Reuse of imputed data in microarray analysis increases imputation efficiency

BACKGROUND: The imputation of missing values is necessary for the efficient use of DNA microarray data, because many clustering algorithms and some statistical analysis require a complete data set. A few imputation methods for DNA microarray data have been introduced, but the efficiency of the methods was low and the validity of imputed values in these methods had not been fully checked. RESULTS: We developed a new cluster-based imputation method called sequential K-nearest neighbor (SKNN) method. This imputes the missing values sequentially from the gene having least missing values, and uses the imputed values for the later imputation. Although it uses the imputed values, the efficiency of this new method is greatly improved in its accuracy and computational complexity over the conventional KNN-based method and other methods based on maximum likelihood estimation. The performance of SKNN was in particular higher than other imputation methods for the data with high missing rates and large number of experiments. Application of Expectation Maximization (EM) to the SKNN method improved the accuracy, but increased computational time proportional to the number of iterations. The Multiple Imputation (MI) method, which is well known but not applied previously to microarray data, showed a similarly high accuracy as the SKNN method, with slightly higher dependency on the types of data sets. CONCLUSIONS: Sequential reuse of imputed data in KNN-based imputation greatly increases the efficiency of imputation. The SKNN method should be practically useful to save the data of some microarray experiments which have high amounts of missing entries. The SKNN method generates reliable imputed values which can be used for further cluster-based analysis of microarray data

A Comparison of Dimensional Standard of Several Nickel-Titanium Rotary Files

Objectives The aim of this study was to compare the dimensional standard of several nickel-titanium (Ni-Ti) rotary files and verify the size conformity. Materials and Methods ProFile (Dentsply Maillefer), RaCe (FKG Dentaire), and TF file (SybronEndo) #25 with a 0.04 and 0.06 taper were investigated, with 10 in each group for a total of 60 files. Digital images of Ni-Ti files were captured under light microscope (SZX16, Olympus) at 32×. Taper and diameter at D1 to D16 of each files were calculated digitally with AnalySIS TS Materials (OLYMPUS Soft Imaging Solutions). Differences in taper, the diameter of each level (D1 to D16) at 1 mm interval from (ANSI/ADA) specification No. 101 were statistically analyzed using one-way ANOVA and Scheffe\u27s post-hoc test at 95% confidence level. Results TF was the only group not conform to the nominal taper in both tapers (p \u3c 0.05). All groups except 0.06 taper ProFile showed significant difference from the nominal diameter (p \u3c 0.05). Conclusions Actual size of Ni-Ti file, especially TF, was different from the manufacturer\u27s statements

Improving the prediction accuracy in classification using the combined data sets by ranks of gene expressions

<p>Abstract</p> <p>Background</p> <p>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.</p> <p>Results</p> <p>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.</p> <p>Conclusion</p> <p>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.</p

Reliability Analysis on Flexural Behavior of FRP Bridge Decks

Design codes for the design of FRP bridge decks shall be established to promote the use of such innovative materials. For the purpose of preparing code provisions, reliability analyses were conducted to evaluate proper levels of safety and serviceability. Based on the results, several guidelines on design codes are suggested

Novel and simple transformation algorithm for combining microarray data sets

<p>Abstract</p> <p>Background</p> <p>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.</p> <p>Results</p> <p>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.</p> <p>Conclusion</p> <p>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.</p

Valuation of Credit Derivatives with Multiple Time Scales in the Intensity Model

We propose approximate solutions for pricing zero-coupon defaultable bonds, credit default swap rates, and bond options based on the averaging principle of stochastic differential equations. We consider the intensity-based defaultable bond, where the volatility of the default intensity is driven by multiple time scales. Small corrections are computed using regular and singular perturbations to the intensity of default. The effectiveness of these corrections is tested on the bond price and yield curve by investigating the behavior of the time scales with respect to the relevant parameters

Pathological hypersexuality induced by dopamine replacement therapy in a patient with progressive supranuclear palsy

To the Editor: The pathogenesis of pathological hypersexuality is still in controversy. To our knowledge, this is the first report of pathological hypersexuality induced by two different dopamine receptor agonists in a single patient with progressive supranuclear palsy. In this case, dopamine D2 receptor agonism, perhaps specifically D3 receptor subclass agonism, might have played a key role in the development of pathological hypersexuality induced by dopamine replacement therapy

A Case of Fabry's Disease with Congenital Agammaglobulinemia

Fabry's disease is an X-linked lysosomal storage disorder caused by abnormalities in the α-galactosidase A (GLA) gene, which leads to a GLA deficiency and to the intracellular deposition of globotriaosylceramide (Gb3) within vascular endothelium and other tissues. It manifests as progressive multiple organ dysfunctions caused by the deposition of Gb3. On the other hand, congenital agammaglobulinemia is usually caused by mutations in Bruton's tyrosine kinase (Btk) gene with X-linked dominence, suppresses B cell maturation, and causes recurrent pyogenic infections. In former reports, the distance between the loci in the Xq22 region of the human X chromosome was found to be about 69 kilobases. A 23-yr-old man diagnosed with congenital agammaglobulinemia at age 5, showed typical clinical and laboratory and histopathological findings of Fabry's disease. The genetic basis of this combination of the two syndromes was studied in this patient. Here, we report a case of Fabry's disease with congenital agammaglobulinemia