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Biomedical subjects

James J Chen

Publications and source records attributed to James J Chen.

27 records · Page 2Linked to original sources

Dye bias correction in dual-labeled cDNA microarray gene expression measurements.

A significant limitation to the analytical accuracy and precision of dual-labeled spotted cDNA microarrays is the signal error due to dye bias. Transcript-dependent dye bias may be due to gene-specific differences of incorporation of two distinctly different chemical dyes and the resultant differential hybridization efficiencies of these two chemically different targets for the same probe. Several approaches were used to assess and minimize the effects of dye bias on fluorescent hybridization signals and maximize the experimental design efficiency of a cell culture experiment. Dye bias was measured at the individual transcript level within each batch of simultaneously processed arrays by replicate dual-labeled split-control sample hybridizations and accounted for a significant component of fluorescent signal differences. This transcript-dependent dye bias alone could introduce unacceptably high numbers of both false-positive and false-negative signals. We found that within a given set of concurrently processed hybridizations, the bias is remarkably consistent and therefore measurable and correctable. The additional microarrays and reagents required for paired technical replicate dye-swap corrections commonly performed to control for dye bias could be costly to end users. Incorporating split-control microarrays within a set of concurrently processed hybridizations to specifically measure dye bias can eliminate the need for technical dye swap replicates and reduce microarray and reagent costs while maintaining experimental accuracy and technical precision. These data support a practical and more efficient experimental design to measure and mathematically correct for dye bias.

Cell Culture Techniques↗

Testing for differentially expressed genes with microarray data.

This paper compares the type I error and power of the one- and two-sample t-tests, and the one- and two-sample permutation tests for detecting differences in gene expression between two microarray samples with replicates using Monte Carlo simulations. When data are generated from a normal distribution, type I errors and powers of the one-sample parametric t-test and one-sample permutation test are very close, as are the two-sample t-test and two-sample permutation test, provided that the number of replicates is adequate. When data are generated from a t-distribution, the permutation tests outperform the corresponding parametric tests if the number of replicates is at least five. For data from a two-color dye swap experiment, the one-sample test appears to perform better than the two-sample test since expression measurements for control and treatment samples from the same spot are correlated. For data from independent samples, such as the one-channel array or two-channel array experiment using reference design, the two-sample t-tests appear more powerful than the one-sample t-tests.

DNA, Complementary↗

Normalization methods for analysis of microarray gene-expression data.

This paper investigates subset normalization to adjust for location biases (e.g., splotches) combined with global normalization for intensity biases (e.g., saturation). A data set from a toxicogenomic experiment using the same control and the same treated sample hybridized to six different microarrays is used to contrast the different normalization methods. Simple t-tests were used to compare two samples for dye effects and for treatment effects. The numbers of genes that reproducibly showed significant p-values for the unnormalized data and normalized data from different methods were evaluated for assessment of different normalization methods. The one-sample t-statistic of the ratio of red to green samples was used to test for dye effects using only control data. For treatment effects, in addition to the one-sample t-test of the ratio of the treated to control samples, the two-sample t-test for testing the difference between treated and control samples was also used to compare the two approaches. The method that combines a subset approach (median or lowess fit) for location adjustment with a global lowess fit for intensity adjustment appears to perform well.

Bias↗

Comparison of methods for estimating the number of true null hypotheses in multiplicity testing.

When a large number of statistical tests is performed, the chance of false positive findings could increase considerably. The traditional approach is to control the probability of rejecting at least one true null hypothesis, the familywise error rate (FWE). To improve the power of detecting treatment differences, an alternative approach is to control the expected proportion of errors among the rejected hypotheses, the false discovery rate (FDR). When some of the hypotheses are not true, the error rate from either the FWE- or the FDR-controlling procedure is usually lower than the designed level. This paper compares five methods used to estimate the number of true null hypotheses over a large number of hypotheses. The estimated number of true null hypotheses is then used to improve the power of FWE- or FDR-controlling methods. Monte Carlo simulations are conducted to evaluate the performance of these methods. The lowest slope method, developed by Benjamini and Hochberg (2000) on the adaptive control of the FDR in multiple testing with independent statistics, and the mean of differences method appear to perform the best. These two methods control the FWE properly when the number of nontrue null hypotheses is small. A data set from a toxicogenomic microarray experiment is used for illustration.

Animals↗

Estimation of false discovery rates in multiple testing: application to gene microarray data.

Testing for significance with gene expression data from DNA microarray experiments involves simultaneous comparisons of hundreds or thousands of genes. If R denotes the number of rejections (declared significant genes) and V denotes the number of false rejections, then V/R, if R > 0, is the proportion of false rejected hypotheses. This paper proposes a model for the distribution of the number of rejections and the conditional distribution of V given R, V / R. Under the independence assumption, the distribution of R is a convolution of two binomials and the distribution of V / R has a noncentral hypergeometric distribution. Under an equicorrelated model, the distributions are more complex and are also derived. Five false discovery rate probability error measures are considered: FDR = E(V/R), pFDR = E(V/R / R > 0) (positive FDR), cFDR = E(V/R / R = r) (conditional FDR), mFDR = E(V)/E(R) (marginal FDR), and eFDR = E(V)/r (empirical FDR). The pFDR, cFDR, and mFDR are shown to be equivalent under the Bayesian framework, in which the number of true null hypotheses is modeled as a random variable. We present a parametric and a bootstrap procedure to estimate the FDRs. Monte Carlo simulations were conducted to evaluate the performance of these two methods. The bootstrap procedure appears to perform reasonably well, even when the alternative hypotheses are correlated (rho = .25). An example from a toxicogenomic microarray experiment is presented for illustration.

Bayes Theorem↗

Tests for equivalence or non-inferiority for paired binary data.

Assessment of therapeutic equivalence or non-inferiority between two medical diagnostic procedures often involves comparisons of the response rates between paired binary endpoints. The commonly used and accepted approach to assessing equivalence is by comparing the asymptotic confidence interval on the difference of two response rates with some clinical meaningful equivalence limits. This paper investigates two asymptotic test statistics, a Wald-type (sample-based) test statistic and a restricted maximum likelihood estimation (RMLE-based) test statistic, to assess equivalence or non-inferiority based on paired binary endpoints. The sample size and power functions of the two tests are derived. The actual type I error and power of the two tests are computed by enumerating the exact probabilities in the rejection region. The results show that the RMLE-based test controls type I error better than the sample-based test. To establish an equivalence between two treatments with a symmetric equivalence limit of 0.15, a minimal sample size of 120 is needed. The RMLE-based test without the continuity correction performs well at the boundary point 0. A numerical example illustrates the proposed procedures.

Biopsy↗

Age-adjusted exact trend tests in the event of rare occurrences.

Preclinical animal carcinogenicity studies are usually concerned with testing the statistical significance of a dose-response relationship. When the response consists of a rare event such as the development of a certain type of tumor, exact statistical methods are often employed. The exact randomization trend test based on the multivariate hypergeometric distribution is less powerful in the presence of treatment-related risks other than the specified response. Particularly, the loss of power becomes more pronounced when competing risks cause progressively higher mortality rates with increasing dose, which is usual in practice. An age-adjusted form of the randomization test is proposed to adjust for this effect. Permutational distribution for Peto's cause-of-death (COD) test is also explored and compared with its asymptotic counterpart by simulation. The use of COD information has been a controversial issue due to the subjectivity in the pathologists' determinations as well as for economic reasons. The proposed age-adjusted exact test does not require COD, and it is shown to compare favorably to the COD tests via an extensive Monte Carlo simulation. Applications of the methods to two real data sets are included.

Age Factors↗

Mutagenicity of food-derived carcinogens and the effect of antioxidant vitamins.

The food-derived heterocyclic amines (HCAs) 2-amino-3-methylimidazo[4,5-f]quinoline (IQ), 2-amino-3,4-dimethylimidazo[4,5-f]quinoline (MeIQ), 2-amino-3,8-dimethylimidazo[4,5-f]quinoxaline (MeIQx), and 2-amino-1-methyl-6-phenylimidazo[4,5-b]pyridine (PhIP) are mutagenic in the Ames test and produce tumors in laboratory animals, including monkeys. These HCAs have also been shown to induce gene mutations in vivo. To assess the antimutagenic effects of dietary antioxidant vitamins, beta-carotene, ascorbic acid (vitamin C), and alpha-tocopherol (vitamin E), on food-borne mutagenes/carcinogens, we evaluated the mutagenic activity of the compounds alone or combined with antioxidant vitamins. We utilized the rat lymphocyte mutation assay at the hypoxanthine guanine phosphoribosyl transferase (Hprt) locus. Female Fischer 344 rats treated with different doses (0, 2.5, 5.0, 25.0, and 50.0 mg/kg) of the carcinogens were sacrificed 5 wk after mutagen treatment. Although IQ and MeIQ slightly increased mutation frequency (MF) at some doses, a significant (P < 0.0009) increase in MF was found in animals exposed to MeIQx at 25 mg/kg. PhIP was the most mutagenic of the HCAs, with increases (P < 0.0001) in MF detected at all dose levels compared with controls. Because PhIP was the most mutagenic, it was selected for studies using the dietary antioxidant vitamins. Addition of antioxidant vitamins, singly or in a mixture, caused a significant (P < 0.0001) decrease in PhIP-induced Hprt MF. Vitamin E was the most effective at decreasing Hprt MF. In addition, we determined whether carcinogen metabolism would be affected by ingestion of vitamins. The activities of endogenous detoxification enzymes, glutathione S-transferase and glutathione peroxidase (GPx), were thus examined. Intake of beta-carotene and vitamin C without the carcinogen resulted in an increase (P < 0.05) in GPx activity. Also a modest increase in GPx activity was seen in animals that received the antioxidant mixture alone. Although the mechanisms of action of the antioxidants remain to be determined, the results indicate that dietary-derived HCA treatment induced MF in rat lymphocytes and suggest that antioxidants in food or taken as supplements could, in part, counteract such mutagenic activities.

Animals↗

Alterations in gene expression profiles and the DNA-damage response in ionizing radiation-exposed TK6 cells.

Identifying genes that are differentially expressed in response to DNA damage may help elucidate markers for genetic damage and provide insight into the cellular responses to specific genotoxic agents. We utilized cDNA microarrays to develop gene expression profiles for ionizing radiation-exposed human lymphoblastoid TK6 cells. In order to relate changes in the expression profiles to biological responses, the effects of ionizing radiation on cell viability, cloning efficiency, and micronucleus formation were measured. TK6 cells were exposed to 0.5, 1, 5, 10, and 20 Gy ionizing radiation and cultured for 4 or 24 hr. A significant (P < 0.0001) decrease in cloning efficiency was observed at all doses at 4 and 24 hr after exposure. Flow cytometry revealed significant decreases in cell viability at 24 hr in cells exposed to 5 (P < 0.001), 10 (P < 0.0001), and 20 Gy (P < 0.0001). An increase in micronucleus frequency occurred at both 4 and 24 hr at 0.5 and 1 Gy; however, insufficient binucleated cells were present for analysis at the higher doses. Gene expression profiles were developed from mRNA isolated from cells exposed to 5, 10, and 20 Gy using a 350 gene human cDNA array platform. Overall, more genes were differentially expressed at 24-hr than at the 4-hr time point. The genes upregulated (> 1.5-fold) or downregulated (< 0.67-fold) at 4 hr were those primarily involved in the cessation of the cell cycle, cellular detoxification pathways, DNA repair, and apoptosis. At 24 hr, glutathione-associated genes were induced in addition to genes involved in apoptosis. Genes involved in cell cycle progression and mitosis were downregulated at 24 hr. Real-time quantitative PCR was used to confirm the microarray results and to evaluate expression levels of selected genes at the low doses (0.5 and 1.0 Gy). The expression profiles reflect the cellular and molecular responses to ionizing radiation related to the recognition of DNA damage, a halt in progression through the cell cycle, activation of DNA-repair pathways, and the promotion of apoptosis.

Analysis of Variance↗