A permutation-based multiple testing method for time-course microarray experiments

<p>Abstract</p> <p>Background</p> <p>Time-course microarray experiments are widely used to study the temporal profiles of gene expression. Storey <it>et al</it>. (2005) developed a method for analyzing time-course microarray studies that can be applied to discovering genes whose expression trajectories change over time within a single biological group, or those that follow different time trajectories among multiple groups. They estimated the expression trajectories of each gene using natural cubic splines under the null (no time-course) and alternative (time-course) hypotheses, and used a goodness of fit test statistic to quantify the discrepancy. The null distribution of the statistic was approximated through a bootstrap method. Gene expression levels in microarray data are often complicatedly correlated. An accurate type I error control adjusting for multiple testing requires the joint null distribution of test statistics for a large number of genes. For this purpose, permutation methods have been widely used because of computational ease and their intuitive interpretation.</p> <p>Results</p> <p>In this paper, we propose a permutation-based multiple testing procedure based on the test statistic used by Storey <it>et al</it>. (2005). We also propose an efficient computation algorithm. Extensive simulations are conducted to investigate the performance of the permutation-based multiple testing procedure. The application of the proposed method is illustrated using the <it>Caenorhabditis elegans </it>dauer developmental data.</p> <p>Conclusion</p> <p>Our method is computationally efficient and applicable for identifying genes whose expression levels are time-dependent in a single biological group and for identifying the genes for which the time-profile depends on the group in a multi-group setting.</p

Immunomodulatory effect of water soluble extract separated from mycelium of Phellinus linteus on experimental atopic dermatitis

Background: Complementary and alternative medicine (CAM) is becoming a popular treatment for modulating diverse immune disorders. Phellinus linteus (P. linteus) as one of the CAMs has been used to modulate cancers, inflammation and allergic activities. However, little evidence has been shown about its underlying mechanism of action by which it exerts a beneficial role in dermatological disease in vivo. In this study, we examined the immunomodulatory effects of P. linteus on experimental atopic dermatitis (AD) and elucidated its action mechanism. Methods: The immunomodulatory effect of total extract of P. linteus on IgE production by human myeloma U266B1 cells was measured by ELISA. To further identify the effective components, P. linteus was fractionated into methanol soluble, water soluble and boiling water soluble extracts. Each extract was treated to U266B1 cells and primary B cells to compare their inhibitory effects on IgE secretion. To test the in vivo efficacy, experimental atopic dermatitis (AD) was established by alternative treatment of DNCB and house dust mite extract into BALB/c mice. Water soluble extract of P. linteus (WA) or ceramide as a positive control were topically applied to ears of atopic mouse every day for 2 weeks and progression of the disease was estimated by the following criteria: (a) ear thickness, clinical score, (b) serum total IgE, IgG and mite specific IgE level by ELSIA, (c) histological examination of ear tissue by H&E staining and (d) cytokine profile of total ear cells and CD4+ T cells by real time PCR and ELSIA. Results: Treatment of total extracts of P. linteus to U266B1 inhibited IgE secretion. Among the diverse extracts of P. linteus, water soluble extract of P. linteus (WA) significantly reduced the IgE production in primary B cells and B cell line U266B1. Moreover, treatment of WA reduced AD symptoms such as ear swelling, erythema, and dryness and decreased recruitment of lymphocyte into the inflamed site. Interestingly WA treatment significantly reduced IgE level without affecting IgG levels and also down-regulated the levels of pathogenic cytokines (IL-4, IL-13, IL-12 and IFN-γ) and chemokines (CCL17 and CCL22) involved in AD development. Conclusions: Our study indicates that protective effect of water soluble extract of P. linteus in atopic dermatitis is mediated by inhibiting IgE production and expression of AD associated pathogenic cytokines as well as chemokines, suggesting the beneficial effect of P. linteus to modulate allergic skin disease

Nonparametric inference on median residual life function

Summary. A simple approach to the estimation of the median residual lifetime is proposed for a single group by inverting a function of the Kaplan-Meier estimators. A test statistic is proposed to compare two median residual lifetimes at any fixed time point. The test statistic does not involve estimation of the underlying probability density function of failure times under censoring. Extensive simulation studies are performed to validate the proposed test statistic in terms of type I error probabilities and powers at various time points. One of the oldest data sets from the National Surgical Adjuvant Breast and Bowel Project (NSABP), which has more than a quarter century of follow-up, is used to illustrate the method. The analysis results indicate that, without systematic post-operative therapy, a significant difference in median residual lifetimes between node-negative and node-positive breast cancer patients persists for about 10 years after surgery. The new estimates of the median residual lifetime could serve as a baseline for physicians to explain any incremental effects of post-operative treatments in terms of delaying breast cancer recurrence or prolonging remaining lifetimes of breast cancer patients

Nonparametric inference on median residual life function

Summary. A simple approach to the estimation of the median residual lifetime is proposed for a single group by inverting a function of the Kaplan-Meier estimators. A test statistic is proposed to compare two median residual lifetimes at any fixed time point. The test statistic does not involve estimation of the underlying probability density function of failure times under censoring. Extensive simulation studies are performed to validate the proposed test statistic in terms of type I error probabilities and powers at various time points. One of the oldest data sets from the National Surgical Adjuvant Breast and Bowel Project (NSABP), which has more than a quarter century of follow-up, is used to illustrate the method. The analysis results indicate that, without systematic post-operative therapy, a significant difference in median residual lifetimes between node-negative and node-positive breast cancer patients persists for about 10 years after surgery. The new estimates of the median residual lifetime could serve as a baseline for physicians to explain any incremental effects of post-operative treatments in terms of delaying breast cancer recurrence or prolonging remaining lifetimes of breast cancer patients

Prediction of a time-to-event trait using genome wide SNP data

BACKGROUND: A popular objective of many high-throughput genome projects is to discover various genomic markers associated with traits and develop statistical models to predict traits of future patients based on marker values. RESULTS: In this paper, we present a prediction method for time-to-event traits using genome-wide single-nucleotide polymorphisms (SNPs). We also propose a MaxTest associating between a time-to-event trait and a SNP accounting for its possible genetic models. The proposed MaxTest can help screen out nonprognostic SNPs and identify genetic models of prognostic SNPs. The performance of the proposed method is evaluated through simulations. CONCLUSIONS: In conjunction with the MaxTest, the proposed method provides more parsimonious prediction models but includes more prognostic SNPs than some naive prediction methods. The proposed method is demonstrated with real GWAS data

Multiple testing for gene sets from microarray experiments

<p>Abstract</p> <p>Background</p> <p>A key objective in many microarray association studies is the identification of individual genes associated with clinical outcome. It is often of additional interest to identify sets of genes, known a priori to have similar biologic function, associated with the outcome.</p> <p>Results</p> <p>In this paper, we propose a general permutation-based framework for gene set testing that controls the false discovery rate (FDR) while accounting for the dependency among the genes within and across each gene set. The application of the proposed method is demonstrated using three public microarray data sets. The performance of our proposed method is contrasted to two other existing Gene Set Enrichment Analysis (GSEA) and Gene Set Analysis (GSA) methods.</p> <p>Conclusions</p> <p>Our simulations show that the proposed method controls the FDR at the desired level. Through simulations and case studies, we observe that our method performs better than GSEA and GSA, especially when the number of prognostic gene sets is large.</p

SNP Selection in Genome-Wide Association Studies via Penalized Support Vector Machine with MAX Test

One of main objectives of a genome-wide association study (GWAS) is to develop a prediction model for a binary clinical outcome using single-nucleotide polymorphisms (SNPs) which can be used for diagnostic and prognostic purposes and for better understanding of the relationship between the disease and SNPs. Penalized support vector machine (SVM) methods have been widely used toward this end. However, since investigators often ignore the genetic models of SNPs, a final model results in a loss of efficiency in prediction of the clinical outcome. In order to overcome this problem, we propose a two-stage method such that the the genetic models of each SNP are identified using the MAX test and then a prediction model is fitted using a penalized SVM method. We apply the proposed method to various penalized SVMs and compare the performance of SVMs using various penalty functions. The results from simulations and real GWAS data analysis show that the proposed method performs better than the prediction methods ignoring the genetic models in terms of prediction power and selectivity

A SAS macro for a clustered logrank test

The clustered logrank test is a nonparametric method of significance testing for correlated survival data. Examples of its application include cluster randomized trials where groups of patients rather than individuals are randomized to either a treatment or control intervention. We describe a SAS macro that implements the 2-sample clustered logrank test for data where the entire cluster is randomized to the same treatment group. We discuss the theory and applications behind this test as well as details of the SAS code

Are coveralls required as personal protective equipment during the management of COVID-19 patients?

Objectives Few studies have investigated the contamination of personal protective equipment (PPE) during the management of patients with severe-to-critical coronavirus disease (COVID-19). This study aimed to determine the necessity of coveralls and foot covers for body protection during the management of COVID-19 patients. Methods PPE samples were collected from the coveralls of physicians exiting a room after the management of a patient with severe-to-critical COVID-19 within 14 days after the patient’s symptom onset. The surface of coveralls was categorized into coverall-only parts (frontal surface of the head, anterior neck, dorsal surface of the foot cover, and back and hip) and gown-covered parts (the anterior side of the forearm and the abdomen). Sampling of the high-contact surfaces in the patient’s environment was performed. We attempted to identify significant differences in contamination with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) between the coverall-only and gown-covered parts. Results A total of 105 swabs from PPEs and 28 swabs from patient rooms were collected. Of the PPE swabs, only three (2.8%) swabs from the gown-covered parts were contaminated with SARS-CoV-2. However, 23 of the 28 sites (82.1%) from patient rooms were contaminated. There was a significant difference in the contamination of PPE between the coverall-only and gown-covered parts (0.0 vs 10.0%, p = 0.022). Conclusions Coverall contamination rarely occurred while managing severe-to-critical COVID-19 patients housed in negative pressure rooms in the early stages of the illness. Long-sleeved gowns may be used in the management of COVID-19 patients.This work was supported by Grant No. 02-2020-020 from the SNUBH Research Fund