Replicated microarray data

By Ingrid Lönnstedt. 2001

764 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/056e7774cd48d90dad92fbac784baa1837459aec

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

Use of within-array replicate spots for assessing differential expression in microarray experiments (2005, methodology): “…each individual gene can be made more reliable by making use of information generated from the whole ensemble of genes (Newton et al., 2001; Tusher et al., 2001; Efron et al., 2001; Efron and Tibshirani, 2002; L¨onnstedt and Speed, 2002; Kendziorski et al., 2003; Newton et al., 2004; Smyth, 2004).”
Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2 (2014, unknown): “In high-throughput assays, this limitation can be overcome by pooling information across genes; specifically, by exploiting assumptions about the similarity of the variances of different genes measured in the same experiment [1].”
Bayesian infinite mixture model based clustering of gene expression profiles (2002, methodology): “Previously, Schmidler et al. (2000) used the Bayesian model-averaging approach to estimate probabilities of a particular secondary structure after averaging over all possible segmentation.”
The optimal discovery procedure: a new approach to simultaneous significance testing (2007, methodology): “Although this is quite difficult to answer in general, we have shown that an estimated ODP substantially outperforms the empirical Bayesian methods of Efron et al. (2001) and Lonnstedt and Speed (2002) when attempting to identify differentially expressed genes in DNA microarray experiments (Storey”
Independent filtering increases detection power for high-throughput experiments (2010, methodology): “The same reasoning used to prove that [1] controls the FWER may also be used to show that [2] provides conditional control of the FWER, given M.”
A multivariate empirical Bayes statistic for replicated microarray time course data (2006, methodology): “The B-statistic in [20] and [24], and the univariate moderates t-statistic t̃g in [24], consider just one parameter or contrast at a time in the null hypotheses.”
Estimating the Null and the Proportion of Nonnull Effects in Large-Scale Multiple Comparisons (2006, methodology): “This is the case in a wide range of applications (Efron 2004; Lönnstedt and Speed 2002).”
A Factor Model Approach to Multiple Testing Under Dependence (2009, background): “Similarly, some authors (see, e.g., Lönnstedt and Speed 2002; Smyth 2004) proposed moderated versions of the t-satistics where the variable-specific variance estimator in the denominator is augmented by a constant, which is derived from the whole set of variables.”
Local-pooled-error test for identifying differentially expressed genes with a small number of replicated microarrays (2003, methodology): “(2001) Lönnstedt and Speed (2002), Baldi and Long (2001), and Newtonet al. (2001) which shrink the within-gene variance estimate towards an estimate including more genes, and construct signal-to-noise ratios using the shrunken variance, are similar in spirit to our LPE test.”
Normalization of cDNA microarray data. (2003, methodology): “The function ebayes computes moderated t-statistics and B-statistics [10] which can be used to rank the genes in order of evidence for differential expression.”
Differential expression analysis for sequence count data (2010, methodology): “An empirical Bayes procedure, similar to the one originally developed for the limma package [24-26], determines how to combine these two sources of information optimally.”
Testing significance relative to a fold-change threshold is a TREAT (2009, methodology): “A simple hierarchical model can reflect this parallel structure by describing how the unknown coefficients and variances vary across genes (Lönnstedt and Speed, 2002; Smyth, 2004; Wright and Simon, 2003).”
Detecting differential gene expression with a semiparametric hierarchical mixture method. (2004, background): “Another useful and fairly innocuous assumption is the discrete mixture model, which is becoming popular in microarray data analysis (Newton et al., 2001; Efron et al., 2001; Allison et al., 2002; Broët et al., 2002; Lee et al., 2002; Lonnstedt and Speed, 2002; Pan, 2002; Kendziorski et al., 2003; St”