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NAR
2010
123views more  NAR 2010»
11 years 4 months ago
Discovering causal signaling pathways through gene-expression patterns
High-throughput gene-expression studies result in lists of differentially expressed genes. Most current meta-analyses of these gene lists include searching for significant members...
Jignesh R. Parikh, Bertram Klinger, Yu Xia, Jarrod...
BIOINFORMATICS
2010
250views more  BIOINFORMATICS 2010»
11 years 8 months ago
DEGseq: an R package for identifying differentially expressed genes from RNA-seq data
Summary: High-throughput RNA sequencing (RNA-seq) is rapidly emerging as a major quantitative transcriptome profiling platform. Here we present DEGseq, an R package to identify di...
Likun Wang, Zhixing Feng, Xi Wang, Xiaowo Wang, Xu...
JCB
2000
103views more  JCB 2000»
11 years 9 months ago
Testing for Differentially-Expressed Genes by Maximum-Likelihood Analysis of Microarray Data
Although two-color uorescent DNA microarrays are now standard equipment in many molecular biology laboratories, methods for identifying differentially expressed genes in microarra...
Trey Ideker, Vesteinn Thorsson, Andrew F. Siegel, ...
BMCBI
2004
169views more  BMCBI 2004»
11 years 9 months ago
A power law global error model for the identification of differentially expressed genes in microarray data
Background: High-density oligonucleotide microarray technology enables the discovery of genes that are transcriptionally modulated in different biological samples due to physiolog...
Norman Pavelka, Mattia Pelizzola, Caterina Vizzard...
BMCBI
2004
124views more  BMCBI 2004»
11 years 9 months ago
Tests for finding complex patterns of differential expression in cancers: towards individualized medicine
Background: Microarray studies in cancer compare expression levels between two or more sample groups on thousands of genes. Data analysis follows a population-level approach (e.g....
James Lyons-Weiler, Satish Patel, Michael J. Becic...
BMCBI
2004
165views more  BMCBI 2004»
11 years 9 months ago
Analysis of oligonucleotide array experiments with repeated measures using mixed models
Background: Two or more factor mixed factorial experiments are becoming increasingly common in microarray data analysis. In this case study, the two factors are presence (Patients...
Hao Li, Constance L. Wood, Thomas V. Getchell, Mar...
BMCBI
2004
113views more  BMCBI 2004»
11 years 9 months ago
Leveraging two-way probe-level block design for identifying differential gene expression with high-density oligonucleotide array
Background: To identify differentially expressed genes across experimental conditions in oligonucleotide microarray experiments, existing statistical methods commonly use a summar...
Leah Barrera, Chris Benner, Yong-Chuan Tao, Elizab...
BMCBI
2004
135views more  BMCBI 2004»
11 years 9 months ago
Determination of the differentially expressed genes in microarray experiments using local FDR
Background: Thousands of genes in a genomewide data set are tested against some null hypothesis, for detecting differentially expressed genes in microarray experiments. The expect...
Julie Aubert, Avner Bar-Hen, Jean-Jacques Daudin, ...
BMCBI
2005
140views more  BMCBI 2005»
11 years 9 months ago
Dissecting systems-wide data using mixture models: application to identify affected cellular processes
Background: Functional analysis of data from genome-scale experiments, such as microarrays, requires an extensive selection of differentially expressed genes. Under many condition...
J. Peter Svensson, Renée X. de Menezes, Ing...
BMCBI
2005
121views more  BMCBI 2005»
11 years 9 months ago
Comparison of seven methods for producing Affymetrix expression scores based on False Discovery Rates in disease profiling data
Background: A critical step in processing oligonucleotide microarray data is combining the information in multiple probes to produce a single number that best captures the express...
Kerby Shedden, Wei Chen, Rork Kuick, Debashis Ghos...
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