Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.

/pdf/gene-set-enrichment-analysis-a-knowledge-based-approach-for-mbpti6qljw.pdf

Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

THE DESIGN AND ANALYSIS OF EXPERIMENTS. By Oscar Kempthorne. New York, John Wiley and Sons, Inc., 1952. 631 pp. $8.50. This book by a teacher of statistics (as well as a consultant for \"experimenters\") is a comprehensive study of the philosophical background for the statistical design of experiment. It is necessary to have some facility with algebraic notation and manipulation to be able to use the volume intelligently. The problems are presented from the theoretical point of view, without such practical examples as would be helpful for those not acquainted with mathematics. The mathematical justification for the techniques is given. As a somewhat advanced treatment of the design and analysis of experiments, this volume will be interesting and helpful for many who approach statistics theoretically as well as practically. With emphasis on the \"why,\" and with description given broadly, the author relates the subject matter to the general theory of statistics and to the general problem of experimental inference. MARGARET J. ROBERTSON

The Design and Analysis of Experiments

We designed a 15-item neurologic examination stroke scale for use in acute stroke therapy trials. In a study of 24 stroke patients, interrater reliability for the scale was found to be high (mean kappa = 0.69), and test-retest reliability was also high (mean kappa = 0.66-0.77). Test-retest reliability did not differ significantly among a neurologist, a neurology house officer, a neurology nurse, or an emergency department nurse. The stroke scale validity was assessed by comparing the scale scores obtained prospectively on 65 acute stroke patients to the patients' infarction size as measured by computed tomography scan at 1 week and to the patients' clinical outcome as determined at 3 months. These correlations (scale-lesion size r = 0.68, scale-outcome r = 0.79) suggested acceptable examination and scale validity. Of the 15 test items, the most interrater reliable item (pupillary response) had low validity. Less reliable items such as upper or lower extremity motor function were more valid. We discuss methods for improving the reliability and validity of brief examination scales to be used in stroke therapy trials.

Measurements of acute cerebral infarction: a clinical examination scale.

To pursue a systematic approach to the discovery of functional connections among diseases, genetic perturbation, and drug action, we have created the first installment of a reference collection of gene-expression profiles from cultured human cells treated with bioactive small molecules, together with pattern-matching software to mine these data. We demonstrate that this "Connectivity Map" resource can be used to find connections among small molecules sharing a mechanism of action, chemicals and physiological processes, and diseases and drugs. These results indicate the feasibility of the approach and suggest the value of a large-scale community Connectivity Map project.

http://cinjweb.umdnj.edu/sites/molmdweb/documents/connectivitymap.pdf

The Connectivity Map: Using Gene-Expression Signatures to Connect Small Molecules, Genes, and Disease

a b s t r a c t The interest in nonparametric statistical analysis has grown recently in the field of computational intelligence. In many experimental studies, the lack of the required properties for a proper application of parametric procedures - independence, normality, and homoscedasticity - yields to nonparametric ones the task of performing a rigorous comparison among algorithms. In this paper, we will discuss the basics and give a survey of a complete set of nonparametric procedures developed to perform both pairwise and multiple comparisons, for multi-problem analysis. The test problems of the CEC'2005 special session on real parameter optimization will help to illustrate the use of the tests throughout this tutorial, analyzing the results of a set of well-known evolutionary and swarm intelligence algorithms. This tutorial is concluded with a compilation of considerations and recommendations, which will guide practitioners when using these tests to contrast their experimental results.

https://sci2s.ugr.es/sites/default/files/ficherosPublicaciones/1374_2011-Derrac-SWEVO.pdf

A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms

This Second Edition of Myles Hollander and Douglas A. Wolfe's successful Nonparametric Statistical Methods meets the needs of a new generation of users, with completely up-to-date coverage of this important statistical area. Like its predecessor, the revised edition, along with its companion ftp site, aims to equip readers with the conceptual and technical skills necessary to select and apply the appropriate procedures for a given situation. An extensive array of examples drawn from actual experiments illustrates clearly how to use nonparametric approaches to handle one- or two-sample location and dispersion problems, dichotomous data, and one-way and two-way layout problems. An ideal text for an upper-level undergraduate or first-year graduate course, Nonparametric Statistical Methods, Second Edition is also an invaluable source for professionals who want to keep abreast of the latest developments within this dynamic branch of modern statistics.

Nonparametric Statistical Methods

An asymptotically distribution-free procedure is proposed for testing whether a univariate population is symmetric about some unknown value versus a broad class of asymmetric distribution alternatives. The consistency class of the test is discussed and two competing tests are described, one based on the sample skewness, and the other on Gupta's nonparametric procedure. A Monte Carlo study shows that the proposed test is superior to either competitor since it maintains the designated α levels fairly accurately even for sample sizes as small as 20, while displaying good power for detecting asymmetric distributions.

https://www.jstor.org/stable/pdfplus/2287406.pdf

An Asymptotically Distribution-Free Test for Symmetry versus Asymmetry

Nonparametric Statistical Methods: Hollander/Nonparametric Statistical Methods

Distribution-free test procedures are proposed for the k-sample problem, where the alternatives of interest are of the form F 1 (x) ≥ ··· ≥ Fl(x) ≤ ··· ≤ Fk (x) for all x, with at least one strict inequality. These are referred to as umbrella alternatives, and both the l (point of umbrella) known and l unknown settings are considered. Small sample null distributions are discussed, and the results of a Monte Carlo power study are presented. A detailed example illustrating the use of the unknown umbrella point procedure is also given.

Douglas A. Wolfe

Papers

Nonparametric Statistical Methods

Nonparametric Statistical Methods

An Asymptotically Distribution-Free Test for Symmetry versus Asymmetry

Nonparametric Statistical Methods: Hollander/Nonparametric Statistical Methods

K-Sample Rank Tests for Umbrella Alternatives