Genetic association test based on principal component analysis

2017 ◽  
Vol 16 (3) ◽  
Author(s):  
Zhongxue Chen ◽  
Shizhong Han ◽  
Kai Wang
Keyword(s):  
Principal Component Analysis ◽  
Genetic Association ◽  
Rare Variants ◽  
Association Studies ◽  
Principal Component ◽  
Real Data ◽  
Component Analysis ◽  
Association Test ◽  
Simulation Studies ◽  
Special Case

AbstractMany gene- and pathway-based association tests have been proposed in the literature. Among them, the SKAT is widely used, especially for rare variants association studies. In this paper, we investigate the connection between SKAT and a principal component analysis. This investigation leads to a procedure that encompasses SKAT as a special case. Through simulation studies and real data applications, we compare the proposed method with some existing tests.

scholarly journals Maximizing the Power of Principal-Component Analysis of Correlated Phenotypes in Genome-wide Association Studies

2014 ◽  
Vol 94 (5) ◽  
pp. 662-676 ◽  
Author(s):  
Hugues Aschard ◽  
Bjarni J. Vilhjálmsson ◽  
Nicolas Greliche ◽  
Pierre-Emmanuel Morange ◽  
David-Alexandre Trégouët ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Association Studies ◽  
Principal Component ◽  
Component Analysis ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genome Wide

The Scree Test and the Number of Factors: a Dynamic Graphics Approach

2015 ◽  
Vol 18 ◽  
Author(s):  
Rubén Daniel Ledesma ◽  
Pedro Valero-Mora ◽  
Guillermo Macbeth
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Real Data ◽  
Component Analysis ◽  
Psychological Research ◽  
Scree Plot ◽  
Dynamic Graphics ◽  
Number Of Factors ◽  
Interactive Data ◽  
Scree Test

AbstractExploratory Factor Analysis and Principal Component Analysis are two data analysis methods that are commonly used in psychological research. When applying these techniques, it is important to determine how many factors to retain. This decision is sometimes based on a visual inspection of the Scree plot. However, the Scree plot may at times be ambiguous and open to interpretation. This paper aims to explore a number of graphical and computational improvements to the Scree plot in order to make it more valid and informative. These enhancements are based on dynamic and interactive data visualization tools, and range from adding Parallel Analysis results to "linking" the Scree plot with other graphics, such as factor-loadings plots. To illustrate our proposed improvements, we introduce and describe an example based on real data on which a principal component analysis is appropriate. We hope to provide better graphical tools to help researchers determine the number of factors to retain.

scholarly journals Microsaccade characterization using the continuous wavelet transform and principal component analysis

2010 ◽  
Vol 3 (5) ◽  
Author(s):  
Mario Bettenbühl ◽  
Claudia Paladini ◽  
Konstantin Mergenthaler ◽  
Reinhold Kliegl ◽  
Ralf Engbert ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Wavelet Transform ◽  
Eye Movements ◽  
Continuous Wavelet Transform ◽  
Principal Component ◽  
Real Data ◽  
Component Analysis ◽  
Continuous Wavelet ◽  
Fixational Eye Movements ◽  
Definition Of

During visual fixation on a target, humans perform miniature (or fixational) eye movements consisting of three components, i.e., tremor, drift, and microsaccades. Microsaccades are high velocity components with small amplitudes within fixational eye movements. However, microsaccade shapes and statistical properties vary between individual observers. Here we show that microsaccades can be formally represented with two significant shapes which we identfied using the mathematical definition of singularities for the detection of the former in real data with the continuous wavelet transform. For character-ization and model selection, we carried out a principal component analysis, which identified a step shape with an overshoot as first and a bump which regulates the overshoot as second component. We conclude that microsaccades are singular events with an overshoot component which can be detected by the continuous wavelet transform.

scholarly journals Kernel Principal Component Analysis Allowing Sparse Representation and Sample Selection

2019 ◽  
Vol 13 (1) ◽  
pp. 9-20 ◽  
Author(s):  
Duo Wang ◽  
Toshihisa Tanaka
Keyword(s):  
Principal Component Analysis ◽  
Sparse Representation ◽  
Sample Selection ◽  
Principal Component ◽  
Real Data ◽  
Component Analysis ◽  
Kernel Functions ◽  
Kernel Principal Component Analysis ◽  
Alternating Direction ◽  
Sparse Kernel

Kernel principal component analysis (KPCA) is a kernelized version of principal component analysis (PCA). A kernel principal component is a superposition of kernel functions. Due to the number of kernel functions equals the number of samples, each component is not a sparse representation. Our purpose is to sparsify coefficients expressing in linear combination of kernel functions, two types of sparse kernel principal component are proposed in this paper. The method for solving sparse problem comprises two steps: (a) we start with the Pythagorean theorem and derive an explicit regression expression of KPCA and (b) two types of regularization $l_1$-norm or $l_{2,1}$-norm are added into the regression expression in order to obtain two different sparsity form, respectively. As the proposed objective function is different from elastic net-based sparse PCA (SPCA), the SPCA method cannot be directly applied to the proposed cost function. We show that the sparse representations are obtained in its iterative optimization by conducting an alternating direction method of multipliers. Experiments on toy examples and real data confirm the performance and effectiveness of the proposed method.

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