Statistical issues with microarrays: processing and analysis.

By Robert Nadon, J. Shoemaker. Trends in Genetics, 2002

338 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/8c188fcb7bf2f14268ccaa4e89eabab8e30699ba

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

Statistical practice in high-throughput screening data analysis (2006, background): “, however, and it is now standard practice to obtain at least three replicates per measurement in recognition that replicates offer advantages that outweigh short-term cost consideration”
Fold change and p-value cutoffs significantly alter microarray interpretations (2012, methodology): “As T-tests have been widely used to identify deviation from the mean, large sampling sizes (~15,000 genes assayed) can influence the number of false positives and may infer little if anything about the biology [9,10].”
Evaluation of gene expression measurements from commercial microarray platforms. (2003, methodology): “Therefore, we analyzed the ANOVA model using a more relaxed alpha to examine a higher power and less conservative approach often taken in knowledge discovery experimentation where false negatives may be more undesirable than false positives (12).”
Proteomics: the first decade and beyond (2003, background): “, and technological improvements continue to increase their utilit”
Post-analysis follow-up and validation of microarray experiments (2002, methodology): “The microarray field is rapidly evolving, and the number of publications on methods is substantia”
Fundamentals of cDNA microarray data analysis. (2003, background): “[ 15 ]). Because of these underlying variations, merely using a fixed threshold to infer significance might increase the proportion of false positives or false negatives.”