Correlation-based Data Representation

The Dagstuhl Seminar \u27Similarity-based Clustering and its Application to Medicine and Biology\u27 (07131) held in March 25--30, 2007, provided an excellent atmosphere for in-depth discussions about the research frontier of computational methods for relevant applications of biomedical clustering and beyond. We address some highlighted issues about correlation-based data analysis in this seminar postribution. First, some prominent correlation measures are briefly revisited. Then, a focus is put on Pearson correlation, because of its widespread use in biomedical sciences and because of its analytic accessibility. A connection to Euclidean distance of z-score transformed data outlined. Cost function optimization of correlation-based data representation is discussed for which, finally, applications to visualization and clustering of gene expression data are given

Fractal analysis of resting state functional connectivity of the brain

A variety of resting state neuroimaging data tend to exhibit fractal behavior where its power spectrum follows power-law scaling. Resting state functional connectivity is significantly influenced by fractal behavior which may not directly originate from neuronal population activities of the brain. To describe the fractal behavior, we adopted the fractionally integrated process (FIP) model instead of the fractional Gaussian noise (FGN) since the FIP model covers more general aspects of fractality than the FGN model. We also introduce a novel concept called the nonfractal connectivity which is defined as the correlation of short memory independent of fractal behavior, and compared it with the fractal connectivity which is an asymptotic wavelet correlation. We propose several wavelet-based estimators of fractal connectivity and nonfractal connectivity for a multivariate fractionally integrated noise (mFIN). The performance of these estimators was evaluated through simulation studies and the analyses of resting state functional MRI data of the rat brain.Comment: The 2012 International Joint Conference on Neural Network

A high-throughput screening system for barley/powdery mildew interactions based on automated analysis of light micrographs

<p>Abstract</p> <p>Background</p> <p>To find candidate genes that potentially influence the susceptibility or resistance of crop plants to powdery mildew fungi, an assay system based on transient-induced gene silencing (TIGS) as well as transient over-expression in single epidermal cells of barley has been developed. However, this system relies on quantitative microscopic analysis of the barley/powdery mildew interaction and will only become a high-throughput tool of phenomics upon automation of the most time-consuming steps.</p> <p>Results</p> <p>We have developed a high-throughput screening system based on a motorized microscope which evaluates the specimens fully automatically. A large-scale double-blind verification of the system showed an excellent agreement of manual and automated analysis and proved the system to work dependably. Furthermore, in a series of bombardment experiments an RNAi construct targeting the <it>Mlo </it>gene was included, which is expected to phenocopy resistance mediated by recessive loss-of-function alleles such as <it>mlo5</it>. In most cases, the automated analysis system recorded a shift towards resistance upon RNAi of <it>Mlo</it>, thus providing proof of concept for its usefulness in detecting gene-target effects.</p> <p>Conclusion</p> <p>Besides saving labor and enabling a screening of thousands of candidate genes, this system offers continuous operation of expensive laboratory equipment and provides a less subjective analysis as well as a complete and enduring documentation of the experimental raw data in terms of digital images. In general, it proves the concept of enabling available microscope hardware to handle challenging screening tasks fully automatically.</p

Microscope color image segmentation for resistance analysis of barley cells against powdery mildew

This paper addresses applied image segmentation techniques for the detection of dyed objects (transgenic barley cells) in microscope color images. It is shown on exemplary image data that edge detection with Canny’s algorithm applied to the hue channel of the HSV color space outperforms both multidimensional edge detection in RGB space as well as color-based pixel classification. This provides a firm basis for a fully automatic high-throughput analysis tool