BACKGROUND: The information from different data sets experimented under different conditions may be inconsistent even though they are performed with the same research objectives. More than that, even when the data sets were generated from the same platform, the data agreement may be affected by the technical variation among the laboratories. In this case, it is necessary to use the combined data set after adjusting the differences between such data sets, for detecting the more reliable information. RESULTS: The proposed method combines data sets posterior to the discretization of data sets based on the ranks of the gene expression ratios, and the statistical method is applied to the combined data set for predictive gene selection. The efficiency of the proposed method was evaluated using five colon cancer related data sets, which were experimented using cDNA microarrays with different RNA sources, and one experiment utilized oligonucleotide arrays. NCI-60 cell lines data sets were used, which were performed with two different platforms of cDNA microarrays and Affymetrix HU6800 oligonucleotide arrays. The combined data set by the proposed method predicted the test data sets more accurately than the separated data sets did. The biological significant genes were detected from the combined data set, which were missed on the separated data sets. CONCLUSION: By transforming gene expressions using ranks, the proposed method is not influenced by systematic bias among chips and normalization method. The method may be especially more useful to find predictive genes from data sets which have different scale in gene expressions.

BACKGROUND: Since the completion of the HapMap project, huge numbers of individual genotypes have been generated from many kinds of laboratories. The efforts of finding or interpreting genetic association between disease and SNPs/haplotypes have been on-going widely. So, the necessity of the capability to analyze huge data and diverse interpretation of the results are growing rapidly. RESULTS: We have developed an advanced tool to perform linkage disequilibrium analysis, and genetic association analysis between disease and SNPs/haplotypes in an integrated web interface. It comprises of four main analysis modules: (i) data import and preprocessing, (ii) haplotype estimation, (iii) LD blocking and (iv) association analysis. Hardy-Weinberg Equilibrium test is implemented for each SNPs in the data preprocessing. Haplotypes are reconstructed from unphased diploid genotype data, and linkage disequilibrium between pairwise SNPs is computed and represented by D', r2 and LOD score. Tagging SNPs are determined by using the square of Pearson's correlation coefficient (r2). If genotypes from two different sample groups are available, diverse genetic association analyses are implemented using additive, codominant, dominant and recessive models. Multiple verified algorithms and statistics are implemented in parallel for the reliability of the analysis. CONCLUSION: SNPAnalyzer 2.0 performs linkage disequilibrium analysis and genetic association analysis in an integrated web interface using multiple verified algorithms and statistics. Diverse analysis methods, capability of handling huge data and visual comparison of analysis results are very comprehensive and easy-to-use.

BACKGROUND: With microarray technology, variability in experimental environments such as RNA sources, microarray production, or the use of different platforms, can cause bias. Such systematic differences present a substantial obstacle to the analysis of microarray data, resulting in inconsistent and unreliable information. Therefore, one of the most pressing challenges in the field of microarray technology is how to integrate results from different microarray experiments or combine data sets prior to the specific analysis. RESULTS: Two microarray data sets based on a 17k cDNA microarray system were used, consisting of 82 normal colon mucosa and 72 colorectal cancer tissues. Each data set was prepared from either total RNA or amplified mRNA, and the difference of RNA source between these two data sets was detected by ANOVA (Analysis of variance) model. A simple integration method was introduced which was based on the distributions of gene expression ratios among different microarray data sets. The method transformed gene expression ratios into the form of a reference data set on a gene by gene basis. Hierarchical clustering analysis, density and box plots, and mixture scores with correlation coefficients revealed that the two data sets were well intermingled, indicating that the proposed method minimized the experimental bias. In addition, any RNA source effect was not detected by the proposed transformation method. In the mixed data set, two previously identified subgroups of normal and tumor were well separated, and the efficiency of integration was more prominent in tumor groups than normal groups. The transformation method was slightly more effective when a data set with strong homogeneity in the same experimental group was used as a reference data set. CONCLUSION: Proposed method is simple but useful to combine several data sets from different experimental conditions. With this method, biologically useful information can be detectable by applying various analytic methods to the combined data set with increased sample size.

MOTIVATION: It is a common practice in cancer microarray experiments that a normal tissue is collected from the same individual from whom the tumor tissue was taken. The indirect design is usually adopted for the experiment that uses a common reference RNA hybridized both to normal and tumor tissues. However, it is often the case that the test material is not large enough for the experimenter to extract enough RNA to conduct the microarray experiment. Hence, collecting n cases does not necessarily end up with a matched pair sample of size n. Instead we usually have a matched pair sample of size n1, and two independent samples of sizes n2 and n3, respectively, for 'reference versus normal tissue only' and 'reference versus tumor tissue only' hybridizations (n=n1 + n2 + n3). Standard statistical methods need to be modified and new statistical procedures are developed for analyzing this mixed dataset. RESULTS: We propose a new test statistic, t3, as a means of combining all the information in the mixed dataset for detecting differentially expressed (DE) genes between normal and tumor tissues. We employed the extended receiver operating characteristic approach to the mixed dataset. We devised a measure of disagreement between a RT-PCR experiment and a microarray experiment. Hotelling's T2 statistic is employed to detect a set of DE genes and its prediction rate is compared with the prediction rate of a univariate procedure. We observe that Hotelling's T2 statistic detects DE genes more efficiently than a univariate procedure and that further research is warranted on the formal test procedure using Hotelling's T2 statistic. CONTACT: [email protected].