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Articles 1 - 9 of 9
Full-Text Articles in Microarrays
Semiparametric Regression Of Multi-Dimensional Genetic Pathway Data: Least Squares Kernel Machines And Linear Mixed Models, Dawei Liu, Xihong Lin, Debashis Ghosh
Semiparametric Regression Of Multi-Dimensional Genetic Pathway Data: Least Squares Kernel Machines And Linear Mixed Models, Dawei Liu, Xihong Lin, Debashis Ghosh
Harvard University Biostatistics Working Paper Series
No abstract provided.
Multiple Testing With An Empirical Alternative Hypothesis, James E. Signorovitch
Multiple Testing With An Empirical Alternative Hypothesis, James E. Signorovitch
Harvard University Biostatistics Working Paper Series
An optimal multiple testing procedure is identified for linear hypotheses under the general linear model, maximizing the expected number of false null hypotheses rejected at any significance level. The optimal procedure depends on the unknown data-generating distribution, but can be consistently estimated. Drawing information together across many hypotheses, the estimated optimal procedure provides an empirical alternative hypothesis by adapting to underlying patterns of departure from the null. Proposed multiple testing procedures based on the empirical alternative are evaluated through simulations and an application to gene expression microarray data. Compared to a standard multiple testing procedure, it is not unusual for …
Exploration Of Distributional Models For A Novel Intensity-Dependent Normalization , Nicola Lama, Patrizia Boracchi, Elia Mario Biganzoli
Exploration Of Distributional Models For A Novel Intensity-Dependent Normalization , Nicola Lama, Patrizia Boracchi, Elia Mario Biganzoli
COBRA Preprint Series
Currently used gene intensity-dependent normalization methods, based on regression smoothing techniques, usually approach the two problems of location bias detrending and data re-scaling without taking into account the censoring characteristic of certain gene expressions produced by experiment measurement constraints or by previous normalization steps. Moreover, the bias vs variance balance control of normalization procedures is not often discussed but left to the user's experience. Here an approximate maximum likelihood procedure to fit a model smoothing the dependences of log-fold gene expression differences on average gene intensities is presented. Central tendency and scaling factor were modeled by means of B-splines smoothing …
A Flexible Statistical Method For Detecting Genomic Copy-Number Changes Using Hidden Markov Models With Reversible Jump Mcmc , Oscar M. Rueda, Ramon Diaz-Uriarte
A Flexible Statistical Method For Detecting Genomic Copy-Number Changes Using Hidden Markov Models With Reversible Jump Mcmc , Oscar M. Rueda, Ramon Diaz-Uriarte
COBRA Preprint Series
We have developed a statistical method for the analysis of array based CGH data to detect genomic DNA copy number changes. Our method allows us to answer the biologically relevant questions (what is the probability that a given gene or region has increased or decreased copy number changes) in a clear and simple way, within a rigorous statistical framework. We use a non-homogeneous Hidden Markov Model that incorporates distance between genes, a crucial requirement to analyze data from platforms where distances between probes is highly variable. As the true number of hidden states (states of copy number changes) is not …
Survival Analysis Of Longitudinal Microarrays, Natasa Rajicic, Dianne M. Finkelstein, David A. Schoenfeld
Survival Analysis Of Longitudinal Microarrays, Natasa Rajicic, Dianne M. Finkelstein, David A. Schoenfeld
COBRA Preprint Series
Motivation: The development of methods for linking gene expressions to various clinical and phenotypic characteristics is an active area of genomic research. Scientists hope that such analysis may, for example, describe relationships between gene function and clinical events such as death or recovery. Methods are available for relating gene expression to measurements that are categorized or continuous, but there is less work in relating expressions to an observed event time such as time to death, response, or relapse. When gene expressions are measured over time, there are methods for differentiating temporal patterns. However, no methods have yet been proposed for …
New Spiked-In Probe Sets For The Affymetrix Hgu-133a Latin Square Experiment, Monnie Mcgee, Zhongxue Chen
New Spiked-In Probe Sets For The Affymetrix Hgu-133a Latin Square Experiment, Monnie Mcgee, Zhongxue Chen
COBRA Preprint Series
The Affymetrix HGU-133A spike in data set has been used for determining the sensitivity and specificity of various methods for the analysis of microarray data. We show that there are 22 additional probe sets that detect spike in RNAs that should be considered as spike in probe sets. We assign each proposed spiked-in probe set to a concentration group within the Latin Square design, and examine the effects of the additional spiked-in probe sets on assessing the accuracy of analysis methods currently in use. We show that several popular preprocessing methods are more sensitive and specific when the new spike-ins …
Bayesian Models For Pooling Microarray Studies With Multiple Sources Of Replications, Erin M. Conlon, Joon J. Song, Jun S. Liu
Bayesian Models For Pooling Microarray Studies With Multiple Sources Of Replications, Erin M. Conlon, Joon J. Song, Jun S. Liu
Erin M. Conlon
Background Biologists often conduct multiple but different cDNA microarray studies that all target the same biological system or pathway. Within each study, replicate slides within repeated identical experiments are often produced. Pooling information across studies can help more accurately identify true target genes. Here, we introduce a method to integrate multiple independent studies efficiently. Results We introduce a Bayesian hierarchical model to pool cDNA microarray data across multiple independent studies to identify highly expressed genes. Each study has multiple sources of variation, i.e. replicate slides within repeated identical experiments. Our model produces the gene-specific posterior probability of differential expression, which …
2^K Factorials In Blocks Of Size 2, With Application To Two-Color Microarray Experiments, Kathleen F. Kerr
2^K Factorials In Blocks Of Size 2, With Application To Two-Color Microarray Experiments, Kathleen F. Kerr
UW Biostatistics Working Paper Series
When a two-level design must be run in blocks of size two, there is a unique blocking scheme that enables estimation of all the main effects. Unfortunately this design does not enable estimation of any two-factor interactions. When the experimental goal is to estimate all main effects and two-factor interactions, it is necessary to combine replicates of the experiment that use different blocking schemes. In this paper we identify such designs for up to eight factors that enable estimation of all main effects and two-factor interactions with the fewest number of replications. In addition, we give a construction for general …
Yeast Through The Ages: A Statistical Analysis Of Genetic Changes In Aging Yeast, Alison Wise '05, Johanna S. Hardin, Laura Hoopes
Yeast Through The Ages: A Statistical Analysis Of Genetic Changes In Aging Yeast, Alison Wise '05, Johanna S. Hardin, Laura Hoopes
Pomona Faculty Publications and Research
Microarray technology allows for the expression levels of thousands of genes in a cell to be measured simultaneously. The technology provides great potential in the fields of biology and medicine, as the analysis of data obtained from microarray experiments gives insight into the roles of specific genes and the associated changes across experimental conditions (e.g., aging, mutation, radiation therapy, drug dosage). The application of statistical tools to microarray data can help make sense of the experiment and thereby advance genetic, biological, and medical research. Likewise, microarrays provide an exciting means through which to explore statistical techniques.