Learning causal networks from systems biology time course data: an effective model selection procedure for the vector autoregressive process

Background: Causal networks based on the vector autoregressive (VAR) process are a promising statistical tool for modeling regulatory interactions in a cell. However, learning these networks is challenging due to the low sample size and high dimensionality of genomic data. Results: We present a novel and highly efficient approach to estimate a VAR network. This proceeds in two steps: (i) improved estimation of VAR regression coefficients using an analytic shrinkage approach, and (ii) subsequent model selection by testing the associated partial correlations. In simulations this approach outperformed for small sample size all other considered approaches in terms of true discovery rate (number of correctly identified edges relative to the significant edges). Moreover, the analysis of expression time series data from Arabidopsis thaliana resulted in a biologically sensible network. Conclusion: Statistical learning of large-scale VAR causal models can be done efficiently by the proposed procedure, even in the difficult data situations prevalent in genomics and proteomics. Availability: The method is implemented in R code that is available from the authors on request

iSAM2 : incremental smoothing and mapping using the Bayes tree

Author Posting. © The Author(s), 2011. This is the author's version of the work. It is posted here by permission of Sage for personal use, not for redistribution. The definitive version was published in International Journal of Robotics Research 31 (2012): 216-235, doi:10.1177/0278364911430419.We present a novel data structure, the Bayes tree, that provides an algorithmic foundation enabling a better understanding of existing graphical model inference algorithms and their connection to sparse matrix factorization methods. Similar to a clique tree, a Bayes tree encodes a factored probability density, but unlike the clique tree it is directed and maps more naturally to the square root information matrix of the simultaneous localization and mapping (SLAM) problem. In this paper, we highlight three insights provided by our new data structure. First, the Bayes tree provides a better understanding of the matrix factorization in terms of probability densities. Second, we show how the fairly abstract updates to a matrix factorization translate to a simple editing of the Bayes tree and its conditional densities. Third, we apply the Bayes tree to obtain a completely novel algorithm for sparse nonlinear incremental optimization, named iSAM2, which achieves improvements in efficiency through incremental variable re-ordering and fluid relinearization, eliminating the need for periodic batch steps. We analyze various properties of iSAM2 in detail, and show on a range of real and simulated datasets that our algorithm compares favorably with other recent mapping algorithms in both quality and efficiency.M. Kaess, H. Johannsson and J. Leonard were partially supported by ONR grants N00014-06-1-0043 and N00014-10-1-0936. F. Dellaert and R. Roberts were partially supported by NSF, award number 0713162, “RI: Inference in Large-Scale Graphical Models”. V. Ila has been partially supported by the Spanish MICINN under the Programa Nacional de Movilidad de Recursos Humanos de Investigación

Direct and Inverse Computation of Jacobi Matrices of Infinite Homogeneous Affine I.F.S

We introduce a new set of algorithms to compute Jacobi matrices associated with measures generated by infinite systems of iterated functions. We demonstrate their relevance in the study of theoretical problems, such as the continuity of these measures and the logarithmic capacity of their support. Since our approach is based on a reversible transformation between pairs of Jacobi matrices, we also discuss its application to an inverse / approximation problem. Numerical experiments show that the proposed algorithms are stable and can reliably compute Jacobi matrices of large order.Comment: 20 pages 6 figure

Isometric Sliced Inverse Regression for Nonlinear Manifolds Learning

[[abstract]]Sliced inverse regression (SIR) was developed to find effective linear dimension-reduction directions for exploring the intrinsic structure of the high-dimensional data. In this study, we present isometric SIR for nonlinear dimension reduction, which is a hybrid of the SIR method using the geodesic distance approximation. First, the proposed method computes the isometric distance between data points; the resulting distance matrix is then sliced according to K-means clustering results, and the classical SIR algorithm is applied. We show that the isometric SIR (ISOSIR) can reveal the geometric structure of a nonlinear manifold dataset (e.g., the Swiss roll). We report and discuss this novel method in comparison to several existing dimension-reduction techniques for data visualization and classification problems. The results show that ISOSIR is a promising nonlinear feature extractor for classification applications.[[incitationindex]]SCI[[booktype]]紙本[[booktype]]電子

k-Nearest neighbor models for microarray gene expression analysis and clinical outcome prediction

In the clinical application of genomic data analysis and modeling, a number of factors contribute to the performance of disease classification and clinical outcome prediction. This study focuses on the k-nearest neighbor (KNN) modeling strategy and its clinical use. Although KNN is simple and clinically appealing, large performance variations were found among experienced data analysis teams in the MicroArray Quality Control Phase II (MAQC-II) project. For clinical end points and controls from breast cancer, neuroblastoma and multiple myeloma, we systematically generated 463 320 KNN models by varying feature ranking method, number of features, distance metric, number of neighbors, vote weighting and decision threshold. We identified factors that contribute to the MAQC-II project performance variation, and validated a KNN data analysis protocol using a newly generated clinical data set with 478 neuroblastoma patients. We interpreted the biological and practical significance of the derived KNN models, and compared their performance with existing clinical factors

Raman spectroscopy as a versatile tool for studying the properties of graphene.

Raman spectroscopy is an integral part of graphene research. It is used to determine the number and orientation of layers, the quality and types of edge, and the effects of perturbations, such as electric and magnetic fields, strain, doping, disorder and functional groups. This, in turn, provides insight into all sp(2)-bonded carbon allotropes, because graphene is their fundamental building block. Here we review the state of the art, future directions and open questions in Raman spectroscopy of graphene. We describe essential physical processes whose importance has only recently been recognized, such as the various types of resonance at play, and the role of quantum interference. We update all basic concepts and notations, and propose a terminology that is able to describe any result in literature. We finally highlight the potential of Raman spectroscopy for layered materials other than graphene

A biomaterials approach to influence stem cell fate in injectable cell-based therapies

Background Numerous stem cell therapies use injection-based administration to deliver high-density cell preparations. However, cell retention rates as low as 1% have been observed within days of transplantation. This study investigated the effects of varying administration and formulation parameters of injection-based administration on cell dose recovery and differentiation fate choice of human mesenchymal stem cells. Methods The impact of ejection rate via clinically relevant Hamilton micro-syringes and biomaterial-assisted delivery was investigated. Cell viability, the percentage of cell dose delivered as viable cells, proliferation capacity as well as differentiation behaviour in bipotential media were assessed. Characterisation of the biomaterial-based cell carriers was also carried out. Results A significant improvement of in-vitro dose recovery in cells co-ejected with natural biomaterials was observed, with ejections within 2% (w/v) gelatin resulting in 87.5 ± 14% of the cell dose being delivered as viable cells, compared to 32.2 ± 19% of the dose ejected in the commonly used saline vehicle at 10 μl/min. Improvement in cell recovery was not associated with the rheological properties of biomaterials utilised, as suggested by previous studies. The extent of osteogenic differentiation was shown to be substantially altered by choice of ejection rate and cell carrier, despite limited contact time with cells during ejection. Collagen type I and bone-derived extracellular matrix cell carriers yielded significant increases in mineralised matrix deposited at day 21 relative to PBS. Conclusions An enhanced understanding of how administration protocols and biomaterials influence cell recovery, differentiation capacity and choice of fate will facilitate the development of improved administration and formulation approaches to achieve higher efficacy in stem cell transplantation

Peripheral fibroblast metabolic pathway alterations in juvenile rhesus monkeys undergoing long-term fluoxetine administration

We report on biochemical pathways perturbed upon chronic fluoxetine administration to juvenile macaques using global metabolomics analyses of fibroblasts derived from skin biopsies. After exposure to tissue culture conditions confounding environmental factors are eliminated and identification of metabolites whose levels are affected by the drug become apparent with a better signal-to-noise ratio compared to data obtained from plasma and cerebrospinal fluid (CSF). Levels of more than 200 metabolites were analyzed to interrogate affected molecular pathways and identify biomarkers of drug response. In addition, we have correlated the metabolomics results with monoamine oxidase (MAOA) genotype and impulsivity behavioral data. Affected pathways include Purine and Pyrimidine metabolisms that have been previously implicated to contribute to neuropsychiatric disorders