Statistical tests for differential expression in cDNA microarray experiments

By Xiangqin Cui, Gary A. Churchill. Genome Biology, 2003

564 citations recorded by the source corpus.

Extracting biological information from microarray data requires appropriate statistical methods. The simplest statistical method for detecting differential expression is the t test, which can be used to compare two conditions when there is replication of samples. With more than two conditions, analysis of variance (ANOVA) can be used, and the mixed ANOVA model is a general and powerful approach for microarray experiments with multiple factors and/or several sources of variation.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/f0c8fc8ab280846d0e4e107adc6f115bddf37200

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In Molecular Biology

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Citations in the corpus

• Statistical strategies for avoiding false discoveries in metabolomics and related experiments (2007, unknown): “Partly because of the expense of transcriptomics (and the inherent noise in such data (Schena, 2000; Tu et al., 2002; Cui and Churchill, 2003; Liang and Kelemen, 2006 )), the numbers of samples and their replicates is often small while the number of candidate genes is typically in the thousands.”
• Linear Models and Empirical Bayes Methods for Assessing Differential Expression in Microarray Experiments (2004, unknown): “Keywords: microarrays, empirical Bayes, linear models, hyperparameters, differential expression”
• RNA Sequencing and Analysis (2015, unknown): “Extensive statistical approaches have been developed to test for differential expression with microarray data, where the continuous probe intensities across replicates can be approximated by a normal distribution (Cui and Churchill 2003; Smyth 2004; Grant et al. 2005).”
• Identifying biological themes within lists of genes with EASE (2003, unknown): “Calculating statistics on thousands of gene categories can lead to a few seemingly significant probabilities due simply to chance [22].”
• Estimating the Null and the Proportion of Nonnull Effects in Large-Scale Multiple Comparisons (2006, unknown): “Because Xj is transformed from the t score, which has been standardized by the corresponding standard error, it is reasonable to assume that the null effects are homogeneous and that all effects are homoscedastic (see, e.g., Cui and Churchill 2003; Efron 2004).”
• Validating internal controls for quantitative plant gene expression studies (2004, methodology): “This overall mean provides an index of RNA quality and quantity for that tissue sample, much as means over test sites provide an index of site fertility in yield trials [13].”
• GFOLD: a generalized fold change for ranking differentially expressed genes from RNA-seq data (2012, unknown): “Researchers have also attempted to combine the fold change and P -value to provide more meaningful results by setting cutoffs for both the fold change and P -value as in the well-known volcano plot (Cui and Churchill, 2003).”