Literature DB >> 11848410

Correcting for signal saturation errors in the analysis of microarray data.

L L Hsiao1, R V Jensen, T Yoshida, K E Clark, J E Blumenstock, S R Gullans.   

Abstract

A variety of technical errors have arisen in data analysis when using cDNA or oligonucleotide microarrays. One of the most insidious problems is the saturation of the hybridization signal of high-abundant transcripts. This problem arises from the truncation of the laser fluorescence signal. When the hybridization signal on the microarray is very strong, this truncation can result in serious consequences that may not be readily apparent to the user. As an illustration of this problem, two subclasses of normal human tissue samples (six liver and six lung samples) were analyzed with GeneChip probe arrays to evaluate the patterns of expression for approximately 7000 human genes. Five of these data sets were found to suffer from signal truncation. This caused several tissues to be incorrectly classified using hierarchical clustering. To rectify this problem so that the gene expression data could be properly compared and clustered, we developed a "filtering" procedure that identifies a subset of genes least affected by the signal saturation. This filtering procedure can be obtained at www.hugeindex.org.

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Year:  2002        PMID: 11848410     DOI: 10.2144/02322st06

Source DB:  PubMed          Journal:  Biotechniques        ISSN: 0736-6205            Impact factor:   1.993


  10 in total

1.  β-Catenin is central to DUX4-driven network rewiring in facioscapulohumeral muscular dystrophy.

Authors:  Christopher R S Banerji; Paul Knopp; Louise A Moyle; Simone Severini; Richard W Orrell; Andrew E Teschendorff; Peter S Zammit
Journal:  J R Soc Interface       Date:  2015-01-06       Impact factor: 4.118

2.  Experimental design, preprocessing, normalization and differential expression analysis of small RNA sequencing experiments.

Authors:  Kevin P McCormick; Matthew R Willmann; Blake C Meyers
Journal:  Silence       Date:  2011-02-28

3.  Bayesian integrated modeling of expression data: a case study on RhoG.

Authors:  Rashi Gupta; Dario Greco; Petri Auvinen; Elja Arjas
Journal:  BMC Bioinformatics       Date:  2010-06-01       Impact factor: 3.169

4.  Segmentation and intensity estimation for microarray images with saturated pixels.

Authors:  Yan Yang; Phillip Stafford; YoonJoo Kim
Journal:  BMC Bioinformatics       Date:  2011-11-30       Impact factor: 3.169

5.  In vivo-in vitro toxicogenomic comparison of TCDD-elicited gene expression in Hepa1c1c7 mouse hepatoma cells and C57BL/6 hepatic tissue.

Authors:  Edward Dere; Darrell R Boverhof; Lyle D Burgoon; Timothy R Zacharewski
Journal:  BMC Genomics       Date:  2006-04-12       Impact factor: 3.969

6.  The impact of amplification on differential expression analyses by RNA-seq.

Authors:  Swati Parekh; Christoph Ziegenhain; Beate Vieth; Wolfgang Enard; Ines Hellmann
Journal:  Sci Rep       Date:  2016-05-09       Impact factor: 4.379

7.  Mixture models reveal multiple positional bias types in RNA-Seq data and lead to accurate transcript concentration estimates.

Authors:  Andreas Tuerk; Gregor Wiktorin; Serhat Güler
Journal:  PLoS Comput Biol       Date:  2017-05-15       Impact factor: 4.475

Review 8.  Statistical and Machine-Learning Analyses in Nutritional Genomics Studies.

Authors:  Leila Khorraminezhad; Mickael Leclercq; Arnaud Droit; Jean-François Bilodeau; Iwona Rudkowska
Journal:  Nutrients       Date:  2020-10-14       Impact factor: 5.717

9.  Profound influence of microarray scanner characteristics on gene expression ratios: analysis and procedure for correction.

Authors:  Heidi Lyng; Azadeh Badiee; Debbie H Svendsrud; Eivind Hovig; Ola Myklebost; Trond Stokke
Journal:  BMC Genomics       Date:  2004-02-03       Impact factor: 3.969

Review 10.  The use of transcriptomics to unveil the role of nutrients in Mammalian liver.

Authors:  Jesús Osada
Journal:  ISRN Nutr       Date:  2013-08-28
  10 in total

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