Fundamentals of experimental design for cDNA microarrays

By Gary A. Churchill. Nature Genetics, 2002

1,166 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/3642f8a0d46119a23e3618c899270d72a2d7042f

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In Molecular Biology

Citations in the corpus

Postharvest water loss induces marked changes in transcript profiling in skins of wine grape berries (2009, result): “Each dehydrated sample (S10, R10, S30, and R30) was directly compared with the T0 sample following the dye-swap experiment design (Churchill, 2002).”
Statistical Design and Analysis of RNA Sequencing Data (2010, background): “2000; K err and C hurchill 2001a,b; C hurchill 2002).”
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 pooled from all test RNA samples that were involved in the experiment (Churchill, 2002).”
Post-analysis follow-up and validation of microarray experiments (2002, background): “514 nature genetics supplement • volume 32 • december 2002 27. Al Moustafa, A.E. et al. Identification of genes associated with head and neck carcinogenesis by cDNA microarray comparison between matched primary normal epithelial and squamous carcinoma cells. Oncogene 21, 2634–2640 (2002). 28.”
Molecular and biochemical impacts of environmental factors on wheat grain development and protein synthesis (2003, background): “Because microarrays measure relative proportions of a given transcript in two samples, comparisons must be made between each time point in a pair-wise fashion (Churchill, 2002).”
PAGE: Parametric Analysis of Gene Set Enrichment (2005, positive): “At the data acquisition level, gene expression profiles from a given system should be reproducible and yield statistically significant changes in gene expression [1].”
Global testing under sparse alternatives: ANOVA, multiple comparisons and the higher criticism (2010, background): “As a consequence, there are countless situations in which it is routinely used; in particular, in the analysis of clinical trials [36] or in that of cDNA microarray experiments [7, 25, 37] to name just two important areas of biostatistics.”
An insight-based methodology for evaluating bioinformatics visualizations (2005, methodology): “Since our study is focused on the interactive visualization portion of data analysis, the data sets were preprocessed, normalized, prefiltered, and converted to the required formats (as discussed in [31] and [32]) in advance.”
Three-parameter lognormal distribution ubiquitously found in cDNA microarray data and its application to parametric data treatment (2004, neutral): “Furthermore, such stability will help in designing comparisons of data [23] by reducing the possibility that different designs will lead very different conclusions.”
From patterns to pathways: gene expression data analysis comes of age (2002, background): “As suggested elsewhere in this issue (see review by G. Churchill, pages 490–495) 5 , replication is essential in experimental design because it allows an estimate of variability (see also ref.”
Statistical tests for differential expression in cDNA microarray experiments (2003, background): “This provides a broad-sense inference (see Box 1) that applies to the biological population from which replicate samples were obtained [3,39].”
Gateways to the FANTOM5 promoter level mammalian expression atlas (2015, background): “Another approach to assess gene expression is by hybridization to pre-designed probes (that is, microarrays) [11-13].”