Arguments

Description This package has been prepared to assist users in computing either a sample size or power value for a microarray experimental study. The user is referred to the cited references for technical background on the methodology underpinning these calculations. This package provides support for five types of sample size and power calculations. These five types can be adapted in various ways to encompass many of the standard designs encountered in practice. License LGPL biocViews Microarray R topics documented: power.matched....................................... 2 power.multi......................................... 3 power.randomized...................................... 4 sampleSize.matched..................................... 5 sampleSize.randomized...................................

SPCalc: A Web-based Calculator for Sample Size and Power Calculations in Micro-array Studies

Calculation of the appropriate sample size in planning microarray studies is important because sample collection can be expensive and time-consuming. Sample-size calculation is also a challenging issue for microarray studies because the number of genes is far larger than the number of samples so that traditional methods of sample-size calculation cannot be directly applied. To help investigators answer the question of how many samples are needed in their microarray studies, we developed a user-friendly web-based calculator, SPCalc, for calculating sample size and power for a variety of commonly used experimental designs, including completely randomized treatment control design, matched-pairs design, multiple-treatment design having an isolated treatment effect, and randomized block design

clues: An R Package for Nonparametric Clustering Based on Local Shrinking

Determining the optimal number of clusters appears to be a persistent and controversial issue in cluster analysis. Most existing R packages targeting clustering require the user to specify the number of clusters in advance. However, if this subjectively chosen number is far from optimal, clustering may produce seriously misleading results. In order to address this vexing problem, we develop the R package clues to automate and evaluate the selection of an optimal number of clusters, which is widely applicable in the field of clustering analysis. Package clues uses two main procedures, shrinking and partitioning, to estimate an optimal number of clusters by maximizing an index function, either the CH index or the Silhouette index, rather than relying on guessing a pre-specified number. Five agreement indices (Rand index, Hubert and ArabieâÂÂs adjusted Rand index, Morey and AgrestiâÂÂs adjusted Rand index, Fowlkes and Mallows index and Jaccard index), which measure the degree of agreement between any two partitions, are also provided in clues. In addition to numerical evidence, clues also supplies a deeper insight into the partitioning process with trajectory plots.

clues: An R Package for Nonparametric Clustering Based on Local Shrinking

Determining the optimal number of clusters appears to be a persistent and controversial issue in cluster analysis. Most existing R packages targeting clustering require the user to specify the number of clusters in advance. However, if this subjectively chosen number is far from optimal, clustering may produce seriously misleading results. In order to address this vexing problem, we develop the R package clues to automate and evaluate the selection of an optimal number of clusters, which is widely applicable in the field of clustering analysis. Package clues uses two main procedures, shrinking and partitioning, to estimate an optimal number of clusters by maximizing an index function, either the CH index or the Silhouette index, rather than relying on guessing a pre-specified number. Five agreement indices (Rand index, Hubert and Arabie's adjusted Rand index, Morey and Agresti's adjusted Rand index, Fowlkes and Mallows index and Jaccard index), which measure the degree of agreement between any two partitions, are also provided in clues. In addition to numerical evidence, clues also supplies a deeper insight into the partitioning process with trajectory plots

An efficient mutagenesis system to improve the propamocarb tolerance in Lecanicillium lecanii (Zimmermann) Zare & Gams

Lecanicillium lecanii (Zimmermann) Zare & Gams is used as an effective biopesticide for the control of sap-sucking insect pests on agricultural crops. However, low fungicide tolerance limits its large-scale field application. To improve the propamocarb tolerance in L. lecanii, a composite mutagenesis system was established by using UV-light (U), N-Methyl-N′-nitro-N-nitrosoguanidine (NTG) (N) and N+ ion-beam (I). The permutation type of three agents was a consecutive mutagenesis treatment (I/N/U) after an intermittent treatment (U + N + I). The “U” mutagenesis was performed at 254 nm for 60 s and at a distance of 45 cm under a 20 W germicidal lamp, the “N” mutagenesis was performed at a concentration of 1.0 mg/mL NTG for 60 min, and the “I” mutagenesis was performed by low energy N+ ion-beam using a dose of 10 × 1013 ions/cm2 at 30 keV. This composite mutagenesis system was recorded as the “U + N + I + I/N/U,” and then the mutagenesis efficiency in improving propamocarb tolerance was assessed by analyzing changes of mutants in the propamocarb sensitivity, mitotic stability, mycelial growth speed on plates or in liquid, sporulation on plates or aphids, conidial germination, 50% lethal concentration (LC50) and 50% lethal time (LT50) to aphids, lipid constituent and cell membrane permeability and control against aphids in the presence or absence of propamocarb. Compared to the wild-type isolate with a 50% effective concentration (EC50) value of 503.6 μg/mL propamocarb, the Ll-IC-UNI produced by the “U + N + I + I/N/U” had the highest EC50 value of 3576.4 μg/mL and a tolerance ratio of 7.1. The mutant was mitotically stable in 20-passage cultivation and did not show any unfavorable changes in growth and virulence indicators. The mutant showed the highest ability to resist or avoid the damaging effects of propamocarb as reflected by the alternations of lipid constituents and membrane permeability. The interval time for applying fungal agent was significantly shortened in this mutant after spraying a field recommended dose of 550 μg/mL propamocarb. In conclude, the “U + N + I + I/N/U” composite mutagenesis mode was efficient and useful to improve the propamocarb-tolerance of L. lecanii and the obtained Ll-IC-UNI could have commercial potential for field application

Correcting AUC for Measurement Error

Diagnostic biomarkers are used frequently in epidemiologic and clinical work. The ability of a diagnostic biomarker to discriminate between subjects who develop disease (cases) and subjects who do not (controls) is often measured by the area under the receiver operating characteristic curve (AUC). The diagnostic biomarkers are usually measured with error. Ignoring measurement error can cause biased estimation of AUC, which results in misleading interpretation of the efficacy of a diagnostic biomarker. Several methods have been proposed to correct AUC for measurement error, most of which required the normality assumption for the distributions of diagnostic biomarkers. In this article, we propose a new method to correct AUC for measurement error and derive approximate confidence limits for the corrected AUC. The proposed method does not require the normality assumption. Both real data analyses and simulation studies show good performance of the proposed measurement error correction method

Differential DNA methylation marks and gene comethylation of COPD in African-Americans with COPD exacerbations

Supplementary data and methods are provided in Additional file 1. This file includes additional methodological details related to study design, Tables S1 and S2, and Figures S1-S3. (DOCX 3650 kb