23,843 research outputs found
Extreme Value Distribution Based Gene Selection Criteria for Discriminant Microarray Data Analysis Using Logistic Regression
One important issue commonly encountered in the analysis of microarray data
is to decide which and how many genes should be selected for further studies.
For discriminant microarray data analyses based on statistical models, such as
the logistic regression models, gene selection can be accomplished by a
comparison of the maximum likelihood of the model given the real data,
, and the expected maximum likelihood of the model given an
ensemble of surrogate data with randomly permuted label, .
Typically, the computational burden for obtaining is immense,
often exceeding the limits of computing available resources by orders of
magnitude. Here, we propose an approach that circumvents such heavy
computations by mapping the simulation problem to an extreme-value problem. We
present the derivation of an asymptotic distribution of the extreme-value as
well as its mean, median, and variance. Using this distribution, we propose two
gene selection criteria, and we apply them to two microarray datasets and three
classification tasks for illustration.Comment: to be published in Journal of Computational Biology (2004
GOexpress: an R/Bioconductor package for the identification and visualisation of robust gene ontology signatures through supervised learning of gene expression data
Background: Identification of gene expression profiles that differentiate experimental groups is critical for discovery and analysis of key molecular pathways and also for selection of robust diagnostic or prognostic biomarkers. While integration of differential expression statistics has been used to refine gene set enrichment analyses, such approaches are typically limited to single gene lists resulting from simple two-group comparisons or time-series analyses. In contrast, functional class scoring and machine learning approaches provide powerful alternative methods to leverage molecular measurements for pathway analyses, and to compare continuous and multi-level categorical factors. Results: We introduce GOexpress, a software package for scoring and summarising the capacity of gene ontology features to simultaneously classify samples from multiple experimental groups. GOexpress integrates normalised gene expression data (e.g., from microarray and RNA-seq experiments) and phenotypic information of individual samples with gene ontology annotations to derive a ranking of genes and gene ontology terms using a supervised learning approach. The default random forest algorithm allows interactions between all experimental factors, and competitive scoring of expressed genes to evaluate their relative importance in classifying predefined groups of samples. Conclusions: GOexpress enables rapid identification and visualisation of ontology-related gene panels that robustly classify groups of samples and supports both categorical (e.g., infection status, treatment) and continuous (e.g., time-series, drug concentrations) experimental factors. The use of standard Bioconductor extension packages and publicly available gene ontology annotations facilitates straightforward integration of GOexpress within existing computational biology pipelines.Department of Agriculture, Food and the MarineEuropean Commission - Seventh Framework Programme (FP7)Science Foundation IrelandUniversity College Dubli
Nonparametric estimation of genewise variance for microarray data
Estimation of genewise variance arises from two important applications in
microarray data analysis: selecting significantly differentially expressed
genes and validation tests for normalization of microarray data. We approach
the problem by introducing a two-way nonparametric model, which is an extension
of the famous Neyman--Scott model and is applicable beyond microarray data. The
problem itself poses interesting challenges because the number of nuisance
parameters is proportional to the sample size and it is not obvious how the
variance function can be estimated when measurements are correlated. In such a
high-dimensional nonparametric problem, we proposed two novel nonparametric
estimators for genewise variance function and semiparametric estimators for
measurement correlation, via solving a system of nonlinear equations. Their
asymptotic normality is established. The finite sample property is demonstrated
by simulation studies. The estimators also improve the power of the tests for
detecting statistically differentially expressed genes. The methodology is
illustrated by the data from microarray quality control (MAQC) project.Comment: Published in at http://dx.doi.org/10.1214/10-AOS802 the Annals of
Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical
Statistics (http://www.imstat.org
Application of Volcano Plots in Analyses of mRNA Differential Expressions with Microarrays
Volcano plot displays unstandardized signal (e.g. log-fold-change) against
noise-adjusted/standardized signal (e.g. t-statistic or -log10(p-value) from
the t test). We review the basic and an interactive use of the volcano plot,
and its crucial role in understanding the regularized t-statistic. The joint
filtering gene selection criterion based on regularized statistics has a curved
discriminant line in the volcano plot, as compared to the two perpendicular
lines for the "double filtering" criterion. This review attempts to provide an
unifying framework for discussions on alternative measures of differential
expression, improved methods for estimating variance, and visual display of a
microarray analysis result. We also discuss the possibility to apply volcano
plots to other fields beyond microarray.Comment: 8 figure
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
A cDNA Microarray Gene Expression Data Classifier for Clinical Diagnostics Based on Graph Theory
Despite great advances in discovering cancer molecular profiles, the proper application of microarray technology to routine clinical diagnostics is still a challenge. Current practices in the classification of microarrays' data show two main limitations: the reliability of the training data sets used to build the classifiers, and the classifiers' performances, especially when the sample to be classified does not belong to any of the available classes. In this case, state-of-the-art algorithms usually produce a high rate of false positives that, in real diagnostic applications, are unacceptable. To address this problem, this paper presents a new cDNA microarray data classification algorithm based on graph theory and is able to overcome most of the limitations of known classification methodologies. The classifier works by analyzing gene expression data organized in an innovative data structure based on graphs, where vertices correspond to genes and edges to gene expression relationships. To demonstrate the novelty of the proposed approach, the authors present an experimental performance comparison between the proposed classifier and several state-of-the-art classification algorithm
Laplace Approximated EM Microarray Analysis: An Empirical Bayes Approach for Comparative Microarray Experiments
A two-groups mixed-effects model for the comparison of (normalized)
microarray data from two treatment groups is considered. Most competing
parametric methods that have appeared in the literature are obtained as special
cases or by minor modification of the proposed model. Approximate maximum
likelihood fitting is accomplished via a fast and scalable algorithm, which we
call LEMMA (Laplace approximated EM Microarray Analysis). The posterior odds of
treatment gene interactions, derived from the model, involve shrinkage
estimates of both the interactions and of the gene specific error variances.
Genes are classified as being associated with treatment based on the posterior
odds and the local false discovery rate (f.d.r.) with a fixed cutoff. Our
model-based approach also allows one to declare the non-null status of a gene
by controlling the false discovery rate (FDR). It is shown in a detailed
simulation study that the approach outperforms well-known competitors. We also
apply the proposed methodology to two previously analyzed microarray examples.
Extensions of the proposed method to paired treatments and multiple treatments
are also discussed.Comment: Published in at http://dx.doi.org/10.1214/10-STS339 the Statistical
Science (http://www.imstat.org/sts/) by the Institute of Mathematical
Statistics (http://www.imstat.org
Inverse Projection Representation and Category Contribution Rate for Robust Tumor Recognition
Sparse representation based classification (SRC) methods have achieved
remarkable results. SRC, however, still suffer from requiring enough training
samples, insufficient use of test samples and instability of representation. In
this paper, a stable inverse projection representation based classification
(IPRC) is presented to tackle these problems by effectively using test samples.
An IPR is firstly proposed and its feasibility and stability are analyzed. A
classification criterion named category contribution rate is constructed to
match the IPR and complete classification. Moreover, a statistical measure is
introduced to quantify the stability of representation-based classification
methods. Based on the IPRC technique, a robust tumor recognition framework is
presented by interpreting microarray gene expression data, where a two-stage
hybrid gene selection method is introduced to select informative genes.
Finally, the functional analysis of candidate's pathogenicity-related genes is
given. Extensive experiments on six public tumor microarray gene expression
datasets demonstrate the proposed technique is competitive with
state-of-the-art methods.Comment: 14 pages, 19 figures, 10 table
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