scholarly journals Clique-Based Clustering of Correlated SNPs in a Gene Can Improve Performance of Gene-Based Multi-Bin Linear Combination Test

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Yun Joo Yoo ◽  
Sun Ah Kim ◽  
Shelley B. Bull
Keyword(s):  
Linear Combination ◽  
Degrees Of Freedom ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Snp Analysis ◽  
Nucleotide Polymorphisms ◽  
Test Statistic ◽  
Global Test ◽  
Combination Test ◽  
Linear Combination Test

Gene-based analysis of multiple single nucleotide polymorphisms (SNPs) in a gene region is an alternative to single SNP analysis. The multi-bin linear combination test (MLC) proposed in previous studies utilizes the correlation among SNPs within a gene to construct a gene-based global test. SNPs are partitioned into clusters of highly correlated SNPs, and the MLC test statistic quadratically combines linear combination statistics constructed for each cluster. The test has degrees of freedom equal to the number of clusters and can be more powerful than a fully quadratic or fully linear test statistic. In this study, we develop a new SNP clustering algorithm designed to find cliques, which are complete subnetworks of SNPs with all pairwise correlations above a threshold. We evaluate the performance of the MLC test using the clique-based CLQ algorithm versus using the tag-SNP-based LDSelect algorithm. In our numerical power calculations we observed that the two clustering algorithms produce identical clusters about 40~60% of the time, yielding similar power on average. However, because the CLQ algorithm tends to produce smaller clusters with stronger positive correlation, the MLC test is less likely to be affected by the occurrence of opposing signs in the individual SNP effect coefficients.

scholarly journals Longitudinal linear combination test for gene set analysis

2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Elham Khodayari Moez ◽  
Morteza Hajihosseini ◽  
Jeffrey L. Andrews ◽  
Irina Dinu
Keyword(s):  
Linear Combination ◽  
Pathway Analysis ◽  
Time Course ◽  
Expression Patterns ◽  
Gene Sets ◽  
Wide Range ◽  
Combination Test ◽  
Microarray Studies ◽  
Study Designs ◽  
Linear Combination Test

Abstract Background Although microarray studies have greatly contributed to recent genetic advances, lack of replication has been a continuing concern in this area. Complex study designs have the potential to address this concern, though they remain undervalued by investigators due to the lack of proper analysis methods. The primary challenge in the analysis of complex microarray study data is handling the correlation structure within data while also dealing with the combination of large number of genetic measurements and small number of subjects that are ubiquitous even in standard microarray studies. Motivated by the lack of available methods for analysis of repeatedly measured phenotypic or transcriptomic data, herein we develop a longitudinal linear combination test (LLCT). Results LLCT is a two-step method to analyze multiple longitudinal phenotypes when there is high dimensionality in response and/or explanatory variables. Alternating between calculating within-subjects and between-subjects variations in two steps, LLCT examines if the maximum possible correlation between a linear combination of the time trends and a linear combination of the predictors given by the gene expressions is statistically significant. A generalization of this method can handle family-based study designs when the subjects are not independent. This method is also applicable to time-course microarray, with the ability to identify gene sets that exhibit significantly different expression patterns over time. Based on the results from a simulation study, LLCT outperformed its alternative: pathway analysis via regression. LLCT was shown to be very powerful in the analysis of large gene sets even when the sample size is small. Conclusions This self-contained pathway analysis method is applicable to a wide range of longitudinal genomics, proteomics, metabolomics (OMICS) data, allows adjusting for potentially time-dependent covariates and works well with unbalanced and incomplete data. An important potential application of this method could be time-course linkage of OMICS, an attractive possibility for future genetic researchers. Availability: R package of LLCT is available at: https://github.com/its-likeli-jeff/LLCT

scholarly journals Linear combination test for gene set analysis of a continuous phenotype

2013 ◽  
Vol 14 (1) ◽  
Author(s):  
Irina Dinu ◽  
Xiaoming Wang ◽  
Linda E Kelemen ◽  
Shabnam Vatanpour ◽  
Saumyadipta Pyne
Keyword(s):  
Linear Combination ◽  
Gene Set Analysis ◽  
Gene Set ◽  
Combination Test ◽  
Continuous Phenotype ◽  
Linear Combination Test

Linear Combination Test for Hierarchical Gene Set Analysis

2011 ◽  
Vol 10 (1) ◽  
Author(s):  
Xiaoming Wang ◽  
Irina Dinu ◽  
Wei Liu ◽  
Yutaka Yasui
Keyword(s):  
Gene Expression ◽  
Linear Combination ◽  
Covariance Matrix ◽  
Gene Set Analysis ◽  
Gene Set ◽  
Hotelling's T2 ◽  
Gene Sets ◽  
Hotelling’S T2 ◽  
Combination Test ◽  
Linear Combination Test

Gene-set analysis (GSA) aims to identify sets of differentially expressed genes by a phenotype in DNA microarray studies. Challenges occur due to the salient characteristics of the data: (1) the number of genes is far larger than the number of observations; (2) gene expression measurements, especially within each gene set, can be highly correlated; and (3) the number of gene sets that can be examined is large and increasing rapidly. These challenges call for gene-set testing procedures that have both efficiency in computation for large GSAs and high power in the presence of the high correlation.We propose a new GSA approach called Linear Combination Test (LCT), incorporating the covariance matrix estimator of gene expression into the test statistic. The proposed LCT and two other GSA methods, a mod-ification of Hotelling’s T2 using a shrinkage covariance matrix and our SAM-GS (Dinu et. al. 2007), the two methods that have been reported by Tsai and Chen (2009) to perform best in terms of power, are evaluated in simulation studies and a real microarray study. The LCT method is more computationally efficient than the modified Hotelling’s T2 and approximates the superb power of the modified Hotelling’s T2. LCT is slightly faster than SAM-GS, but more powerful, due to incorporating the covariance matrix estimator. An extra step to enhance the interpretation of GSA results is also proposed in the form of a hierarchical LC (HLC) testing procedure, providing scientists useful hierarchical information on gene sets that LCT identified as differentially expressed.Availability: A free R-code to perform LCT-GSA and HLC test is available at http://www.ualberta.ca/~yyasui/homepage.html.

Semi-Supervised Clustering for the Identification of Different Cancer Types Using the Gene Expression Profiles

2012 ◽  
pp. 50-66
Author(s):  
Manuel Martín-Merino
Keyword(s):  
Gene Expression ◽  
Linear Combination ◽  
Clustering Algorithm ◽  
Expression Profiles ◽  
Error Function ◽  
Clustering Algorithms ◽  
Gene Expression Profiles ◽  
Data Sets ◽  
Supervised Clustering ◽  
Supervised Learning Algorithms

DNA Microarrays allow for monitoring the expression level of thousands of genes simultaneously across a collection of related samples. Supervised learning algorithms such as -NN or SVM (Support Vector Machines) have been applied to the classification of cancer samples with encouraging results. However, the classification algorithms are not able to discover new subtypes of diseases considering the gene expression profiles. In this chapter, the author reviews several supervised clustering algorithms suitable to discover new subtypes of cancer. Next, he introduces a semi-supervised clustering algorithm that learns a linear combination of dissimilarities from the a priory knowledge provided by human experts. A priori knowledge is formulated in the form of equivalence constraints. The minimization of the error function is based on a quadratic optimization algorithm. A norm regularizer is included that penalizes the complexity of the family of distances and avoids overfitting. The method proposed has been applied to several benchmark data sets and to human complex cancer problems using the gene expression profiles. The experimental results suggest that considering a linear combination of heterogeneous dissimilarities helps to improve both classification and clustering algorithms based on a single similarity.

Semi-Supervised Clustering for the Identification of Different Cancer Types Using the Gene Expression Profiles

2013 ◽  
pp. 1609-1625
Author(s):  
Manuel Martín-Merino
Keyword(s):  
Gene Expression ◽  
Linear Combination ◽  
Clustering Algorithm ◽  
Expression Profiles ◽  
Error Function ◽  
Clustering Algorithms ◽  
Gene Expression Profiles ◽  
Data Sets ◽  
Supervised Clustering ◽  
Supervised Learning Algorithms

DNA Microarrays allow for monitoring the expression level of thousands of genes simultaneously across a collection of related samples. Supervised learning algorithms such as k-NN or SVM (Support Vector Machines) have been applied to the classification of cancer samples with encouraging results. However, the classification algorithms are not able to discover new subtypes of diseases considering the gene expression profiles. In this chapter, the author reviews several supervised clustering algorithms suitable to discover new subtypes of cancer. Next, he introduces a semi-supervised clustering algorithm that learns a linear combination of dissimilarities from the a priory knowledge provided by human experts. A priori knowledge is formulated in the form of equivalence constraints. The minimization of the error function is based on a quadratic optimization algorithm. A L2 norm regularizer is included that penalizes the complexity of the family of distances and avoids overfitting. The method proposed has been applied to several benchmark data sets and to human complex cancer problems using the gene expression profiles. The experimental results suggest that considering a linear combination of heterogeneous dissimilarities helps to improve both classification and clustering algorithms based on a single similarity.

scholarly journals Accurate recapture identification for genetic mark–recapture studies with error-tolerant likelihood-based match calling and sample clustering

2016 ◽  
Vol 3 (12) ◽  
pp. 160457 ◽  
Author(s):  
Suresh A. Sethi ◽  
Daniel Linden ◽  
John Wenburg ◽  
Cara Lewis ◽  
Patrick Lemons ◽  
...  
Keyword(s):  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Study Data ◽  
Genotyping Error ◽  
Nucleotide Polymorphisms ◽  
Pacific Walrus ◽  
Mark Recapture ◽  
Pekania Pennanti ◽  
Wide Range ◽  
The Face

Error-tolerant likelihood-based match calling presents a promising technique to accurately identify recapture events in genetic mark–recapture studies by combining probabilities of latent genotypes and probabilities of observed genotypes, which may contain genotyping errors. Combined with clustering algorithms to group samples into sets of recaptures based upon pairwise match calls, these tools can be used to reconstruct accurate capture histories for mark–recapture modelling. Here, we assess the performance of a recently introduced error-tolerant likelihood-based match-calling model and sample clustering algorithm for genetic mark–recapture studies. We assessed both biallelic (i.e. single nucleotide polymorphisms; SNP) and multiallelic (i.e. microsatellite; MSAT) markers using a combination of simulation analyses and case study data on Pacific walrus ( Odobenus rosmarus divergens ) and fishers ( Pekania pennanti ). A novel two-stage clustering approach is demonstrated for genetic mark–recapture applications. First, repeat captures within a sampling occasion are identified. Subsequently, recaptures across sampling occasions are identified. The likelihood-based matching protocol performed well in simulation trials, demonstrating utility for use in a wide range of genetic mark–recapture studies. Moderately sized SNP (64+) and MSAT (10–15) panels produced accurate match calls for recaptures and accurate non-match calls for samples from closely related individuals in the face of low to moderate genotyping error. Furthermore, matching performance remained stable or increased as the number of genetic markers increased, genotyping error notwithstanding.

Handling WSD using Hierarchical Clustering Algorithm with sentences

2018 ◽  
pp. 83-88
Author(s):  
Mohana Priya K ◽  
Pooja Ragavi S ◽  
Krishna Priya G
Keyword(s):  
Hierarchical Clustering ◽  
Similarity Measure ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Cosine Similarity Measure ◽  
Hierarchical Clustering Algorithm ◽  
Multiple Levels ◽  
Pos Tagger ◽  
Sentence Clustering ◽  
The Right

Clustering is the process of grouping objects into subsets that have meaning in the context of a particular problem. It does not rely on predefined classes. It is referred to as an unsupervised learning method because no information is provided about the "right answer" for any of the objects. Many clustering algorithms have been proposed and are used based on different applications. Sentence clustering is one of best clustering technique. Hierarchical Clustering Algorithm is applied for multiple levels for accuracy. For tagging purpose POS tagger, porter stemmer is used. WordNet dictionary is utilized for determining the similarity by invoking the Jiang Conrath and Cosine similarity measure. Grouping is performed with respect to the highest similarity measure value with a mean threshold. This paper incorporates many parameters for finding similarity between words. In order to identify the disambiguated words, the sense identification is performed for the adjectives and comparison is performed. semcor and machine learning datasets are employed. On comparing with previous results for WSD, our work has improvised a lot which gives a percentage of 91.2%

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