Multiple Testing Procedures with Applications to Genomics

By A. Hubbard. 2007

383 citations recorded by the source corpus.

Subfields: Molecular Biology, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/7302febdec049235971895db54aef77e7624dc3e

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

1. Simone Vantini
2. Alessia Pini

In Statistics and Probability

1. Simone Vantini
2. Alessia Pini

Citations in the corpus

• CHiCAGO: robust detection of DNA looping interactions in Capture Hi-C data (2015, neutral): “Multiple testing issues are important in genomic analyses and, in attempting to address these issues, a number of bespoke approaches have been developed [20, 31].”
• Interval-wise testing for functional data (2017, positive): “Focussing on inferentially driven feature/domain selection techniques, in the finite-dimensional multivariate case a feature selection can be performed by exploiting multiple testing techniques (Dudoit and Van Der Laan 2007).”
• Multiple hypothesis testing in genomics (2014, neutral): “One can find more technical overviews in the papers of Farcomeni [3] and Roquain [4] and the book by Dudoit and Van der Laan [5].”
• Distribution-free Multiple Testing (2016, neutral): “Multiple testing arises in a wide array of applied settings, ranging from anomaly detection in sensor arrays to the selection of genes that are differentially expressed [8, 10].”
• Karl Pearson’s meta analysis revisited (2009, neutral): “Pearson also notices that the approach is applicable more generally than testing goodness of fit for IID data, and in a note at the end, acknowledges that Fisher (1932) found the distribution earlier.”
• Asymptotic optimality of the Westfall--Young permutation procedure for multiple testing under dependence (2011, neutral): “Such testing problems arise in many applications, in particular, in the fields of genomics and genome-wide association studies [9, 18, 24], but also in astronomy and other fields [21, 26].”
• Large-scale inference (2010, neutral): “The Ebay empirical Bayes prediction algorithm appears in Efron (2009c). Subramanian et al.”