A Model-Based Background Adjustment for Oligonucleotide Expression Arrays

By Zhijin Wu, Rafael A. Irizarry, Robert Gentleman, F. Martínez-Murillo, Forrest Spencer. 2004

1,924 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/15d7b0649214462801d6d0c1732681820487dd52

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Citations in the corpus

Oxidative stress in health and disease: the therapeutic potential of Nrf2 activation. (2011, methodology): “AIFM2 (aka AMID), induced 29-fold, is implicated in caspase-independent apoptosis and was found to be downregulated in a majority of human tumors (Wu et al., 2004b).”
Frozen robust multiarray analysis (fRMA). (2010, neutral): “, 2003), gcRMA (Wu et al., 2004), MBEI (Li and Wong, 2001), and PLIER (Affymetrix, 2005).”
Comparison of Affymetrix GeneChip expression measures (2006, neutral): “Because accuracy depends on the overall expression of genes, we separated the main accuracy assessment, [Signal detect slope (row 6 in Table 1 of Cope et al. , (2004)] into three components.”
Revisiting Global Gene Expression Analysis (2012, neutral): “Furthermore, the most commonly used analysis methods are primarily intended to account for technical variations in signal to noise and assume that the signals for different samples from different experiments should be scaled to have the same median or average value or that the distributions of signa”
Efficient and rapid generation of induced pluripotent stem cells from human keratinocytes (2008, methodology): “First, the raw data were normalized using the gcRMA algorithm implemented in R (ref”
Modeling Oncogenic Signaling in Colon Tumors by Multidirectional Analyses of Microarray Data Directed for Maximization of Analytical Reliability (2010, background): “0 [36], MBEI [37], RMA [38], and GCGMA [39].”
Most Random Gene Expression Signatures Are Significantly Associated with Breast Cancer Outcome (2011, methodology): “We renormalized the raw data (CEL files) using Bioconductor [67] package gcrma [68].”
A gene expression atlas of the model legume Medicago truncatula. (2008, methodology): “All expression data were normalized using GCRMA (Wu et al., 2004).”
Microarray analysis after RNA amplification can detect pronounced differences in gene expression using limma (2006, methodology): “The bayesian adjusted t-statistics from the linear models for Micoarray data (limma) package [ 14 ,15] and the Z-scores method as described by Quackenbush [16].”
Bgee: Integrating and Comparing Heterogeneous Transcriptome Data Among Species (2008, methodology): “We are currently implementing another method of detection [19], which uses the gcRMA algorithm [20] to normalize the signal taking into account probe sequences, and uses a subset of weakly expressed probesets for estimating the background.”
The AtGenExpress global stress expression data set: protocols, evaluation and model data analysis of UV-B light, drought and cold stress responses. (2007, methodology): “The arrays were adjusted for background of optical noise using the GC-RMA software (Wu et al., 2004) and normalized using quantile normalization.”
The MYB96 Transcription Factor Regulates Cuticular Wax Biosynthesis under Drought Conditions in Arabidopsis[W] (2011, methodology): “Global normalization and expression estimates were analyzed by GC-robust multiarray implemented in the bioconductor (http:// www.bioconductor.org) and R software (Wu et al., 2004).”
Predicting effective microRNA target sites in mammalian mRNAs (2015, methodology): “Affymetrix data were first background-corrected with the ‘gcrma’ R package ( Wu et al., 2004 ), whereas Illumina BeadArray data from the miR-302 knockdown and miR-522 transfection datasets ( Lipchina et al., 2011 ; Tan et al., 2014 ) were processed and background-corrected using the ‘lumiR’ and…”