Multiple-laboratory comparison of microarray platforms

By Rafael A. Irizarry, Daniel Warren, Forrest Spencer, Irene F. Kim, Shyam Biswal, Bryan Frank, E. Gabrielson, Joe G. N. Garcia, J. Geoghegan, G. Germino and 15 more. Nature Methods, 2005

887 citations recorded by the source corpus.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/7107d41150d80e47451cf1d8084742b57a19295a

How researchers see this paper

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

In Molecular Biology

Citations in the corpus

Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2 (2014, neutral): “[2] and the proportion of genes in common after ranking by absolute logarithmic fold changes is compared [3].”
The balance of reproducibility, sensitivity, and specificity of lists of differentially expressed genes in microarray studies (2008, background): “[55] and the POG graphs that we introduced previously [11] except that in the current POG graphs the x-axis is in log-scale to emphasize the details when fewer genes are selected.”
A modular analysis framework for blood genomics studies: application to systemic lupus erythematosus (2008, background): “Early on, poor reproducibility of microarray results obtained by different laboratories and across platforms raised suspicion about the validity of these results and remains a major concern (Bammler et al., 2005; Frantz, 2005; Ioannidis, 2005; Irizarry et al., 2005; Larkin et al., 2005; Michiels et ”
A Survey on Filter Techniques for Feature Selection in Gene Expression Microarray Analysis (2012, methodology): “Another example is Correspondence At the Top (CAT) [67] which is a visualization method that represents the proportion of overlap of the top p features/genes versus p.”
ChIP-Seq of transcription factors predicts absolute and differential gene expression in embryonic stem cells (2009, background): “It is known that microarray has an intrinsic limitation to measure absolute mRNA concentration because of probe affinity effects (22).”
The MicroArray Quality Control (MAQC)-II study of common practices for the development and validation of microarray-based predictive models (2010, background): “and confirmed that microarray technology is able to reliably identify differentially expressed genes between sample classes or population”
Heavy-tailed prior distributions for sequence count data: removing the noise and preserving large differences (2018, methodology): “(b) Concordance At the Top (CAT) plot [18] comparing ranked gene lists from each method against the reference ranked gene list for 3 vs 3 samples.”