ROAST: rotation gene set tests for complex microarray experiments
نویسندگان
چکیده
MOTIVATION A gene set test is a differential expression analysis in which a P-value is assigned to a set of genes as a unit. Gene set tests are valuable for increasing statistical power, organizing and interpreting results and for relating expression patterns across different experiments. Existing methods are based on permutation. Methods that rely on permutation of probes unrealistically assume independence of genes, while those that rely on permutation of sample are suitable only for two-group comparisons with a good number of replicates in each group. RESULTS We present ROAST, a statistically rigorous gene set test that allows for gene-wise correlation while being applicable to almost any experimental design. Instead of permutation, ROAST uses rotation, a Monte Carlo technology for multivariate regression. Since the number of rotations does not depend on sample size, ROAST gives useful results even for experiments with minimal replication. ROAST allows for any experimental design that can be expressed as a linear model, and can also incorporate array weights and correlated samples. ROAST can be tuned for situations in which only a subset of the genes in the set are actively involved in the molecular pathway. ROAST can test for uni- or bi-direction regulation. Probes can also be weighted to allow for prior importance. The power and size of the ROAST procedure is demonstrated in a simulation study, and compared to that of a representative permutation method. Finally, ROAST is used to test the degree of transcriptional conservation between human and mouse mammary stems. AVAILABILITY ROAST is implemented as a function in the Bioconductor package limma available from www.bioconductor.org.
منابع مشابه
Statistical power of gene-set enrichment analysis is a function of gene set correlation structure
We develop an analytic statistical framework for examining a variety of gene-set enrichment analysis tests. Within this framework, we describe why statistical power for both self-contained and competitive gene set tests is a function of the correlation structure of co-expressed genes, and why this characteristic is undesireable for gene-set analyses. We additionally describe why past gene-set t...
متن کاملGlobal gene expression analysis using microarray to study differential vulnerability to neurodegeneration
Neurodegenerative disorders such as Parkinson’s disease, motor neuron disease and Alzheimer’s disease is characterized by loss of specific cells within certain regions of the brain. One of the most compelling questions is to determine why specific cell populations are vulnerable to neurodegeneration. We addressed this question by studying global gene expression changes using an animal model of ...
متن کاملGlobal gene expression analysis using microarray to study differential vulnerability to neurodegeneration
Neurodegenerative disorders such as Parkinson’s disease, motor neuron disease and Alzheimer’s disease is characterized by loss of specific cells within certain regions of the brain. One of the most compelling questions is to determine why specific cell populations are vulnerable to neurodegeneration. We addressed this question by studying global gene expression changes using an animal model of ...
متن کاملGene Identification from Microarray Data for Diagnosis of Acute Myeloid and Lymphoblastic Leukemia Using a Sparse Gene Selection Method
Background: Microarray experiments can simultaneously determine the expression of thousands of genes. Identification of potential genes from microarray data for diagnosis of cancer is important. This study aimed to identify genes for the diagnosis of acute myeloid and lymphoblastic leukemia using a sparse feature selection method. Materials and Methods: In this descriptive study, the expressio...
متن کاملIntegration and Reduction of Microarray Gene Expressions Using an Information Theory Approach
The DNA microarray is an important technique that allows researchers to analyze many gene expression data in parallel. Although the data can be more significant if they come out of separate experiments, one of the most challenging phases in the microarray context is the integration of separate expression level datasets that have gathered through different techniques. In this paper, we prese...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره 26 شماره
صفحات -
تاریخ انتشار 2010