TY - JOUR AU - Li, Yihan AU - Ghosh, Debashis PY - 2014 DA - 2014/04/14 TI - A two-step hierarchical hypothesis set testing framework, with applications to gene expression data on ordered categories JO - BMC Bioinformatics SP - 108 VL - 15 IS - 1 AB - In complex large-scale experiments, in addition to simultaneously considering a large number of features, multiple hypotheses are often being tested for each feature. This leads to a problem of multi-dimensional multiple testing. For example, in gene expression studies over ordered categories (such as time-course or dose-response experiments), interest is often in testing differential expression across several categories for each gene. In this paper, we consider a framework for testing multiple sets of hypothesis, which can be applied to a wide range of problems. SN - 1471-2105 UR - https://doi.org/10.1186/1471-2105-15-108 DO - 10.1186/1471-2105-15-108 ID - Li2014 ER -