A Model for Measurement Error for Gene Expression Arrays

By David M. Rocke, Blythe Durbin. J. Comput. Biol., 2001

502 citations recorded by the source corpus.

Subfields: Management Science and Operations Research, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/da917e536ea627e95556dad2103685eabeff54c4

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In Management Science and Operations Research

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Citations in the corpus

Microarray data normalization and transformation (2002, methodology): “, and sophisticated methods have been proposed for development of error models based on analysis of repeated hybridization”
Measurement error in Lasso: impact and likelihood bias correction (2012, background): “In genomics, examples include gene expression microarray data (Purdom and Holmes (2005), Rocke and Durbin (2001)) and high-throughput sequenc- ing (Benjamini and Speed (2012)).”
Statistical tests for differential expression in cDNA microarray experiments (2003, background): “For example, an excess of low-intensity genes may be identified as being differentially expressed because their fold-change values have a larger variance than the fold-change values of high-intensity genes [13,14].”
A variance-stabilizing transformation for gene-expression microarray data (2002, methodology): “Furthermore, a straightforward delta-method approach shows that the asymptotic variance of ln ( ˆ µ) , AV ( ln ( ˆ µ) , is which is approximately constant for large µ but which approaches inﬁnity as µ → 0 (Rocke and Durbin, 2001).”
SynTReN: a generator of synthetic gene expression data for design and analysis of structure learning algorithms (2006, unknown): “This experimental noise is added to the simulated microarray data and is approximated by a lognormal distribution [27].”
Model-based variance-stabilizing transformation for Illumina microarray data (2008, methodology): “Due to the nature of the multiplicative and additive processes involved in the labeling reaction, in the photon detection system and in signal amplification, microarray raw intensity measurements always demonstrate an intensity-dependent (non-linear) measurement variation (7,8,10).”
Three-parameter lognormal distribution ubiquitously found in cDNA microarray data and its application to parametric data treatment (2004, unknown): “The intensity data of microarray experiments always contain a certain level of background [ 8 ], and inadequate estimation of this background can affect the assumed stabilities in ratio-based normalization methods as well as lognormal data distributions.”
MAANOVA: A Software Package for the Analysis of Spotted cDNA Microarray Experiments (2003, background): “It has been noted that raw microarray data have both additive and multiplicative error components (Rocke and Durbin, 2001).”
Rosetta error model for gene expression analysis (2006, methodology): “The other approach (the explicit approach) is to model the errors from various sources or types, such as additive and multiplicative errors (Rocke and Durbin, 2001; Theilhaber, et al., 2001; Dror, et al., 2003).”
Discovering regulatory and signalling circuits in molecular interaction networks (2002, unknown): “, 1999), significance testing (Kerr and Churchill, 2001; Rocke and Durbin, 2001; Ideker et al., 2000), and sequence motif identification (Pilpel et al.”
Variance stabilization applied to microarray data calibration and to the quantification of differential expression (2002, methodology): “One is provided by the following model for the measurement error of gene expression arrays (Rocke and Durbin, 2001): Y = α + βeη + ν, (10) where β is the expression level in arbitrary units, α is an offset, and ν and η are additive and multiplicative error terms, respectively. ν and η are assumed…”
Enhancing the Prioritization of Disease-Causing Genes through Tissue Specific Protein Interaction Networks (2012, background): “Thus, the former is likely to be less robust to noisy data such as gene expression [49,51,52]).”
Bayesian infinite mixture model based clustering of gene expression profiles (2002, unknown): “approaches have been proposed (Kerr et al., 2000; Newton et al., 2001; Ideker et al., 2000; Wolfinger et al., 2001; Rocke and Dubin, 2001; Lonnstedt and Speed, 2001).”
Assessing Gene Significance from cDNA Microarray Expression Data via Mixed Models (2001, unknown): “(2000), and Rocke and Durbin (2001), who accommodate gene heterogeneity via different global distributional assumptions across all measurements on an array.”
The Overlooked Importance of Constants Added in Log Transformation of Independent Variables with Zero Values: A Proposed Approach for Determining an Optimal Constant (2018, methodology): “…of gene-expression microarray data have also suggested methods for determining optimal values of the constant c, for variance-stabilization purposes when the expression levels are taken to be realizations from a specific measurement model (Rocke and Durbin 2001, 2003; Durbin and Rocke 2004).”