Design issues for cDNA microarray experiments

By Y. H. Yang, Terence P. Speed. 2002

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In Molecular Biology

Citations in the corpus

Statistical tests for differential expression in cDNA microarray experiments (2003, background): “Using two-color cDNA microarrays, samples can be compared directly on the same microarray or indirectly by hybridizing each sample with a common reference sample [4,6].”
A systems biology approach for pathway level analysis. (2007, methodology): “The factor ⌬E (g) represents the signed normalized measured expression change of the gene g determined using one of the available methods (Quackenbush 2001; Churchill 2002; Draghici 2002; Yang and Speed 2002).”
Ensemble deep learning in bioinformatics (2020, background): “Gene expression data including microarray, RNA-sequencing (RNA-seq) and, recently, single-cell RNA-seq (scRNA-seq) 63–65 , has been studied extensively to better understand complex diseases and to identify biomarkers that can guide therapeutic decision making.”
From patterns to pathways: gene expression data analysis comes of age (2002, neutral): “6).”
A broad characterization of the transcriptional profile of the compatible tomato response to the plant parasitic root knot nematode Meloidogyne javanica (2005, methodology): “, the RNA reference sample was labeled with Cy3-dUTP and all RNA test samples were labeled with Cy5-dUTP, no dyeswap was needed (Yang and Speed, 2002).”
Fundamentals of cDNA microarray data analysis. (2003, methodology): “Therefore it is usually preferable to have biological replication rather than technical replication if there are not enough tissues or resources to perform several microarray experiments, provided the experiment procedures are carried out care-fully [4,5].”