Genetics and Genomics Commons^™

Resampling-Based Multiple Hypothesis Testing With Applications To Genomics: New Developments In The R/Bioconductor Package Multtest, Houston N. Gilbert, Katherine S. Pollard, Mark J. Van Der Laan, Sandrine Dudoit

U.C. Berkeley Division of Biostatistics Working Paper Series

The multtest package is a standard Bioconductor package containing a suite of functions useful for executing, summarizing, and displaying the results from a wide variety of multiple testing procedures (MTPs). In addition to many popular MTPs, the central methodological focus of the multtest package is the implementation of powerful joint multiple testing procedures. Joint MTPs are able to account for the dependencies between test statistics by effectively making use of (estimates of) the test statistics joint null distribution. To this end, two additional bootstrap-based estimates of the test statistics joint null distribution have been developed for use in the …

Hierarchical Hidden Markov Model With Application To Joint Analysis Of Chip-Chip And Chip-Seq Data, Hyungwon Choi, Debashis Ghosh, Zhaohui S. Qin

Debashis Ghosh

Motivation: Identication of transcription factor binding sites (TFBS) is a fundamental problem in understanding the mechanism of gene regulation. The ChIP-chip technology has accelerated this eort by providing a simultaneous genome-wide map of TFBS in a high-throughput fashion. Recently, a sequencing-based ChIP-seq has appeared as a promising alternative that can identify targets with an improved sensitivity/specicity in high resolution. However, studies have suggested that distinct experimental platforms can be complementary in TFBS identication. The availability of data obtained from multiple platforms motivates a meta-analysis for improved identication of candidate motifs.

Results: In this work, we propose a hierarchical hidden Markov …

Discrete Nonparametric Algorithms For Outlier Detection With Genomic Data, Debashis Ghosh

Debashis Ghosh

In high-throughput studies involving genetic data such as from gene expression microarrays, differential expression analysis between two or more experimental conditions has been a very common analytical task. Much of the resulting literature on multiple comparisons has paid relatively little attention to the choice of test statistic. In this article, we focus on the issue of choice of test statistic based on a special pattern of differential expression. The approach here is based on recasting multiple comparisons procedures for assessing outlying expression values. A major complication is that the resulting p-values are discrete; some theoretical properties of sequential testing procedures …