotelling T 2 -test to detect significant differences of the temporal M-quantile profiles across conditions. Simulated data shows the increased robustness of M-quantile regression methods over standard regression methods. We conclude by using the method to detect differentially expressed genes from time-course microarray data on muscular dystrophy.

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Penalized Clustering of Large-Scale Functional Data With Multiple Covariates

Cited by 44 publications

References 37 publications

A Pseudo-EM Algorithm for Clustering Incomplete Longitudinal Data

A Pseudo-EM Algorithm for Clustering Incomplete Longitudinal Data

Analyzing Time-Course Microarray Data Using Functional Data Analysis - A Review

M-quantile Regression Analysis of Temporal Gene Expression Data

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