Journal of Data Science

Abstract: This paper describes how to explore gene expression data using a combination of graphical and numerical methods. We start from the general methodology for multivariate data visualization, describing heatmaps, par allel coordinate plots and scatterplots. We propose new methods for gene expression data analysis using direct manipulation graphics. With linked scatterplots and parallel coordinate plots we explore gene expression data differently than many common practices. To check replicates in relation to treatments we introduce a new type of plot called a “replicate line” plot. There is a worked example, that focuses on an experimental study containing two two-level factors, genotype and cofactor presence, with two replicates.

Abstract: As a useful alternative to the Cox proportional hazards model, the linear regression survival model assumes a linear relationship between the covariates and a known monotone transformation, for example logarithm, of an event time of interest. In this article, we study the linear regression survival model with right censored survival data, when high-dimensional microarray measurements are present. Such data may arise in studies in vestigating the statistical influence of molecular features on survival risk. We propose using the principal component regression (PCR) technique for model reduction based on the weight least squared Stute estimate. Com pared with other model reduction techniques, the PCR approach is relatively insensitive to the number of covariates and hence suitable for high dimen sional microarray data. Component selection based on the nonparametric bootstrap, and model evaluation using the time-dependent ROC (receiver operating characteristic) technique are investigated. We demonstrate the proposed approach with datasets from two microarray gene expression pro filing studies of lymphoma cancers

Abstract: The chi-squared test for independence in two-way categorical tables depends on the assumptions that the data follow the multinomial distribution. Thus, we suggest alternatives when the assumptions of multi nomial distribution do not hold. First, we consider the Bayes factor which is used for hypothesis testing in Bayesian statistics. Unfortunately, this has the problem that it is sensitive to the choice of prior distributions. We note here that the intrinsic Bayes factor is not appropriate because the prior distribu tions under consideration are all proper. Thus, we propose using Bayesian estimation which is generally not as sensitive to prior specifications as the Bayes factor. Our approach is to construct a 95% simultaneous credible re gion (i.e., a hyper-rectangle) for the interactions. A test that all interactions are zero is equivalent to a test of independence in two-way categorical tables. Thus, a 95% simultaneous credible region of the interactions provides a test of independence by inversion.

Abstract: In recent years Singular Spectrum Analysis (SSA), used as a powerful technique in time series analysis, has been developed and applied to many practical problems. In this paper, the performance of the SSA tech nique has been considered by applying it to a well-known time series data set, namely, monthly accidental deaths in the USA. The results are com pared with those obtained using Box-Jenkins SARIMA models, the ARAR algorithm and the Holt-Winter algorithm (as described in Brockwell and Davis (2002)). The results show that the SSA technique gives a much more accurate forecast than the other methods indicated above.

Abstract: We have developed an automated linking scheme for PUBMED citations with GO terms using SVM (Support Vector Machine), a classifica tion algorithm. The PUBMED database has been essential to life science re searchers with over 12 million citations. More recently GO (Gene Ontology) has provided a graph structure for biological process, cellular component, and molecular function of genomic data. By text mining the textual content of PUBMED and associating them with GO terms, we have built up an ontological map for these databases so that users can search PUBMED via GO terms and conversely GO entries via PUBMED classification. Conse quently, some interesting and unexpected knowledge may be captured from them for further data analysis and biological experimentation. This paper reports our results on SVM implementation and the need to parallelize for the training phase.

Abstract: Latent class analysis (LCA) is a popular method for analyzing multiple categorical outcomes. Given the potential for LCA model assump tions to influence inference, model diagnostics are a particulary important part of LCA. We suggest using the rate of missing information as an addi tional diagnostic tool. The rate of missing information gives an indication of the amount of information missing as a result of observing multiple sur rogates in place of the underlying latent variable of interest and provides a measure of how confident one can be in the model results. Simulation studies and real data examples are presented to explore the usefulness of the proposed measure.

Abstract: In the natural history of Human Immunodeficiency Virus Type-1 (HIV-1) infection, many studies included the participants who were seropos itive at time of enrollment. Estimation of the unknown times since exposure to HIV-1 in the prevalent cohorts is of primary importance for estimation of the incubation period of Acquired Immunodeficiency Syndrome (AIDS). To estimate incubation period of AIDS we used prior distribution of incubation times, based on a external data as suggested by Bacchetti and Jewell (1991, Biometrics, 47,947-960). In the present study, our estimate was nonpara metric based on a method proposed by Wang, Jewell and Tsai (1986, Annals of Statistics, 14, 1597-1605).