Michael G. Schimek

Estimation in partially linear models

Abstract Order n algorithms are developed for computing the estimated mean vector, regression coefficients, standard errors and smoothing parameter selection criteria for Speckman smoothing spline estimators in partially linear models. A difference type variance estimator is proposed and shown to be 3 - consistent .

Estimation and inference in partially linear models with smoothing splines

A new estimation concept for partially linear models based on smoothing splines has been recently introduced in Eubank et al. (Comput. Statist. Data Anal. 29 (1998) 27). It is based on Speckmans (J. Roy. Statist. Soc. Ser. B 50 (1988) 413) approach. Here, we describe cheap direct algorithms for this approach as well as for the well-known approach of Green et al. (J. Roy. Statist. Soc. Ser. B 47 (1985) 294). The smoothing parameter can be selected via an unbiased risk criterion. This requires the estimation of the model variance and the evaluation of the hat matrix. Standard errors of the parametric coefficients can be obtained from the estimated covariance matrix. Inference is discussed for both the parametric and the nonparametric part of the model. The time demand of all algorithms is only linear. They can be executed from the statistical and graphical environment S-Plus. Under correlation between the parametric and the nonparametric part of the model, which is quite common in practice, the approach due to Green et al. (1985) is known to be asymptotically biased. Apart from the bias, correlation can cause estimation problems. Hence we study and compare the small sample performance of both approaches. For this purpose a simulation study is performed. We find that both approaches work well for reasonable signal-to-noise ratios and sample sizes, even under correlation. But for inferential purposes we suggest to use the Speckman estimates.

Vascular Endothelial Expression of Indoleamine 2,3-Dioxygenase 1 Forms a Positive Gradient towards the Feto-Maternal Interface

We describe the distribution of indoleamine 2,3-dioxygenase 1 (IDO1) in vascular endothelium of human first-trimester and term placenta. Expression of IDO1 protein on the fetal side of the interface extended from almost exclusively sub-trophoblastic capillaries in first-trimester placenta to a nearly general presence on villous vascular endothelia at term, including also most bigger vessels such as villous arteries and veins of stem villi and vessels of the chorionic plate. Umbilical cord vessels were generally negative for IDO1 protein. In the fetal part of the placenta positivity for IDO1 was restricted to vascular endothelium, which did not co-express HLA-DR. This finding paralleled detectability of IDO1 mRNA in first trimester and term tissue and a high increase in the kynurenine to tryptophan ratio in chorionic villous tissue from first trimester to term placenta. Endothelial cells isolated from the chorionic plate of term placenta expressed IDO1 mRNA in contrast to endothelial cells originating from human umbilical vein, iliac vein or aorta. In first trimester decidua we found endothelium of arteries rather than veins expressing IDO1, which was complementory to expression of HLA-DR. An estimation of IDO activity on the basis of the ratio of kynurenine and tryptophan in blood taken from vessels of the chorionic plate of term placenta indicated far higher values than those found in the peripheral blood of adults. Thus, a gradient of vascular endothelial IDO1 expression is present at both sides of the feto-maternal interface.

Moderate-Deviation-Based Inference for Random Degeneration in Paired Rank Lists

Consider a problem where N items (objects or individuals) are judged by assessors using their perceptions of a set of performance criteria, or alternatively by technical devices. In particular, two assessors might rank the items between 1 and N on the basis of relative performance, independently of each other. We can aggregate the rank lists by assigning one if the two assessors agree, and zero otherwise, and we can modify this approach to make it robust against irregularities. In this article, we consider methods and algorithms that can be used to address this problem. We study their theoretical properties in the case of a model based on nonstationary Bernoulli trials, and we report on their numerical properties for both simulated and real data.

An Untargeted Metabolomics Approach to Characterize Short-Term and Long-Term Metabolic Changes after Bariatric Surgery.

Bariatric surgery is currently one of the most effective treatments for obesity and leads to significant weight reduction, improved cardiovascular risk factors and overall survival in treated patients. To date, most studies focused on short-term effects of bariatric surgery on the metabolic profile and found high variation in the individual responses to surgery. The aim of this study was to identify relevant metabolic changes not only shortly after bariatric surgery (Roux-en-Y gastric bypass) but also up to one year after the intervention by using untargeted metabolomics. 132 serum samples taken from 44 patients before surgery, after hospital discharge (1–3 weeks after surgery) and at a 1-year follow-up during a prospective study (NCT01271062) performed at two study centers (Austria and Switzerland). The samples included 24 patients with type 2 diabetes at baseline, thereof 9 with diabetes remission after one year. The samples were analyzed by using liquid chromatography coupled to high resolution mass spectrometry (LC-HRMS, HILIC-QExactive). Raw data was processed with XCMS and drift-corrected through quantile regression based on quality controls. 177 relevant metabolic features were selected through Random Forests and univariate testing and 36 metabolites were identified. Identified metabolites included trimethylamine-N-oxide, alanine, phenylalanine and indoxyl-sulfate which are known markers for cardiovascular risk. In addition we found a significant decrease in alanine after one year in the group of patients with diabetes remission relative to non-remission. Our analysis highlights the importance of assessing multiple points in time in subjects undergoing bariatric surgery to enable the identification of biomarkers for treatment response, cardiovascular benefit and diabetes remission. Key-findings include different trend pattern over time for various metabolites and demonstrated that short term changes should not necessarily be used to identify important long term effects of bariatric surgery.

TopKLists: a comprehensive R package for statistical inference, stochastic aggregation, and visualization of multiple omics ranked lists

Abstract High-throughput sequencing techniques are increasingly affordable and produce massive amounts of data. Together with other high-throughput technologies, such as microarrays, there are an enormous amount of resources in databases. The collection of these valuable data has been routine for more than a decade. Despite different technologies, many experiments share the same goal. For instance, the aims of RNA-seq studies often coincide with those of differential gene expression experiments based on microarrays. As such, it would be logical to utilize all available data. However, there is a lack of biostatistical tools for the integration of results obtained from different technologies. Although diverse technological platforms produce different raw data, one commonality for experiments with the same goal is that all the outcomes can be transformed into a platform-independent data format – rankings – for the same set of items. Here we present the R package TopKLists, which allows for statistical inference on the lengths of informative (top-k) partial lists, for stochastic aggregation of full or partial lists, and for graphical exploration of the input and consolidated output. A graphical user interface has also been implemented for providing access to the underlying algorithms. To illustrate the applicability and usefulness of the package, we integrated microRNA data of non-small cell lung cancer across different measurement techniques and draw conclusions. The package can be obtained from CRAN under a LGPL-3 license.

MSMAD: a computationally efficient method for the analysis of noisy array CGH data

MOTIVATION Genome analysis has become one of the most important tools for understanding the complex process of cancerogenesis. With increasing resolution of CGH arrays, the demand for computationally efficient algorithms arises, which are effective in the detection of aberrations even in very noisy data. RESULTS We developed a rather simple, non-parametric technique of high computational efficiency for CGH array analysis that adopts a median absolute deviation concept for breakpoint detection, comprising median smoothing for pre-processing. The resulting algorithm has the potential to outperform any single smoothing approach as well as several recently proposed segmentation techniques. We show its performance through the application of simulated and real datasets in comparison to three other methods for array CGH analysis. IMPLEMENTATION Our approach is implemented in the R-language and environment for statistical computing (version 2.6.1 for Windows, R-project, 2007). The code is available at: http://www.iba.muni.cz/~budinska/msmad.html. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

An Inference and Integration Approach for the Consolidation of Ranked Lists

In this article, we describe a new approach that combines the estimation of the lengths of highly conforming sublists with their stochastic aggregation, to deal with two or more rankings of the same set of objects. The goal is to obtain a much smaller set of informative common objects in a new rank order. The input lists can be of large or huge size, their rankings irregular and incomplete due to random and missing assignments. A moderate deviation-based inference procedure and a cross-entropy Monte Carlo technique are used to handle the combinatorial complexity of the task. Two alternative distance measures are considered that can accommodate truncated list information. Finally, the outlined approach is applied to simulated data that was motivated by microarray meta-analysis, an important field of application.

A new method for morphometric analysis of tissue distribution of mobile cells in relation to immobile tissue structures.

The distribution of cells in stained tissue sections provides information that may be analyzed by means of morphometric computation. We developed an algorithm for automated analysis for the purpose of answering questions pertaining to the relative densities of wandering cells in the vicinity of comparatively immobile tissue structures such as vessels or tumors. As an example, we present the analysis of distribution of CD56-positive cells and of CXCR3-positive cells (relative densities of peri-vascular versus non-vascular cell populations) in relation to the endothelium of capillaries and venules of human parietal decidua tissue of first trimester pregnancy. In addition, the distibution of CD56-positive cells (mostly uterine NK cells) in relation to spiral arteries is analyzed. The image analysis is based on microphotographs of two-color immunohistological stainings. Discrete distances (10–50 µm) from the fixed structures were chosen for the purpose of definining the extent of neighborhood areas. For the sake of better comparison of cell distributions at different overall cell densities a model of random distribution of “cells” in relation to neighborhood areas and rest decidua of a specific sample was built. In the chosen instances, we found increased perivascular density of CD56-positive cells and of CXCR3-positive cells. In contrast, no accumulation of CD56-positive cells was found in the neighborhood of spiral arteries.

Keratin 18-deficiency results in steatohepatitis and liver tumors in old mice: A model of steatohepatitis-associated liver carcinogenesis

Backround Steatohepatitis (SH)-associated liver carcinogenesis is an increasingly important issue in clinical medicine. SH is morphologically characterized by steatosis, hepatocyte injury, ballooning, hepatocytic cytoplasmic inclusions termed Mallory-Denk bodies (MDBs), inflammation and fibrosis. Results 17-20-months-old Krt18−/− and Krt18+/− mice in contrast to wt mice spontaneously developed liver lesions closely resembling the morphological spectrum of human SH as well as liver tumors. The pathologic alterations were more pronounced in Krt18−/− than in Krt18+/− mice. The frequency of liver tumors with male predominance was significantly higher in Krt18−/− compared to age-matched Krt18+/− and wt mice. Krt18-deficient tumors in contrast to wt animals displayed SH features and often pleomorphic morphology. aCGH analysis of tumors revealed chromosomal aberrations in Krt18−/− liver tumors, affecting loci of oncogenes and tumor suppressor genes. Materials and Methods Livers of 3-, 6-, 12- and 17-20-months-old aged wild type (wt), Krt18+/− and Krt18−/− (129P2/OlaHsd background) mice were analyzed by light and immunofluorescence microscopy as well as immunohistochemistry. Liver tumors arising in aged mice were analyzed by array comparative genomic hybridization (aCGH). Conclusions Our findings show that K18 deficiency of hepatocytes leads to steatosis, increasing with age, and finally to SH. K18 deficiency and age promote liver tumor development in mice, frequently on the basis of chromosomal instability, resembling human HCC with stemness features.