12770105
A tractable probabilistic model for Affymetrix probe-level analysis across multiple chips
MOTIVATION
Affymetrix GeneChip arrays are currently the most widely used microarray technology. Many summarization methods have been developed to provide gene expression levels from Affymetrix probe-level data. Most of the currently popular methods do not provide a measure of uncertainty for the expression level of each gene. The use of probabilistic models can overcome this limitation. A full hierarchical Bayesian approach requires the use of computationally intensive MCMC methods that are impractical for large datasets. An alternative computationally efficient probabilistic model, mgMOS, uses Gamma distributions to model specific and non-specific binding with a latent variable to capture variations in probe affinity. Although promising, the main limitations of this model are that it does not use information from multiple chips and does not account for specific binding to the mismatch (MM) probes.


RESULTS
We extend mgMOS to model the binding affinity of probe-pairs across multiple chips and to capture the effect of specific binding to MM probes. The new model, multi-mgMOS, provides improved accuracy, as demonstrated on some bench-mark datasets and a real time-course dataset, and is much more computationally efficient than a competing hierarchical Bayesian approach that requires MCMC sampling. We demonstrate how the probabilistic model can be used to estimate credibility intervals for expression levels and their log-ratios between conditions.


AVAILABILITY
Both mgMOS and the new model multi-mgMOS have been implemented in an R package, which is available at http://www.bioinf.man.ac.uk/resources/puma.
17314700
Propagating uncertainty in microarray data analysis
Microarray technology is associated with many sources of experimental uncertainty. In this review we discuss a number of approaches for dealing with this uncertainty in the processing of data from microarray experiments. We focus here on the analysis of high-density oligonucleotide arrays, such as the popular Affymetrix GeneChip array, which contain multiple probes for each target. This set of probes can be used to determine an estimate for the target concentration and can also be used to determine the experimental uncertainty associated with this measurement. This measurement uncertainty can then be propagated through the downstream analysis using probabilistic methods. We give examples showing how these credibility intervals can be used to help identify differential expression, to combine information from replicated experiments and to improve the performance of principal component analysis.
8835059
Computing the maximum similarity bi-clusters of gene expression data
MOTIVATIONS
Bi-clustering is an important approach in microarray data analysis. The underlying bases for using bi-clustering in the analysis of gene expression data are (1) similar genes may exhibit similar behaviors only under a subset of conditions, not all conditions, (2) genes may participate in more than one function, resulting in one regulation pattern in one context and a different pattern in another. Using bi-clustering algorithms, one can obtain sets of genes that are co-regulated under subsets of conditions.


RESULTS
We develop a polynomial time algorithm to find an optimal bi-cluster with the maximum similarity score. To our knowledge, this is the first formulation for bi-cluster problems that admits a polynomial time algorithm for optimal solutions. The algorithm works for a special case, where the bi-clusters are approximately squares. We then extend the algorithm to handle various kinds of other cases. Experiments on simulation data and real data show that the new algorithms outperform most of the existing methods in many cases. Our new algorithms have the following advantages: (1) no discretization procedure is required, (2) performs well for overlapping bi-clusters and (3) works well for additive bi-clusters.


AVAILABILITY
The software is available at http://www.cs.cityu.edu.hk/~liuxw/msbe/help.html.
17696206
P-quadtrees: a point and polygon hybrid multi-resolution rendering approach
Point and polygon representations have their respective merits in rendering objects. In this paper, we propose a novel hybrid multi-resolution approach, PQuadtrees, to efficiently render highly detailed objects. PQuadtrees are constructed from geometry images. Both point and polygon are tightly integrated into a uniform structure. While traversing the P-Quadtrees in rendering, the part of surface that face the viewer can be rendered by coarser quad mesh to reduce the numbers of rendering primitives. The shading details can be enhanced by hardware accelerated normal mapping. The view dependent LOD selects the finer hierarchy on silhouette, which is rendered by points. The rendering of large-scale model is greatly accelerated while the visual effect both at the surfaces and the silhouette is guaranteed.
39271056
A hybrid representation of environment models in image-based real time walkthrough
In this paper, a hybrid representation of environment models using a combination of points and polygons is proposed. Through the model, a real time walkthrough of a complex environment can be achieved. We start from multiple depth reference images and classify the pixels of images into two categories, corresponding respectively to planar and non-planar surfaces in 3D space, then all the redundant points are removed by a comparison algorithm of sampling rate. For pixels corresponding to nonplanar surfaces, the point representation is maintained, and a local reconstruction and resampling process is employed to re-sample a set of points that distribute more uniformly on the surfaces. The re-sampled points are organized by an OBB-tree according to their space coordinates and a multiresolution structure is built to improve the rendering efficiency. For pixels corresponding to planar surfaces, the plane coefficients are reconstructed and all their appearances in the reference images are merged to texture maps. Under this hybrid representation, texture mapping and point-based rendering are employed to render the novel views, to take full advantage of the acceleration utility of graphics hardware. The algorithm demonstrates the combined advantages of approaches in traditional computer graphics and PBR/IBR, texture mapping for plane surfaces and point-based rendering for high detail surfaces and shapes.
2696332
A boosting approach for motif modeling using ChIP-chip data
MOTIVATION
Building an accurate binding model for a transcription factor (TF) is essential to differentiate its true binding targets from those spurious ones. This is an important step toward understanding gene regulation.


RESULTS
This paper describes a boosting approach to modeling TF-DNA binding. Different from the widely used weight matrix model, which predicts TF-DNA binding based on a linear combination of position-specific contributions, our approach builds a TF binding classifier by combining a set of weight matrix based classifiers, thus yielding a non-linear binding decision rule. The proposed approach was applied to the ChIP-chip data of Saccharomyces cerevisiae. When compared with the weight matrix method, our new approach showed significant improvements on the specificity in a majority of cases.
893749
A Distributed and Efficient Flooding Scheme Using 1-Hop Information in Mobile Ad Hoc Networks
Flooding is one of the most fundamental operations in mobile ad hoc networks. Traditional implementation of flooding suffers from the problems of excessive redundancy of messages, resource contention, and signal collision. This causes high protocol overhead and interference with the existing traffic in the networks. Some efficient flooding algorithms were proposed to avoid these problems. However, these algorithms either perform poorly in reducing redundant transmissions or require each node to maintain 2-hop (or more) neighbors information. In the paper, we study the sufficient and necessary condition of 100 percent deliverability for flooding schemes that are based on only 1-hop neighbors information. We further propose an efficient flooding algorithm that achieves the local optimality in two senses: 1) the number of forwarding nodes in each step is minimal and 2) the time complexity for computing forwarding nodes is the lowest, which is O(nlogn), where n is the number of neighbors of a node. Extensive simulations have been conducted and simulation results have shown the excellent performance of our algorithm