Functional Clustering Algorithm for High‐Dimensional Proteomics Data

Bensmail, Halima; Aruna, B.; Semmes, O. John; Haoudi, Abdelali

doi:10.1155/jbb.2005.80

Cited by 7 publications

(4 citation statements)

References 18 publications

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“…These dimension reduction approaches can be modified to analyze and cluster high-dimensional protein profiles, although several statistical issues related to curve parameter estimation and longitudinal covariance modeling should be resolved [ 41 ]. In particular, when the number of proteins is largely smaller than the number of protein peaks, Bensmail et al proposed an alternative hierarchical clustering algorithm based on a dissimilarity measure combined with a functional data analysis [ 28 ]. This alternative can also allow functional smoothing of proteomics expression profiles or spectra.…”

Section: Discussionmentioning

confidence: 99%

“…In addition, protein data have their unique feature, i.e., a protein may appear in a spectrum typically with thousands of peaks, leading to the number of samples largely smaller than the number of protein peaks. A variety of clustering approaches have been developed to handle such high-dimensional complexity of proteomics data [ 24 - 28 ]. The purpose of this article is to implement functional clustering into analysis and modeling of proteome dynamics.…”

Section: Introductionmentioning

confidence: 99%

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A Computational Algorithm for Functional Clustering of Proteome Dynamics During Development

Wang

Han

et al. 2014

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Phenotypic traits, such as seed development, are a consequence of complex biochemical interactions among genes, proteins and metabolites, but the underlying mechanisms that operate in a coordinated and sequential manner remain elusive. Here, we address this issue by developing a computational algorithm to monitor proteome changes during the course of trait development. The algorithm is built within the mixture-model framework in which each mixture component is modeled by a specific group of proteins that display a similar temporal pattern of expression in trait development. A nonparametric approach based on Legendre orthogonal polynomials was used to fit dynamic changes of protein expression, increasing the power and flexibility of protein clustering. By analyzing a dataset of proteomic dynamics during early embryogenesis of the Chinese fir, the algorithm has successfully identified several distinct types of proteins that coordinate with each other to determine seed development in this forest tree commercially and environmentally important to China. The algorithm will find its immediate applications for the characterization of mechanistic underpinnings for any other biological processes in which protein abundance plays a key role.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Computational Algorithm for Functional Clustering of Proteome Dynamics During Development

Wang

Han

et al. 2014

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“…Each serum sample was fractionated to increase the peak resolution in SELDI spectra and normalized peak information from each of the fractions merged into an input data matrix that included 48 samples and 168 feature (predictor) vectors. Such small datasets are often a reality in biomedical research, since obtaining large number of serum samples for many diseases can be difficult and expensive [38].…”

Section: Discussionmentioning

confidence: 99%

Biomarkers that Discriminate Multiple Myeloma Patients with or without Skeletal Involvement Detected Using SELDI‐TOF Mass Spectrometry and Statistical and Machine Learning Tools

2006

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Multiple Myeloma (MM) is a severely debilitating neoplastic disease of B cell origin, with the primary source of morbidity and mortality associated with unrestrained bone destruction. Surface enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS) was used to screen for potential biomarkers indicative of skeletal involvement in patients with MM. Serum samples from 48 MM patients, 24 with more than three bone lesions and 24 with no evidence of bone lesions were fractionated and analyzed in duplicate using copper ion loaded immobilized metal affinity SELDI chip arrays. The spectra obtained were compiled, normalized, and mass peaks with mass-to-charge ratios (m/z) between 2000 and 20,000 Da identified. Peak information from all fractions was combined together and analyzed using univariate statistics, as well as a linear, partial least squares discriminant analysis (PLS-DA), and a non-linear, random forest (RF), classification algorithm. The PLS-DA model resulted in prediction accuracy between 96–100%, while the RF model was able to achieve a specificity and sensitivity of 87.5% each. Both models as well as multiple comparison adjusted univariate analysis identified a set of four peaks that were the most discriminating between the two groups of patients and hold promise as potential biomarkers for future diagnostic and/or therapeutic purposes.

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“…In this article, we focus on the nonsupervised task which consists in finding groups of individuals whose proteomic landscape is similar. Surprisingly the clustering task received less attention, and is mainly based on hierarchical clustering on the set of peaks detected across spectra (Bensmail et al ; Morris et al ). However, such method is known to depend heavily on the peak detection method and has the strong disadvantage to neglect the interindividual variability whereas this information should be central for subgroup discovery.…”

Section: Introductionmentioning

confidence: 99%

Wavelet‐Based Clustering for Mixed‐Effects Functional Models in High Dimension

et al. 2013

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Summary We propose a method for high‐dimensional curve clustering in the presence of interindividual variability. Curve clustering has longly been studied especially using splines to account for functional random effects. However, splines are not appropriate when dealing with high‐dimensional data and can not be used to model irregular curves such as peak‐like data. Our method is based on a wavelet decomposition of the signal for both fixed and random effects. We propose an efficient dimension reduction step based on wavelet thresholding adapted to multiple curves and using an appropriate structure for the random effect variance, we ensure that both fixed and random effects lie in the same functional space even when dealing with irregular functions that belong to Besov spaces. In the wavelet domain our model resumes to a linear mixed‐effects model that can be used for a model‐based clustering algorithm and for which we develop an EM‐algorithm for maximum likelihood estimation. The properties of the overall procedure are validated by an extensive simulation study. Then, we illustrate our method on mass spectrometry data and we propose an original application of functional data analysis on microarray comparative genomic hybridization (CGH) data. Our procedure is available through the R package curvclust which is the first publicly available package that performs curve clustering with random effects in the high dimensional framework (available on the CRAN).

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Functional Clustering Algorithm for High‐Dimensional Proteomics Data

Cited by 7 publications

References 18 publications

A Computational Algorithm for Functional Clustering of Proteome Dynamics During Development

A Computational Algorithm for Functional Clustering of Proteome Dynamics During Development

Biomarkers that Discriminate Multiple Myeloma Patients with or without Skeletal Involvement Detected Using SELDI‐TOF Mass Spectrometry and Statistical and Machine Learning Tools

Wavelet‐Based Clustering for Mixed‐Effects Functional Models in High Dimension

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