Biasogram: visualization of confounding technical bias in gene expression data.

Gene expression profiles of clinical cohorts can be used to identify genes that are correlated with a clinical variable of interest such as patient outcome or response to a particular drug. However, expression measurements are susceptible to technical bias caused by variation in extraneous factors such as RNA quality and array hybridization conditions. If such technical bias is correlated with the clinical variable of interest, the likelihood of identifying false positive genes is increased. Here we describe a method to visualize an expression matrix as a projection of all genes onto a plane defined by a clinical variable and a technical nuisance variable. The resulting plot indicates the extent to which each gene is correlated with the clinical variable or the technical variable. We demonstrate this method by applying it to three clinical trial microarray data sets, one of which identified genes that may have been driven by a confounding technical variable. This approach can be used as a quality control step to identify data sets that are likely to yield false positive results

Reliable assessment of telomere maintenance mechanisms in neuroblastoma

BACKGROUND: Telomere maintenance mechanisms (TMM) are a hallmark of high-risk neuroblastoma, and are conferred by activation of telomerase or alternative lengthening of telomeres (ALT). However, detection of TMM is not yet part of the clinical routine, and consensus on TMM detection, especially on ALT assessment, remains to be achieved. METHODS: Whole genome sequencing (WGS) data of 68 primary neuroblastoma samples were analyzed. Telomere length was calculated from WGS data or by telomere restriction fragment analysis (n = 39). ALT was assessed by C-circle assay (CCA, n = 67) and detection of ALT-associated PML nuclear bodies (APB) by combined fluorescence in situ hybridization and immunofluorescence staining (n = 68). RNA sequencing was performed (n = 64) to determine expression of TERT and telomeric long non-coding RNA (TERRA). Telomerase activity was examined by telomerase repeat amplification protocol (TRAP, n = 15). RESULTS: Tumors were considered as telomerase-positive if they harbored a TERT rearrangement, MYCN amplification or high TERT expression (45.6%, 31/68), and ALT-positive if they were positive for APB and CCA (19.1%, 13/68). If all these markers were absent, tumors were considered TMM-negative (25.0%, 17/68). According to these criteria, the majority of samples were classified unambiguously (89.7%, 61/68). Assessment of additional ALT-associated parameters clarified the TMM status of the remaining seven cases with high likelihood: ALT-positive tumors had higher TERRA expression, longer telomeres, more telomere insertions, a characteristic pattern of telomere variant repeats, and were associated with ATRX mutations. CONCLUSIONS: We here propose a workflow to reliably detect TMM in neuroblastoma. We show that unambiguous classification is feasible following a stepwise approach that determines both, activation of telomerase and ALT. The workflow proposed in this study can be used in clinical routine and provides a framework to systematically and reliably determine telomere maintenance mechanisms for risk stratification and treatment allocation of neuroblastoma patients

Comparative study of unsupervised dimension reduction techniques for the visualization of microarray gene expression data

<p>Abstract</p> <p>Background</p> <p>Visualization of DNA microarray data in two or three dimensional spaces is an important exploratory analysis step in order to detect quality issues or to generate new hypotheses. Principal Component Analysis (PCA) is a widely used linear method to define the mapping between the high-dimensional data and its low-dimensional representation. During the last decade, many new nonlinear methods for dimension reduction have been proposed, but it is still unclear how well these methods capture the underlying structure of microarray gene expression data. In this study, we assessed the performance of the PCA approach and of six nonlinear dimension reduction methods, namely Kernel PCA, Locally Linear Embedding, Isomap, Diffusion Maps, Laplacian Eigenmaps and Maximum Variance Unfolding, in terms of visualization of microarray data.</p> <p>Results</p> <p>A systematic benchmark, consisting of Support Vector Machine classification, cluster validation and noise evaluations was applied to ten microarray and several simulated datasets. Significant differences between PCA and most of the nonlinear methods were observed in two and three dimensional target spaces. With an increasing number of dimensions and an increasing number of differentially expressed genes, all methods showed similar performance. PCA and Diffusion Maps responded less sensitive to noise than the other nonlinear methods.</p> <p>Conclusions</p> <p>Locally Linear Embedding and Isomap showed a superior performance on all datasets. In very low-dimensional representations and with few differentially expressed genes, these two methods preserve more of the underlying structure of the data than PCA, and thus are favorable alternatives for the visualization of microarray data.</p

A shale rock physics model for analysis of brittleness index, mineralogy and porosity in the Barnett Shale

We construct a rock physics workflow to link the elastic properties of shales to complex constituents and specific microstructure attributes. The key feature in our rock physics model is the degrees of preferred orientation of clay and kerogen particles defined by the proportions of such particles in their total content. The self-consistent approximation method and Backus averaging method are used to consider the isotropic distribution and preferred orientation of compositions and pores in shales. Using the core and well log data from the Barnett Shale, we demonstrate the application of the constructed templates for the evaluation of porosity, lithology and brittleness index. Then, we investigate the brittleness index defined in terms of mineralogy and geomechanical properties. The results show that as clay content increases, Poisson's ratio tends to increase and Young's modulus tends to decrease. Moreover, we find that Poisson's ratio is more sensitive to the variation in the texture of shales resulting from the preferred orientation of clay particles. Finally, based on the constructed rock physics model, we calculate AVO responses from the top and bottom of the Barnett Shale, and the results indicate predictable trends for the variations in porosity, lithology and brittleness index in shales