Seungyeoun Lee

Evaluation of normalization methods for microarray data

BackgroundMicroarray technology allows the monitoring of expression levels for thousands of genes simultaneously. This novel technique helps us to understand gene regulation as well as gene by gene interactions more systematically. In the microarray experiment, however, many undesirable systematic variations are observed. Even in replicated experiment, some variations are commonly observed. Normalization is the process of removing some sources of variation which affect the measured gene expression levels. Although a number of normalization methods have been proposed, it has been difficult to decide which methods perform best. Normalization plays an important role in the earlier stage of microarray data analysis. The subsequent analysis results are highly dependent on normalization.ResultsIn this paper, we use the variability among the replicated slides to compare performance of normalization methods. We also compare normalization methods with regard to bias and mean square error using simulated data.ConclusionsOur results show that intensity-dependent normalization often performs better than global normalization methods, and that linear and nonlinear normalization methods perform similarly. These conclusions are based on analysis of 36 cDNA microarrays of 3,840 genes obtained in an experiment to search for changes in gene expression profiles during neuronal differentiation of cortical stem cells. Simulation studies confirm our findings.

A test of missing completely at random for longitudinal data with missing observations.

Liang and Zeger proposed a generalized estimating equations approach to the analysis of longitudinal data. Their models assume that missing observations are missing completely at random in the sense of Rubin. However, when this assumption does not hold, their analysis may yield biased results. In this paper, we develop a simple and practical procedure for testing this assumption. The proposed procedure is related to that of Park and Davis.

Simple pattern-mixture models for longitudinal data with missing observations: analysis of urinary incontinence data.

In longitudinal studies each subject is observed at several different times. Longitudinal studies are rarely balanced and complete due to occurrence of missing data. Little proposed pattern-mixture models for the analysis of incomplete multivariate normal data. Later, Little proposed an approach to modelling the drop-out mechanism based on the pattern-mixture models. We advocate the pattern-mixture models for analysing the longitudinal data with binary or Poisson responses in which the generalized estimating equations formulation of Liang and Zeger is sensible. The proposed method is illustrated with a real data set.

Identifying molecular subtypes related to clinicopathologic factors in pancreatic cancer

BackgroundPancreatic ductal adenocarcinoma (PDAC) is one of the most lethal tumors and usually presented with locally advanced and distant metastasis disease, which prevent curative resection or treatments. In this regard, we considered identifying molecular subtypes associated with clinicopathological factor as prognosis factors to stratify PDAC for appropriate treatment of patients.ResultsIn this study, we identified three molecular subtypes which were significant on survival time and metastasis. We also identified significant genes and enriched pathways represented for each molecular subtype. Considering R0 resection patients included in each subtype, metastasis and survival times are significantly associated with subtype 1 and subtype 2.ConclusionsWe observed three PDAC molecular subtypes and demonstrated that those subtypes were significantly related with metastasis and survival time. The study may have utility in stratifying patients for cancer treatment.

Gene–gene interaction analysis for the survival phenotype based on the Cox model

Motivation: For the past few decades, many statistical methods in genome-wide association studies (GWAS) have been developed to identify SNP–SNP interactions for case-control studies. However, there has been less work for prospective cohort studies, involving the survival time. Recently, Gui et al. (2011) proposed a novel method, called Surv-MDR, for detecting gene–gene interactions associated with survival time. Surv-MDR is an extension of the multifactor dimensionality reduction (MDR) method to the survival phenotype by using the log-rank test for defining a binary attribute. However, the Surv-MDR method has some drawbacks in the sense that it needs more intensive computations and does not allow for a covariate adjustment. In this article, we propose a new approach, called Cox-MDR, which is an extension of the generalized multifactor dimensionality reduction (GMDR) to the survival phenotype by using a martingale residual as a score to classify multi-level genotypes as high- and low-risk groups. The advantages of Cox-MDR over Surv-MDR are to allow for the effects of discrete and quantitative covariates in the frame of Cox regression model and to require less computation than Surv-MDR. Results: Through simulation studies, we compared the power of Cox-MDR with those of Surv-MDR and Cox regression model for various heritability and minor allele frequency combinations without and with adjusting for covariate. We found that Cox-MDR and Cox regression model perform better than Surv-MDR for low minor allele frequency of 0.2, but Surv-MDR has high power for minor allele frequency of 0.4. However, when the effect of covariate is adjusted for, Cox-MDR and Cox regression model perform much better than Surv-MDR. We also compared the performance of Cox-MDR and Surv-MDR for a real data of leukemia patients to detect the gene–gene interactions with the survival time. Contact: leesy@sejong.ac.kr; tspark@snu.ac.kr

Assailant and Victim Behaviors Immediately Preceding Inpatient Assault

The goals of this study were to detect and define victim and assailant behaviors immediately before inpatient assault and to determine their specificity to assault, to assailants, and to more serious assaults. Assault was defined as physical contact such as hitting and slapping and detected using video-cameras installed on a specialized ward for violent patients. Antecedent behaviors, or cues, were identified in victim and assailant in the five minutes preceding assault and in control periods, five minute segments in which both patients were present but no assault occurred. Fifty-five assaults between 59 patients were detected. Threatening and intrusive behaviors in assailant and victim preceded 60% of assaults and 10% of control periods. They were more than ten times more numerous before assaults. Assault was often the end of a set of overt, recognizable interactions between patients. In a chronically ill inpatient population these behaviors can be useful predictors of assault.

Diagnostic plots for detecting outlying slides in a cDNA microarray experiment

Different sources of systematic and random error variations are often observed in cDNA microarray experiments. A simple scatter plot is commonly used to examine outlying slides that have unusual expression patterns or larger variability than other slides. These outlying slides tend to have large impacts on the subsequent analyses, such as identification of differentially expressed genes and clustering analysis. However, it is difficult to select outlying slides rigorously and consistently based on subjective human pattern recognition on their scatter plots. A graphical method and a rigorous diagnostic measure are proposed to detect outlying slides. The proposed graphical method is easy to implement and shown to be quite effective in detecting outlying slides in real microarray data sets. This diagnostic measure is also informative to compare variability among slides. Two cDNA microarray data sets are carefully examined to illustrate the proposed approach. A 3840-gene microarray experiment for neuronal differentiation of cortical stem cells and a 2076-gene microarray experiment for anticancer compound time-course expression of the NCI-60 cancer cell lines.

A Simple Test for Independent Censoring Under the Proportional Hazards Model

A simple method for testing the assumption of independent censoring is developed using a Cox proportional hazards regression model with a time-dependent covariate. This method involves further follow-up of a subset of lost-to-follow-up censored subjects. An adjusted estimator of the survivor function is obtained for the dependent censoring model under a proportional hazards alternative. The proposed procedure is applied to an example of a clinical trial for lung cancer and a simulation study is given for investigating the power of the proposed test.

A comparative study on gene-set analysis methods for assessing differential expression associated with the survival phenotype

BackgroundMany gene-set analysis methods have been previously proposed and compared through simulation studies and analysis of real datasets for binary phenotypes. We focused on the survival phenotype and compared the performances of Gene Set Enrichment Analysis (GSEA), Global Test (GT), Wald-type Test (WT) and Global Boost Test (GBST) methods in a simulation study and on two ovarian cancer data sets. We considered two versions of GSEA by allowing different weights: GSEA1 uses equal weights, yielding results similar to the Kolmogorov-Smirnov test; while GSEA2s weights are based on the correlation between genes and the phenotype.ResultsWe compared GSEA1, GSEA2, GT, WT and GBST in a simulation study with various settings for the correlation structure of the genes and the association parameter between the survival outcome and the genes. Simulation results indicated that GT, WT and GBST consistently have higher power than GSEA1 and GSEA2 across all scenarios. However, the power of the five tests depends on the combination of correlation structure and association parameter. For the ovarian cancer data set, using the FDR threshold of q < 0.1, the GT, WT and GBST detected 12, 6 and 8 significant pathways, respectively, whereas neither GSEA1 nor GSEA2 detected any significant pathways. In addition, among the pathways found significant by GT, WT, and GBST, three pathways - Purine metabolism, Leukocyte transendothelial migration and Jak-STAT signaling pathway - overlapped with those reported in previous ovarian cancer microarray studies.ConclusionSimulation studies and a real data example indicate that GT, WT and GBST tend to have high power, whereas GSEA1 and GSEA2 have lower power. We also found that the power of the five tests is much higher when genes are correlated than when genes are independent, when survival is positively associated with genes. It seems that there is a synergistic effect in detecting significant gene sets when significant genes have within-class correlation and the association between survival and genes is positive or negative (i.e., one-direction correlation).

Goodness-of-fit tests for the additive risk model with (p > 2)-dimensional time-invariant covariates.

This paper presents methods for checking the goodness-of-fit of the additive risk model with p(> 2)-dimensional time-invariant covariates. The procedures are an extension of Kim and Lee (1996) who developed a test to assess the additive risk assumption for two-sample censored data. We apply the proposed tests to survival data from South Wales nikel refinery workers. Simulation studies are carried out to investigate the performance of the proposed tests for practical sample sizes.