scholarly journals An empirical Bayes approach for analysis of diverse periodic trends in time-course gene expression data

2012 ◽  
Vol 29 (2) ◽  
pp. 182-188 ◽  
Author(s):  
Mehmet Kocak ◽  
E. Olusegun George ◽  
Saumyadipta Pyne ◽  
Stanley Pounds
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Empirical Bayes ◽  
Time Course ◽  
Expression Data ◽  
Empirical Bayes Approach ◽  
Bayes Approach

scholarly journals Robust regression for periodicity detection in non-uniformly sampled time-course gene expression data

2007 ◽  
Vol 8 (1) ◽  
Author(s):  
Miika Ahdesmäki ◽  
Harri Lähdesmäki ◽  
Andrew Gracey ◽  
llya Shmulevich ◽  
Olli Yli-Harja
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Time Course ◽  
Robust Regression ◽  
Expression Data ◽  
Periodicity Detection

scholarly journals Shrinkage of dispersion parameters in the binomial family, with application to differential exon skipping

2014 ◽  
Author(s):  
Sean Ruddy ◽  
Marla Johnson ◽  
Elizabeth Purdom
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Empirical Bayes ◽  
Simulated Data ◽  
Exon Skipping ◽  
Expression Data ◽  
Weighted Likelihood ◽  
Sequencing Data ◽  
Dispersion Parameters ◽  
Per Gene

The prevalence of sequencing experiments in genomics has led to an increased use of methods for count data in analyzing high-throughput genomic data to perform analyses. The importance of shrinkage methods in improving the performance of statistical methods remains. A common example is that of gene expression data, where the counts per gene are often modeled as some form of an over-dispersed Poisson. In this case, shrinkage estimates of the per-gene dispersion parameter have led to improved estimation of dispersion in the case of a small number of samples. We address a different count setting introduced by the use of sequencing data: comparing differential proportional usage via an over-dispersed binomial model. This is motivated by our interest in testing for differential exon skipping in mRNA-Seq experiments. We introduce a novel method that is developed by modeling the dispersion based on the double binomial distribution proposed by Efron (1986). Our method (WEB-Seq) is an empirical bayes strategy for producing a shrunken estimate of dispersion and effectively detects differential proportional usage, and has close ties to the weighted-likelihood strategy of edgeR developed for gene expression data (Robinson and Smyth, 2007; Robinson et al., 2010). We analyze its behavior on simulated data sets as well as real data and show that our method is fast, powerful and gives accurate control of the FDR compared to alternative approaches. We provide implementation of our methods in the R package DoubleExpSeq available on CRAN.

Clustering of short time-course gene expression data with dissimilar replicates

2017 ◽  
Vol 263 (1-2) ◽  
pp. 405-428
Author(s):  
Ozan Cinar ◽  
Ozlem Ilk ◽  
Cem Iyigun
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Time Course ◽  
Expression Data ◽  
Short Time

scholarly journals Differential and trajectory methods for time course gene expression data

2005 ◽  
Vol 21 (13) ◽  
pp. 3009-3016 ◽  
Author(s):  
Y. Liang ◽  
B. Tayo ◽  
X. Cai ◽  
A. Kelemen
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Time Course ◽  
Expression Data ◽  
Trajectory Methods

CURVE-BASED CLUSTERING OF TIME COURSE GENE EXPRESSION DATA USING SELF-ORGANIZING MAPS

2009 ◽  
Vol 07 (04) ◽  
pp. 645-661 ◽  
Author(s):  
XIN CHEN
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Regulatory Networks ◽  
Time Course ◽  
Clustering Algorithm ◽  
Expression Patterns ◽  
Self Organizing Map ◽  
Expression Data ◽  
Wide Range ◽  
Self Organizing

There is an increasing interest in clustering time course gene expression data to investigate a wide range of biological processes. However, developing a clustering algorithm ideal for time course gene express data is still challenging. As timing is an important factor in defining true clusters, a clustering algorithm shall explore expression correlations between time points in order to achieve a high clustering accuracy. Moreover, inter-cluster gene relationships are often desired in order to facilitate the computational inference of biological pathways and regulatory networks. In this paper, a new clustering algorithm called CurveSOM is developed to offer both features above. It first presents each gene by a cubic smoothing spline fitted to the time course expression profile, and then groups genes into clusters by applying a self-organizing map-based clustering on the resulting splines. CurveSOM has been tested on three well-studied yeast cell cycle datasets, and compared with four popular programs including Cluster 3.0, GENECLUSTER, MCLUST, and SSClust. The results show that CurveSOM is a very promising tool for the exploratory analysis of time course expression data, as it is not only able to group genes into clusters with high accuracy but also able to find true time-shifted correlations of expression patterns across clusters.

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