11,731 research outputs found
Gene expression reliability estimation through cluster-based analysis
Gene expression is the fundamental control of the structure and functions of the cellular versatility and adaptability of any organisms. The measurement of gene expressions is performed on images generated by optical inspection of microarray devices which allow the simultaneous analysis of thousands of genes. The images produced by these devices are used to calculate the expression levels of mRNA in order to draw diagnostic information related to human disease. The quality measures are mandatory in genes classification and in the decision-making diagnostic. However, microarrays are characterized by imperfections due to sample contaminations, scratches, precipitation or imperfect gridding and spot detection. The automatic and efficient quality measurement of microarray is needed in order to discriminate faulty gene expression levels. In this paper we present a new method for estimate the quality degree and the data's reliability of a microarray analysis. The efficiency of the proposed approach in terms of genes expression classification has been demonstrated through a clustering supervised analysis performed on a set of three different histological samples related to the Lymphoma's cancer diseas
Noise and nonlinearities in high-throughput data
High-throughput data analyses are becoming common in biology, communications,
economics and sociology. The vast amounts of data are usually represented in
the form of matrices and can be considered as knowledge networks. Spectra-based
approaches have proved useful in extracting hidden information within such
networks and for estimating missing data, but these methods are based
essentially on linear assumptions. The physical models of matching, when
applicable, often suggest non-linear mechanisms, that may sometimes be
identified as noise. The use of non-linear models in data analysis, however,
may require the introduction of many parameters, which lowers the statistical
weight of the model. According to the quality of data, a simpler linear
analysis may be more convenient than more complex approaches.
In this paper, we show how a simple non-parametric Bayesian model may be used
to explore the role of non-linearities and noise in synthetic and experimental
data sets.Comment: 12 pages, 3 figure
Microarray sub-grid detection: A novel algorithm
This is the post print version of the article. The official published version can be obtained from the link below - Copyright 2007 Taylor & Francis LtdA novel algorithm for detecting microarray subgrids is proposed. The only input to the algorithm is the raw microarray image, which can be of any resolution, and the subgrid detection is performed with no prior assumptions. The algorithm consists of a series of methods of spot shape detection, spot filtering, spot spacing estimation, and subgrid shape detection. It is shown to be able to divide images of varying quality into subgrid regions with no manual interaction. The algorithm is robust against high levels of noise and high percentages of poorly expressed or missing spots. In addition, it is proved to be effective in locating regular groupings of primitives in a set of non-microarray images, suggesting potential application in the general area of image processing
Penalized EM algorithm and copula skeptic graphical models for inferring networks for mixed variables
In this article, we consider the problem of reconstructing networks for
continuous, binary, count and discrete ordinal variables by estimating sparse
precision matrix in Gaussian copula graphical models. We propose two
approaches: penalized extended rank likelihood with Monte Carlo
Expectation-Maximization algorithm (copula EM glasso) and copula skeptic with
pair-wise copula estimation for copula Gaussian graphical models. The proposed
approaches help to infer networks arising from nonnormal and mixed variables.
We demonstrate the performance of our methods through simulation studies and
analysis of breast cancer genomic and clinical data and maize genetics data
The EM Algorithm and the Rise of Computational Biology
In the past decade computational biology has grown from a cottage industry
with a handful of researchers to an attractive interdisciplinary field,
catching the attention and imagination of many quantitatively-minded
scientists. Of interest to us is the key role played by the EM algorithm during
this transformation. We survey the use of the EM algorithm in a few important
computational biology problems surrounding the "central dogma"; of molecular
biology: from DNA to RNA and then to proteins. Topics of this article include
sequence motif discovery, protein sequence alignment, population genetics,
evolutionary models and mRNA expression microarray data analysis.Comment: Published in at http://dx.doi.org/10.1214/09-STS312 the Statistical
Science (http://www.imstat.org/sts/) by the Institute of Mathematical
Statistics (http://www.imstat.org
Transposable regularized covariance models with an application to missing data imputation
Missing data estimation is an important challenge with high-dimensional data
arranged in the form of a matrix. Typically this data matrix is transposable,
meaning that either the rows, columns or both can be treated as features. To
model transposable data, we present a modification of the matrix-variate
normal, the mean-restricted matrix-variate normal, in which the rows and
columns each have a separate mean vector and covariance matrix. By placing
additive penalties on the inverse covariance matrices of the rows and columns,
these so-called transposable regularized covariance models allow for maximum
likelihood estimation of the mean and nonsingular covariance matrices. Using
these models, we formulate EM-type algorithms for missing data imputation in
both the multivariate and transposable frameworks. We present theoretical
results exploiting the structure of our transposable models that allow these
models and imputation methods to be applied to high-dimensional data.
Simulations and results on microarray data and the Netflix data show that these
imputation techniques often outperform existing methods and offer a greater
degree of flexibility.Comment: Published in at http://dx.doi.org/10.1214/09-AOAS314 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
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