A high performance test of differential gene expression for oligonucleotide arrays
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* Corresponding author: Ming You youm@msnotes.wustl.edu
Genome Biology 2003, 4:R67 doi:10.1186/gb-2003-4-10-r67
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Method
Essi Laajala, Tero Aittokallio, Riitta Lahesmaa, Laura L Elo Genome Biology 2009, 10:R77 (16 July 2009) A novel statistical procedure is presented that uses probe-level information on Affymetrix exon arrays to detect differential splicing. |
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Methodology article
Determining gene expression on a single pair of microarrays Robert W Reid, Anthony A Fodor BMC Bioinformatics 2008, 9:489 (21 November 2008) |
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Methodology article
Empirical Bayes models for multiple probe type microarrays at the probe level Magnus Åstrand, Petter Mostad, Mats Rudemo BMC Bioinformatics 2008, 9:156 (20 March 2008) |
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Methodology article
Ann Hess, Hari Iyer BMC Genomics 2007, 8:96 (9 April 2007) |
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Research
Metabolic network driven analysis of genome-wide transcription data from Aspergillus nidulans Helga David, Gerald Hofmann, Ana Oliveira, Hanne Jarmer, Jens Nielsen Genome Biology 2006, 7:R108 (15 November 2006) Genome-wide transcription analysis of |
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Methodology article
Correlation test to assess low-level processing of high-density oligonucleotide microarray data Alexander Ploner, Lance D Miller, Per Hall, Jonas Bergh, Yudi Pawitan BMC Bioinformatics 2005, 6:80 (31 March 2005) A new graphical method for assessing the quality of low-level analysis of microarray expression data shows, among other things, that normalization, while necessary, should not be based on housekeeping genes.
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Method
Genomic analysis of early murine mammary gland development using novel probe-level algorithms Stephen R Master, Alexander J Stoddard, L Charles Bailey, Tien-Chi Pan, Katherine D Dugan, Lewis A Chodosh Genome Biology 2005, 6:R20 (1 February 2005) A novel algorithm (ChipStat) is presented for detecting gene-expression changes from Affymetrix microarray data. The method is used to identify changes in murine mammary development. |
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Research
Preferred analysis methods for Affymetrix GeneChips revealed by a wholly defined control dataset Sung E Choe, Michael Boutros, Alan M Michelson, George M Church, Marc S Halfon Genome Biology 2005, 6:R16 (28 January 2005) A 'spike-in' experiment for Affymetrix GeneChips is described that provides a defined dataset of 3,860 RNA species. A 'best route' combination of analysis methods is presented which allows detection of approximately 70% of true positives before reaching a 10% false discovery rate. |
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Research article
Leah Barrera, Chris Benner, Yong-Chuan Tao, Elizabeth Winzeler, Yingyao Zhou BMC Bioinformatics 2004, 5:42 (20 April 2004) |