14648575
dChipSNP: significance curve and clustering of SNP-array-based loss-of-heterozygosity data
MOTIVATION
Oligonucleotide microarrays allow genotyping of thousands of single-nucleotide polymorphisms (SNPs) in parallel. Recently, this technology has been applied to loss-of-heterozygosity (LOH) analysis of paired normal and tumor samples. However, methods and software for analyzing such data are not fully developed.


RESULT
Here, we report automated methods for pooling SNP array replicates to make LOH calls, visualizing SNP and LOH data along chromosomes in the context of genes and cytobands, making statistical inference to identify shared LOH regions, clustering samples based on LOH profiles and correlating the clustering results to clinical variables. Application of these methods to prostate and breast cancer datasets generates biologically important results.


AVAILABILITY
The software module dChipSNP implementing these methods is available at http://biosun1.harvard.edu/complab/dchip/snp/


SUPPLEMENTARY INFORMATION
The breast cancer data are provided by Andrea L. Richardson, Zhigang C. Wang and James D. Iglehart.
40827612
Conditional entropy coding of VQ indexes for image compression
Vector quantization (VQ) is a source coding methodology with provable rate-distortion optimality. However, despite more than two decades of intensive research, VQ theoretical promise is yet to be fully realized in image compression practice. Restricted by high VQ complexity in dimensions and due to high-order sample correlations in images, block sizes of practical VQ image coders are hardly large enough to achieve the rate-distortion optimality. Among the large number of VQ variants in the literature, a technique called address VQ (A-VQ) by Nasrabadi and Feng (1990) achieved the best rate-distortion performance so far to the best of our knowledge. The essence of A-VQ is to effectively increase VQ dimensions by a lossless coding of a group of 16-dimensional VQ codewords that are spatially adjacent. From a different perspective, we can consider a signal source that is coded by memoryless basic VQ to be just another signal source whose samples are the indices of the memoryless VQ codewords, and then induce the problem of lossless compression of the VQ-coded source. If the memoryless VQ is not rate-distortion optimal (often the case in practice), then there must exist hidden structures between the samples of VQ-coded source (VQ codewords). Therefore, an alternative way of approaching the rate-distortion optimality is to model and utilize these inter-codewords structures or correlations by context modeling and conditional entropy coding of VQ indexes.
14960694
Comparative analysis of gene sets in the gene ontology space under the multiple hypothesis testing framework
The gene ontology (GO) resource can be used as a powerful tool to uncover the properties shared among, and specific to, a list of genes produced by high-throughput functional genomics studies, such as microarray studies. In the comparative analysis of several gene lists, researchers maybe interested in knowing which GO terms are enriched in one list of genes but relatively depleted in another. Statistical tests such as Fisher's exact test or Chi-square test can be performed to search for such GO terms. However, because multiple GO terms are tested simultaneously, individual p-values from individual tests do not serve as good indicators for picking GO terms. Furthermore, these multiple tests are highly correlated, usual multiple testing procedures that work under an independence assumption are not applicable. In this paper we introduce a procedure, based on false discovery rate (FDR), to treat this correlated multiple testing problem. This procedure calculates a moderately conserved estimator of q-value for every GO term. We identify the GO terms with q-values that satisfy a desired level as the significant GO terms. This procedure has been implemented into the GoSurfer software. GoSurfer is a windows based graphical data mining tool. It is freely available at http://www.gosurfer.org.
1444557
Conditional entropy coding of VQ indexes for image compression
Block sizes of practical vector quantization (VQ) image coders are not large enough to exploit all high-order statistical dependencies among pixels. Therefore, adaptive entropy coding of VQ indexes via statistical context modeling can significantly reduce the bit rate of VQ coders for given distortion. Address VQ was a pioneer work in this direction. In this paper we develop a framework of conditional entropy coding of VQ indexes (CECOVI) based on a simple Bayesian-type method of estimating probabilities conditioned on causal contexts, CECOVI is conceptually cleaner and algorithmically more efficient than address VQ, with address-VQ technique being its special case. It reduces the bit rate of address VQ by more than 20% for the same distortion, and does so at only a tiny fraction of address VQ's computational cost.
4523050
Determination of Local Statistical Significance of Patterns in Markov Sequences with Application to Promoter Element Identification
High-level eukaryotic genomes present a particular challenge to the computational identification of transcription factor binding sites (TFBSs) because of their long noncoding regions and large numbers of repeat elements. This is evidenced by the noisy results generated by most current methods. In this paper, we present a p-value-based scoring scheme using probability generating functions to evaluate the statistical significance of potential TFBSs. Furthermore, we introduce the local genomic context into the model so that candidate sites are evaluated based both on their similarities to known binding sites and on their contrasts against their respective local genomic contexts. We demonstrate that our approach is advantageous in the prediction of myogenin and MEF2 binding sites in the human genome. We also apply LMM to large-scale human binding site sequences in situ and found that, compared to current popular methods, LMM analysis can reduce false positive errors by more than 50% without compromising sensitivity. This improvement will be of importance to any subsequent algorithm that aims to detect regulatory modules based on known PSSMs.
1021507
Augmented vessels for quantitative analysis of vascular abnormalities and endovascular treatment planning
Endovascular treatment plays an important role in the minimally invasive treatment of patients with vascular diseases, a major cause of morbidity and mortality worldwide. Given a segmentation of an angiography, quantitative analysis of abnormal structures can aid radiologists in choosing appropriate treatments and apparatuses. However, effective quantitation is only attainable if the abnormalities are identified from the vasculature. To achieve this, a novel method is developed, which works on the simpler shape of normal vessels to identify different vascular abnormalities (viz. stenotic atherosclerotic plaque, and saccular and fusiform aneurysmal lumens) in an indirect fashion, instead of directly manipulating the complex-shaped abnormalities. The proposed method has been tested on three synthetic and 17 clinical data sets. Comparisons with two related works are also conducted. Experimental results show that our method can produce satisfactory identification of the abnormalities and approximations of the ideal post-treatment vessel lumens. In addition, it can help increase the repeatability of the measurement of clinical parameters significantly
7433522
Effective Fault Localization using Code Coverage
Localizing a bug in a program can be a complex and time- consuming process. In this paper we propose a code coverage-based fault localization method to prioritize suspicious code in terms of its likelihood of containing program bugs. Code with a higher risk should be examined before that with a lower risk, as the former is more suspicious (i.e., more likely to contain program bugs) than the latter. We also answer a very important question: how can each additional test case that executes the program successfully help locate program bugs? We propose that with respect to a piece of code, the aid introduced by the first successful test that executes it in computing its likelihood of containing a bug is larger than or equal to that of the second successful test that executes it, which is larger than or equal to that of the third successful test that executes it, etc. A tool, chiDebug, was implemented to automate the computation of the risk of the code and the subsequent prioritization of suspicious code for locating program bugs. A case study using the Siemens suite was also conducted. Data collected from our study support the proposal described above. They also indicate that our method (in particular Heuristics III (c), (d), and (e)) can effectively reduce the search domain for locating program bugs.
14591314
Optimizing and simplifying software metric models constructed using maximum likelihood methods
A software metric model can be used to predict a target metric (e.g., the development work effort) for a future release of a software system based on the project's predictor metrics (e.g., the project team size). However, missing or incomplete data often appear in the data samples used to construct the model. So far, the least biased and thus the most recommended software metric models for dealing with the missing/incomplete data are those constructed by using the maximum likelihood methods. It is true that the inclusion of a particular predictor metric in the model construction is initially based on an intuitive or experience-based assumption that the predictor metric impacts significantly the target metric. Nevertheless, this assumption has to be verified. Previous research on metric models constructed by using the maximum likelihood methods simply took this verification for granted. This can result in probable inclusion of superfluous predictor metric(s) and/or unnecessary predictor metric complexity. In this paper, we propose a methodology to optimize and simplify such models based on the results of appropriate hypothesis tests. An experiment is also reported to demonstrate the use of our methodology in trimming redundant predictor metric(s) and/or unnecessary predictor metric complexity.
18143878
Compiling Parallel Lisp for a Shared Memory Multiprocessor
There is a commonly held idea that although Lisp is a good language for prototyping and software development, it is often too inefcient to be useful in actual implementation. Advances in compiling Lisp have begun to change this notion. However, the situation is less clear in the case of parallel Lisp dialects. In this paper, we report on our experience in implementing a compiler for a parallel Lisp dialect we called BaLinda Lisp for a shared memory multiprocessor. For a class of problems, our compiler was able to achieve performances on Lisp programs that are near, if not equal to, their imperative (C) equivalents. However, on other problems, the situation is less than ideal. We investigate this phenomenum and outline the future challenges in realizing a production strength parallel Lisp compiler.
32484854
Some Critical Remarks on a Hierarchy of Fault-Detecting Abilities of Test Methods
In a recent article by P.G. Frankl and E.J. Weyuker (see ibid., vol.19, no.3, p.962-75, 1993), results are reported that appear to establish a hierarchy of software test methods based on their respective abilities to detect faults. The methods used by Frankl and Weyuker to obtain this hierarchy constitute a new and important addition to their arsenal of tools. These tools were developed specifically to establish simple, useful comparisons of test data generation methods. This is the latest step in an ambitious test method classification program undertaken by the Frankl and Weyuker and their collaborators. The article discusses the method and goes on to present a reply to the critique. >