Tests for differentiation in gene expression using a data-driven order or weights for hypotheses

In the analysis of gene expression by microarrays there are usually few subjects, but high-dimensional data. By means of techniques, such as the theory of spherical tests or with suitable permutation tests, it is possible to sort the endpoints or to give weights to them according to specific criteria determined by the data while controlling the multiple type I error rate. The procedures developed so far are based on a sequential analysis of weighted p-values (corresponding to the endpoints), including the most extreme situation of weighting leading to a complete order of p-values. When the data for the endpoints have approximately equal variances, these procedures show good power properties. In this paper, we consider an alternative procedure, which is based on completely sorting the endpoints, but smoothed in the sense that some perturbations in the sequence of the p-values are allowed. The procedure is relatively easy to perform, but has high power under the same restrictions as for the weight-based procedures.     

k‐FWER Control without p ‐value Adjustment,with Application to Detection of Genetic Determinants of Multiple Sclerosis in Italian Twins

Summary We show a novel approach for k‐FWER control which does not involve any correction, but only testing the hypotheses along a (possibly data‐driven) order until a suitable number of p‐values are found above the uncorrected α level. p‐values can arise from any linear model in a parametric or nonparametric setting. The approach is not only very simple and computationally undemanding, but also the data‐driven order enhances power when the sample size is small (and also when k and/or the number of tests is large). We illustrate the method on an original study about gene discovery in multiple sclerosis, in which were involved a small number of couples of twins, discordant by disease. The methods are implemented in an R package (someKfwer ), freely available on CRAN.     

Multiple Comparisons of Treatments with Stable Multivariate Tests in a Two‐Stage Adaptive Design,Including a Test for Non‐Inferiority

The application of stabilized multivariate tests is demonstrated in the analysis of a two‐stage adaptive clinical trial with three treatment arms. Due to the clinical problem, the multiple comparisons include tests of superiority as well as a test for non‐inferiority, where non‐inferiority is (because of missing absolute tolerance limits) expressed as linear contrast of the three treatments. Special emphasis is paid to the combination of the three sources of multiplicity – multiple endpoints, multiple treatments, and two stages of the adaptive design. Particularly, the adaptation after the first stage comprises a change of the a‐priori order of hypotheses.     

Robust Tests for Candidate‐Gene Association Using Case‐Parents Trios with a Multi‐Allelic Marker

In case‐parents trios design, the association between a multi‐allelic candidate‐gene and a disease can be detected by using maximum of score tests (max‐score) when the mode of inheritance is known. We apply the maximum of the max‐score statistics and the maximum of likelihood ratio statistics when the genetic model is unknown and examine their robust properties compared to max‐score statistics. The simulation results demonstrate that the two maximum robust tests are more efficacious and robust across all genetic models compared with the three max‐score tests. Moreover, in most situations, the maximum of the max‐score tests seems to be more powerful than the maximum of the likelihood ratio tests. (© 2004 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)     

High‐dimensional data analysis: Selection of variables,data compression and graphics – Application to gene expression

The paper presents effective and mathematically exact procedures for selection of variables which are applicable in cases with a very high dimension as, for example, in gene expression analysis. Choosing sets of variables is an important method to increase the power of the statistical conclusions and to facilitate the biological interpretation. For the construction of sets, each single variable is considered as the centre of potential sets of variables. Testing for significance is carried out by means of the Westfall‐Young principle based on resampling or by the parametric method of spherical tests. The particular requirements for statistical stability are taken into account; each kind of overfitting is avoided. Thus, high power is attained and the familywise type I error can be kept in spite of the large dimension. To obtain graphical representations by heat maps and curves, a specific data compression technique is applied. Gene expression data from B‐cell lymphoma patients serve for the demonstration of the procedures.     

Application of Multiple Test Procedures to the Combination of Multivariate and Univariate Tests with Varying Variable Sets

The positive ascertainment of location differences in a multivariate comparison of two or more groups gives rise to the question for the contribution of the single variables or of subsets of variables to the multivariate difference. In this paper two methods are proposed to accomplish the original multivariate test by tests in variable subsets or in single variables using a closed test procedure and Holm's procedure, respectively. Both control the multiple level of the whole procedure.     

Tests for Monotonic Trend from Case‐Control Data: Cochran‐Armitage‐Mantel Trend Test,Isotonic Regression and Single and Multiple Contrast Tests

Tests for a monotonic trend between an ordered categorical exposure and disease status are routinely carried out from case‐control data using the Mantel‐extension trend test or the asymptotically equivalent Cochran‐Armitage test. In this study, we considered two alternative tests based on isotonic regression, namely an order‐restricted likelihood ratio test and an isotonic modification of the Mantel‐extension test extending the recent proposal by Mancuso, Ahn and Chen (2001) to case‐control data. Furthermore, we considered three tests based on contrasts, namely a single contrast (SC) test based on Schaafsma's coefficients, the Dosemeci and Benichou (DB) test, a multiple contrast (MC) test based on the Helmert, reverse‐Helmert and linear contrasts and we derived their case‐control versions. Using simulations, we compared the statistical properties of these five alternative tests to those of the Mantel‐extension test under various patterns including no relationship, as well as monotonic and non‐monotonic relationships between exposure and disease status. In the case of no relationship, all tests had close to nominal type I error except in situations combining a very unbalanced exposure distribution and small sample size, where the asymptotic versions of the three tests based on contrasts were highly anticonservative. The use of bootstrap instead of asymptotic versions corrected this anticonservatism. For monotonic patterns, all tests had close powers. For non monotonic patterns, the DB‐test showed the most favourable results as it was the least powerful test. The two tests based on isotonic regression were the most powerful tests and the Mantel‐extension test, the SC‐ and MC‐tests had in‐between powers. The six tests were applied to data from a case‐control study investigating the relationship between alcohol consumption and risk of laryngeal cancer in Turkey. In situations with no evidence of a monotonic relationship between exposure and disease status, the three tests based on contrasts did not conclude in favour of a significant trend whereas all the other tests did. (© 2004 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)     

Application of Adaptive Cluster Sampling with a Data‐Driven Stopping Rule to Plant Disease Incidence

Plant pathologists need to manage plant diseases at low incidence levels. This needs to be performed efficiently in terms of precision, cost and time because most plant infections spread rapidly to other plants. Adaptive cluster sampling with a data‐driven stopping rule (ACS*) was proposed to control the final sample size and improve efficiency of the ordinary adaptive cluster sampling (ACS) when prior knowledge of population structure is not known. This study seeks to apply the ACS* design to plant diseases at various levels of clustering and incidences levels. Results from simulation study show that the ACS* is as efficient as the ordinary ACS design at low levels of disease incidence with highly clustered diseased plants and is an efficient design compared with simple random sampling (SRS) and ordinary ACS for some highly to less clustered diseased plants with moderate to higher levels of disease incidence.     

Rejoinder to Use of Principal Component Analysis and the GE -Biplot for the Graphical Exploration of Gene Expression Data

Summary This note is in response to Wouters et al. (2003, Biometrics 59, 1131–1139) who compared three methods for exploring gene expression data. Contrary to their summary that principal component analysis is not very informative, we show that it is possible to determine principal component analyses that are useful for exploratory analysis of microarray data. We also present another biplot representation, the GE‐biplot (Gene Expression biplot), that is a useful method for exploring gene expression data with the major advantage of being able to aid interpretation of both the samples and the genes relative to each other.     

Gene Cloning of Dihydroxyacetone Reductase from a Methylotrophic Yeast,Hansenula ofunaensis,and its Expression in Escherichia coli HB101 for Production of Optically Active 2‐Pentanol

Optically active alcohols are important building blocks as versatile chiral synthons for asymmetric syntheses of pharmaceuticals and agrochemicals. The aim of this paper is to efficiently prepare chiral 2‐pentanol by means of microorganisms. The gene of dihydroxyacetone reductase (EC 1.1.1.6) from a methylotrophic yeast, Hansenula ofunaensis, was cloned and chiral 2‐pentanol was produced by the recombinant Escherichia coli harboring the gene. The gene encoding the enzyme was cloned from an H. ofunaensis genomic library. In the deduced amino acid sequence of 364 residues, the NAD(H) binding motif and the cysteine residues that correspond to the cysteine ligands in the zinc atom were conserved, as they are in alcohol dehydrogenases from other origins. Dihydroxyacetone reductase was similar to alcohol dehydrogenases of prokaryotes. For the production of chiral compounds, an E. coli HB101 strain was transformed. The H. ofunaensis gene product, dihydroxyacetone reductase, catalyzed the NAD⁺‐dependent oxidation of 2‐pentanol to 2‐pentanone as well as the corresponding reverse reactions, showing specificity towards the secondary alcohol in (R)‐configuration. From 100 mM 2‐pentanone, (R)‐2‐pentanol (98 mM, > 99.9 % enantiometric excess, e.e.) was obtained in a 30‐min reaction with resting cells of the E. coli HB101 strain harboring the expression plasmid, pSG‐HOD1, which possesses the genes of both dihydroxyacetone reductase and glucose dehydrogenase as an NADH reproducing system. The stereospecificity changed during the reduction, depending on the pH. E. coli HB101 was also transformed by the expression plasmid, pSE‐HOD4, in which the gene of glucose dehydrogenase was removed from pSG‐HOD1, and designated as E. coli HB101 (pSE‐HOD4). E. coli HB101 (pSE‐HOD4) oxidized only (R)‐2‐pentanol in 100 mM of the racemate (R:S = 52:48), and the reaction medium was enriched with (S)‐2‐pentanol (48 mM, 98 % e.e.) after 30 min of incubation. The reaction was sufficiently promoted without the other additives. E. coli transformants expressing the gene of this enzyme could be particularly advantageous to the production of optically active 2‐pentanol.     

SecProMTB: Support Vector Machine‐Based Classifier for Secretory Proteins Using Imbalanced Data Sets Applied to Mycobacterium tuberculosis

Secretory proteins of Mycobacterium tuberculosis have created more concern, given their dominant immunogenicity and role in pathogenesis. In view of expensive and time‐consuming traditional biochemical experiments, an advanced support vector machine model named SecProMTB is constructed in this study and the proteins are identified by a bioinformatic approach. First, an improved pseudo‐amino acid composition (PseAAC) algorithm is used to extract features from all entities. Second, a novel imbalanced‐data strategy is proposed and adopted to divide the original data set into train set and test set. Third, to overcome the overfitting problem, feature‐ranking algorithms are applied with an increment feature selection. Finally, the model is trained and optimized. Consequently, a model is obtained with an area under the curve of 0.862 and average accuracy of 86% in the independent test. For the convenience of users, SecProMTB and related data are openly accessible at http://server.malab.cn/SecProMTB/index.jsp .     

Model for Heterogeneous Random Networks Using Continuous Latent Variables and an Application to a Tree–Fungus Network

Summary The mixture model is a method of choice for modeling heterogeneous random graphs, because it contains most of the known structures of heterogeneity: hubs, hierarchical structures, or community structure. One of the weaknesses of mixture models on random graphs is that, at the present time, there is no computationally feasible estimation method that is completely satisfying from a theoretical point of view. Moreover, mixture models assume that each vertex pertains to one group, so there is no place for vertices being at intermediate positions. The model proposed in this article is a grade of membership model for heterogeneous random graphs, which assumes that each vertex is a mixture of extremal hypothetical vertices. The connectivity properties of each vertex are deduced from those of the extreme vertices. In this new model, the vector of weights of each vertex are fixed continuous parameters. A model with a vector of parameters for each vertex is tractable because the number of observations is proportional to the square of the number of vertices of the network. The estimation of the parameters is given by the maximum likelihood procedure. The model is used to elucidate some of the processes shaping the heterogeneous structure of a well‐resolved network of host/parasite interactions.     

Pre‐operative features of non‐invasive follicular thyroid neoplasms with papillary‐like nuclear features: An analysis of their cytological,Gene Expression Classifier and sonographic findings

Objective

To investigate the corresponding cytological diagnoses, Gene Expression Classifier (GEC) results and ultrasound features of thyroid nodules diagnosed as non‐invasive follicular thyroid neoplasms with papillary‐like nuclear features (NIFTP), as well as any coexisting pathology.

Methods

We performed a retrospective review of thyroid nodules histologically diagnosed as NIFTP at our institution between 1^st April 2016 and 1^st April 2017. The following data points were collected: demographics, nodule size, ultrasound features, cytological diagnosis, GEC results, origin of sample (in‐house vs outside hospital) and any additional pathology identified in the resection specimen.

Results

The case cohort included 87 nodules diagnosed as NIFTP (size range: 1‐7 cm, mean: 2.5 cm) from 82 patients (age range: 22‐82, mean age: 50.4, M:F—1:4.1). Corresponding FNA results were available for 72 nodules (82.8%) and were categorised as follows: benign (n = 5, 6.9%), atypia of unknown significance/follicular lesion of undetermined significance (n = 29, 40.3%), follicular neoplasm/suspicious for follicular neoplasm/follicular neoplasm with oncocytic features (n = 27, 37.5%), suspicious for papillary thyroid carcinoma (n = 6, 8.3%) and malignant (n = 5, 6.9%). GEC results were available for 32 (44.4%) nodules, with the majority of cases classified as suspicious (81.3%). On ultrasound, most of the nodules were predominantly solid (81.8%), vascular (93.8%), non‐calcified (86.5%), and either hypoechoic (44.9%) or isoechoic (38.8%). In addition to NIFTP and other benign findings in the background thyroid, 75 separate malignant tumours were identified in 38 (46.3%) patients, many of which were papillary thyroid microcarcinomas (86.5%) with lymph node metastases present in two cases.

Conclusions

The majority of thyroid nodules histologically diagnosed as NIFTP have indeterminate cytology (77.8%) and are classified as suspicious (81.3%) by GEC testing. Taken together, these findings can guide clinicians toward a more conservative therapeutic approach.

     

Enzyme Kinetics of an Alternative Splicing Isoform of Mitochondrial 8‐Oxoguanine DNA Glycosylase,OGG1‐1b,and Compared with the Nuclear OGG1‐1a

Eight alternatively spliced isoforms of human 8‐oxoguanine DNA glycosylase (OGG1) (OGG1‐1a to ‐1c and ‐2a to ‐2e) are registered in the National Center for Biotechnology Information. OGG1(s) in mitochondria have not yet been fully characterized biochemically. In this study, we purified mitochondrial recombinant OGG1‐1b protein and compared its activity with nuclear OGG1‐1a protein. The reaction rate constant (k_g) of the 7,8‐dihydro‐8‐oxoguanine (8‐oxoG) glycosylase activity of OGG1‐1b was 8‐oxoG:C >> 8‐oxoG:T >> 8‐oxoG:G > 8‐oxoG:A (7.96, 0.805, 0.070, and 0.015 min^?1, respectively) and that of the N‐glycosylase/DNA lyase activity (k_gl) of OGG1‐1b was 8‐oxoG:C > 8‐oxoG:T ?8‐oxoG:G >> 8‐oxoG:A (0.286, 0.079, 0.040, and negligible min^?1, respectively). These reaction rate constants were similar to those of OGG1‐1a except for k_gl against 8‐oxoG:A. APEX nuclease 1 was required to promote DNA strand breakage by OGG1‐1b. These results suggest that OGG1‐1b is associated with 8‐oxoG cleavage in human mitochondria and that the mechanism of this repair is similar to that of nuclear OGG1‐1a.