339 research outputs found
Statistical analysis of proteomics, metabolomics, and lipidomics data using mass spectrometry
No full textThis book presents an overview of computational and statistical design and analysis of mass spectrometry-based proteomics, metabolomics, and lipidomics data. This contributed volume provides an introduction to the special aspects of statistical design and analysis with mass spectrometry data for the new omic sciences. The text discusses common aspects of design and analysis between and across all (or most) forms of mass spectrometry, while also providing special examples of application with the most common forms of mass spectrometry. Also covered are applications of computational mass spectrometry not only in clinical study but also in the interpretation of omics data in plant biology studies. Omics research fields are expected to revolutionize biomolecular research by the ability to simultaneously profile many compounds within either patient blood, urine, tissue, or other biological samples. Mass spectrometry is one of the key analytical techniques used in these new omic sciences. Liquid chromatography mass spectrometry, time-of-flight data, and Fourier transform mass spectrometry are but a selection of the measurement platforms available to the modern analyst. Thus in practical proteomics or metabolomics, researchers will not only be confronted with new high dimensional data types—as opposed to the familiar data structures in more classical genomics—but also with great variation between distinct types of mass spectral measurements derived from different platforms, which may complicate analyses, comparison, and interpretation of results. Susmita Datta received her PhD in statistics from the University of Georgia. She is a tenured professor in the Department of Biostatistics at the University of Florida. Before joining the University of Florida she was a professor and a distinguished university scholar at the University of Louisville. She is a Fellow of the American Association for the Advancement of Science, American Statistical Association, and an elected member of the International Statistical Institute. She is past president of the Caucus for Women in Statistics, and she actively supports research and education for women in STEM fields. Bart Mertens received his PhD in statistical sciences from University College London, Department of Statistical Sciences, on statistical analysis methods for spectrometry data. He is currently Associate Professor at the Department of Medical Statistics and Bioinformatics of the Leiden University Medical Centre, where he has been working in both research and consulting for statistical analysis methodology with mass spectrometry proteomic data for more than 10 years
On star Rothberger spaces modulo an ideal
No full text[EN] In this article, we introduce the ideal star-Rothberger property by coupling the notion of a star operator to that of an ideal Rothberger space, after which some of its topological characteristics are analysed. By creating relationships between a numbers of topological features with structures similar to the ideal star-Rothberger space, we reinforce the concept. In order to illustrate the differences between a number of related topological properties, we also provide several counter examples. Certain preservation-related properties under subspaces and functions are investigated. Lastly we find a way to express ideal star-Rothberger space by means of families of closed sets by bringing some modifications to the SSI^I property. Sarkar, S.; Bal, P.; Datta, M. (2024). On star Rothberger spaces modulo an ideal. Applied General Topology. 25(2):407-414. https://doi.org/10.4995/agt.2024.20464OJS40741425
clValid: An R Package for Cluster Validation
No full textThe R package clValid contains functions for validating the results of a clustering analysis. There are three main types of cluster validation measures available, "internal", "stability", and "biological". The user can choose from nine clustering algorithms in existing R packages, including hierarchical, K-means, self-organizing maps (SOM), and model-based clustering. In addition, we provide a function to perform the self-organizing tree algorithm (SOTA) method of clustering. Any combination of validation measures and clustering methods can be requested in a single function call. This allows the user to simultaneously evaluate several clustering algorithms while varying the number of clusters, to help determine the most appropriate method and number of clusters for the dataset of interest. Additionally, the package can automatically make use of the biological information contained in the Gene Ontology (GO) database to calculate the biological validation measures, via the annotation packages available in Bioconductor. The function returns an object of S4 class "clValid", which has summary, plot, print, and additional methods which allow the user to display the optimal validation scores and extract clustering results.
Analyzing the Combination of Polymorphisms Associating with Antidepressant Response by Exact Conditional Test
No full textGenetic factors have been shown to be involved in etiology of a poor response to the antidepressant treatment with sufficient dosage and duration. Our goal was to identify the role of polymorphisms in the poor response to the treatment. To this end, 5 functional polymorphisms in 109 patients diagnosed with unipolar, major depressive disorder are analyzed. Due to the small sample size, exact conditional tests are utilized to analyze the contingency table. The data analysis involves: (1) Exact test for conditional independence in a high dimensional contingency table; (2) Marginal independence test; (3) Exact test for three-way interactions. The efficiency of program always limits the application of exact test. The appropriate methods for enumerating exact tables are the key to improve the efficiency of programs. The algorithm of enumerating the exact tables is also introduced
Mass Spectrometry Analysis Using MALDIquant
No full textMALDIquant and associated R packages provide a versatile and completely free open-source platform for analyzing 2D mass spectrometry data as generated, for instance, by MALDI and SELDI instruments. We first describe the various methods and algorithms available in MALDIquant. Subsequently, we illustrate a typical analysis workflow using MALDIquant by investigating an experimental cancer data set, starting from raw mass spectrometry measurements and ending at multivariate classification
ISSN:0974-276X JPB, an open access journal Journal of Proteomics & Bioinformatics- Open Access www.omicsonline.com Research Article JPB/Vol.2/May 2009 A Statistical Technique for Monoisotopic Peak Detection in a Mass Spectrum
No full textCopyright: © 2009 Atlas M, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Mass spectrometry has emerged as a core technology for high throughput proteomics profiling. It has enormous potential in biomedical research. However, the complexity of the data poses new statistical challenges for the analysis. Statistical methods and software developments for analyzing proteomic data are likely to continue to be a major area of research in the coming years. In this paper, a novel statistical method for analyzing high dimensional MALDI-TOF mass-spectrometry data in proteomic research is proposed. The chemical knowledge regarding isotopic distribution of the peptide molecules along with quantitative modeling is used to detect chemically valuable peaks from each spectrum. More specifically, a mixture of location-shifted Poisson distribution is fitted to the deamidated isotopic distribution of a peptide molecule. Maximum likelihood estimation by the expectation-maximization (EM) technique is used to estimate the parameters of the distribution. A formal statistical test is then constructed to determine whether a cluster of consecutive features (intensity values) in a mass spectrum corresponds to a true isotropic pattern. Thus, the monoisotopic peak
Methods for evaluating clustering algorithms for gene expression data using a reference set of functional classes
No full textAbstract Background A cluster analysis is the most commonly performed procedure (often regarded as a first step) on a set of gene expression profiles. In most cases, a post hoc analysis is done to see if the genes in the same clusters can be functionally correlated. While past successes of such analyses have often been reported in a number of microarray studies (most of which used the standard hierarchical clustering, UPGMA, with one minus the Pearson's correlation coefficient as a measure of dissimilarity), often times such groupings could be misleading. More importantly, a systematic evaluation of the entire set of clusters produced by such unsupervised procedures is necessary since they also contain genes that are seemingly unrelated or may have more than one common function. Here we quantify the performance of a given unsupervised clustering algorithm applied to a given microarray study in terms of its ability to produce biologically meaningful clusters using a reference set of functional classes. Such a reference set may come from prior biological knowledge specific to a microarray study or may be formed using the growing databases of gene ontologies (GO) for the annotated genes of the relevant species. Results In this paper, we introduce two performance measures for evaluating the results of a clustering algorithm in its ability to produce biologically meaningful clusters. The first measure is a biological homogeneity index (BHI). As the name suggests, it is a measure of how biologically homogeneous the clusters are. This can be used to quantify the performance of a given clustering algorithm such as UPGMA in grouping genes for a particular data set and also for comparing the performance of a number of competing clustering algorithms applied to the same data set. The second performance measure is called a biological stability index (BSI). For a given clustering algorithm and an expression data set, it measures the consistency of the clustering algorithm's ability to produce biologically meaningful clusters when applied repeatedly to similar data sets. A good clustering algorithm should have high BHI and moderate to high BSI. We evaluated the performance of ten well known clustering algorithms on two gene expression data sets and identified the optimal algorithm in each case. The first data set deals with SAGE profiles of differentially expressed tags between normal and ductal carcinoma in situ samples of breast cancer patients. The second data set contains the expression profiles over time of positively expressed genes (ORF's) during sporulation of budding yeast. Two separate choices of the functional classes were used for this data set and the results were compared for consistency. Conclusion Functional information of annotated genes available from various GO databases mined using ontology tools can be used to systematically judge the results of an unsupervised clustering algorithm as applied to a gene expression data set in clustering genes. This information could be used to select the right algorithm from a class of clustering algorithms for the given data set.</p
Parallel Computing in Statistical-Validation of Clustering Algorithm for the Analysis of High throughput Data
No full textCurrently, clustering applications use classical methods to partition a set of data (or objects) in a set of meaningful sub-classes, called clusters. A cluster is therefore a collection of objects which are “similar” among them, thus can be treated collectively as one group, and are “dissimilar” to the objects belonging to other clusters. However, there are a number of problems with clustering. Among them, as mentioned in [Datta03], dealing with large number of dimensions and large number of data items can be problematic because of computational time. In this thesis, we investigate all clustering algorithms used in [Datta03] and we present a parallel solution to minimize the computational time. We apply parallel programming techniques to the statistical algorithms as a natural extension to sequential programming technique using R. The proposed parallel model has been tested on a high throughput dataset. It is microarray data on the transcriptional profile during sporulation in budding yeast. It contains more than 6,000 genes. Our evaluation includes clustering algorithm scalability pertaining to datasets with varying dimensions, the speedup factor, and the efficiency of the parallel model over the sequential implementation. Our experiments show that the gene expression data follow the pattern predicted in [Datta03] that is Diana appears to be solid performer also the group means for each cluster coincides with that in [Datta03]. We show that our parallel model is applicable to the clustering algorithms and more useful in applications that deal with high throughput data, such as gene expression data
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