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Nonlinear gene cluster analysis with labeling for microarray gene expression data in organ development

Martin Ehler, Vinodh N Rajapakse, Barry R Zeeberg, Brian P Brooks, Jacob Brown, Wojciech Czaja, Robert F Bonner
2011 BMC Proceedings  
Results: Our nonlinear methods created clusters of genes that mapped onto more specific biological processes and functions related to eye development as defined by Gene Ontology at lower false discovery  ...  Our results motivate further analysis of nonlinear dimension reduction with labeling within other microarray data sets from LCM dissected tissues or other cell specific samples to determine the more general  ...  To validate the findings, we also use two standard clustering schemes, basic k-means and principal component analysis combined with k-means and hierarchical clustering.  ... 
doi:10.1186/1753-6561-5-s2-s3 pmid:21554761 pmcid:PMC3090761 fatcat:ufxu4wvcdngaznjcxd6eobbfle

Topographic gradients of intrinsic dynamics across neocortex

Golia Shafiei, Ross D Markello, Reinder Vos de Wael, Boris C Bernhardt, Ben D Fulcher, Bratislav Misic
2020 eLife  
Here we comprehensively characterize the rich temporal patterns of neural activity throughout the human brain.  ...  These gradients reflect spatial patterns of gene expression, intracortical myelin and cortical thickness, as well as structural and functional network embedding.  ...  In the second analysis, principal component analysis (PCA) was performed to identify orthogonal linear combinations of time-series features that vary maximally across the cortex. timately, cortical patterns  ... 
doi:10.7554/elife.62116 pmid:33331819 pmcid:PMC7771969 fatcat:63twpz6fdra4dm3a67oxcuhsda

Analysis of Temporal-spatial Co-variation within Gene Expression Microarray Data in an Organogenesis Model [chapter]

Martin Ehler, Vinodh Rajapakse, Barry Zeeberg, Brian Brooks, Jacob Brown, Wojciech Czaja, Robert F. Bonner
2010 Lecture Notes in Computer Science  
We used a novel clustering method based on Laplacian Eigenmaps, a nonlinear dimension reduction method, to analyze microarray data from laser capture microdissected (LCM) cells at the site and developmental  ...  The combination of LCM of embryonic organs, gene expression microarrays, and extracting spatial and temporal co-variations appear to be a powerful approach to understanding the gene regulatory networks  ...  To validate the findings, we also use two standard clustering schemes, basic k-means and principal component analysis combined with k-means.  ... 
doi:10.1007/978-3-642-13078-6_6 fatcat:v64x6jsypjdqphb5pkhd47g3u4

Topographic gradients of intrinsic dynamics across neocortex [article]

Golia Shafiei, Ross D. Markello, Reinder Vos de Wael, Boris C. Bernhardt, Ben D. Fulcher, Bratislav Misic
2020 bioRxiv   pre-print
These gradients are distinct in terms of their temporal composition and reflect spatial patterns of microarray gene expression, intracortical myelin and cortical thickness, as well as structural and functional  ...  Neural activity arising from the interplay of these local and global factors therefore varies from moment to moment, with rich temporal patterns.  ...  Applying principal component analysis (PCA) to the region × feature matrix yielded mutually orthogonal patterns of intrinsic dynamics (Fig. 1) , with the top two components collectively accounting for  ... 
doi:10.1101/2020.07.03.186916 fatcat:5ogava7bhzh45glnwicf2skcw4

Pattern of extrapyramidal signs in Alzheimer's disease

Giuseppe Tosto, Sarah E. Monsell, Stephen E. Hawes, Richard Mayeux
2015 Journal of Neurology  
Patterns of presentation of EPS were identified employing categorical principal component analysis (CATPCA).  ...  Six principal components were identified in both mild and moderate AD samples: (I) hand movements, alternating movements, finger tapping, leg agility ("limbs bradykinesia"); (II) posture, postural instability  ...  We employed a nonlinear principal component analysis (CATPCA [7] ) to explore patterns of extrapyramidal signs in individuals diagnosed with Alzheimer's disease.  ... 
doi:10.1007/s00415-015-7886-1 pmid:26338814 pmcid:PMC4776751 fatcat:moqyek7x7recte5otwikgcs3sa

Application of Microarrays to Neurological Disease

Lisa-Marie Sturla, Ana Fernandez-Teijeiro, Scott L. Pomeroy
2003 Archives of Neurology  
have the potential to dramatically enhance progress, being used at all stages from target discovery (through validation of new molecular targets and understanding modes of action) to predicting patient  ...  Where genetic mutations and aberrations are already well characterized, microarrays can be customized to be effectively used as a diagnostic and prognostic tool. 2,3 In the field of drug discovery, microarrays  ...  USEFUL WEB SITES The following Web sites are useful in the study of microarray-based functional genomics: Figure 1 . 1 Principal component analysis, with axes representing the 3 principal components (  ... 
doi:10.1001/archneur.60.5.676 pmid:12756130 fatcat:jowti2shhvbrtoj5xwehlvoux4

Topographic gradients of intrinsic dynamics across neocortex

Golia Shafiei, Ross D Markello, Reinder Vos de Wael, Boris C Bernhardt, Ben D Fulcher, Bratislav Misic
2020 eLife  
Here we comprehensively characterize the rich temporal patterns of neural activity throughout the human brain.  ...  These gradients reflect spatial patterns of gene expression, intracortical myelin and cortical thickness, as well as structural and functional network embedding.  ...  This is not an artifact of the principal component analysis.  ... 
doi:10.7554/elife.62116 fatcat:zijsuzn6ezel5izzij27lkcu2m

tmap: an integrative framework based on topological data analysis for population-scale microbiome stratification and association studies

Tianhua Liao, Yuchen Wei, Mingjing Luo, Guo-Ping Zhao, Haokui Zhou
2019 Genome Biology  
The performance of tmap in detecting nonlinear patterns is validated by different scenarios of simulation, which clearly demonstrate its superiority over the most commonly used methods.  ...  Here, we present tmap, an integrative framework based on topological data analysis for population-scale microbiome stratification and association studies.  ...  Alternatively, dimension reduction methods can be used to project high-dimensional microbiome profiles to lowdimensional spaces for pattern discovery and association, such as principal coordinates analysis  ... 
doi:10.1186/s13059-019-1871-4 pmid:31870407 pmcid:PMC6927166 fatcat:3sglvqam2zg7zpzhkiayb2bl7e

Selective association between cortical thickness and reference abilities in normal aging

Seonjoo Lee, Christian Habeck, Qolamreza Razlighi, Timothy Salthouse, Yaakov Stern
2016 NeuroImage  
Global cognition was correlated with mean overall thickness, but also was found to have a regionally specific pattern of associations.  ...  In this manuscript, we showed that preserving detailed spatial patterns of cortical thickness can identify reference ability specific association besides the association explained by global cognition and  ...  No representatives of the company were involved in data analysis or development of this report, nor did the company exert any control or restrictions with regard to these activities.  ... 
doi:10.1016/j.neuroimage.2016.06.041 pmid:27353567 pmcid:PMC5159226 fatcat:kunk47rkdrg73d5otqypb2isjq

Machine learning in chemoinformatics and drug discovery

Yu-Chen Lo, Stefano E. Rensi, Wen Torng, Russ B. Altman
2018 Drug Discovery Today  
analysis.  ...  To process the chemical data, we first reviewed multiple processing layers in the chemoinformatics pipeline followed by the introduction of commonly used machine learning models in drug discovery and QSAR  ...  Unsupervised methods include dimensionality reduction techniques such as principal components analysis (PCA), independent components analysis (ICA) and several supervised methods that can also support  ... 
doi:10.1016/j.drudis.2018.05.010 pmid:29750902 pmcid:PMC6078794 fatcat:ckxznjxuujajle6iqycgi74d7i

MULTIPRODUCT PRICING IN MAJOR LEAGUE BASEBALL: A PRINCIPAL COMPONENTS ANALYSIS

CRAIG A. DEPKEN, DARREN GRANT
2011 Economic Inquiry  
This paper analyzes ticket, parking, and concession pricing in Major League Baseball for the period 1991-2003 using a new methodology based on principal components, which allows inferences to be formed  ...  This paper analyzes ticket, parking, and concession pricing in Major League Baseball for the period 1991-2003 using a new methodology based on principal components, which allows inferences to be formed  ...  The price data comprise a sample of 372 observations used in the principal component analysis.  ... 
doi:10.1111/j.1465-7295.2010.00263.x fatcat:bcwytlbdwfb5pc7djn6iincpki

Discovery of metabolite features for the modelling and analysis of high-resolution NMR spectra

Hyun Woo Cho, Seoung Bum Kim, Myong K. Jeong, Youngja Park, Nana Gletsu Miller, Thomas R. Ziegler, Dean P. Jones
2008 International Journal of Data Mining and Bioinformatics  
Loading vectors of Principal Component Analysis (PCA), the optimal discriminant direction of Fisher discriminant analysis, and index values of the Variable Importance in Projection (VIP) in a Partial Least  ...  Square Discriminant Analysis (PLS-DA) were used to calculate the importance of individual metabolite feature in spectra.  ...  Principal Components Analysis (PCA) and clustering analysis are examples of unsupervised methods that have been widely used to facilitate the extraction of implicit patterns and elicit the natural groupings  ... 
doi:10.1504/ijdmb.2008.019097 pmid:18767354 pmcid:PMC3883573 fatcat:d3tgk24rpjh5jin3bkjbmc3wzi

Reproducible analysis of disease space via principal components using the novel R package syndRomics

Abel Torres Espín, Austin Chou, Russell Huie, Nikolaos Kyritsis, Pavan S Upadhyayula, Adam Ferguson
2021 eLife  
'Syndromics' refers to an analytical framework for measuring disease states using principal component analysis and related multivariate statistics as primary tools for extracting underlying disease patterns  ...  We present a new software package, syndRomics, an open-source R package with utility for component visualization, interpretation, and stability for syndromic analysis.  ...  SoftwareDoctoral Thesis: Nonparametric Inference in Nonlinear Principal Components Analysis: Exploration and BeyondDoctoral Thesis: Nonparametric Inference in Nonlinear Principal Components Analysis: Exploration  ... 
doi:10.7554/elife.61812 fatcat:vzrkrk4v3jacrm2y434hvl3nuq

Precipitation patterns and associated hydrological extremes in the Yangtze River basin, China, using TRMM/PR data and EOF analysis

Zhandong Sun, Ni-Bin Chang, Qun Huang, Christian Opp
2012 Hydrological Sciences Journal  
With the aid of gauge station data, the amplitudes of major principal components (PCs) were used to examine the generic relationships between precipitation variations and hydrological extremes (e.g. floods  ...  A l'aide de données de la station hydrométrique, l'amplitude des principales composantes principales (CP) a été utilisée pour examiner les relations entre les variations de précipitations et les extrêmes  ...  EOFs have been used in atmospheric science since the early 1950s (Fukuoka 1951 , Obukhov 1960 , Lorenz 1970 ) and EOF techniques are deeply rooted in statistics, especially in principal component analysis  ... 
doi:10.1080/02626667.2012.716905 fatcat:szhfgf4sgbfipbyq2oqycpw44e

Nonlinear PCA for Spatio-Temporal Analysis of Earth Observation Data

Diego Bueso, Maria Piles, Gustau Camps-Valls
2020 IEEE Transactions on Geoscience and Remote Sensing  
Principal Component Analysis (PCA), also known as Empirical Orthogonal Functions (EOF) in geophysics, has been traditionally used to analyze climatic data.  ...  The method is unsupervised and computationally very efficient.We illustrate its ability to uncover spatio-temporal patterns using synthetic experiments and real data.  ...  PRINCIPAL COMPONENT ANALYSIS METHODS FOR SPATIO-TEMPORAL DATA ANALYSIS This section reviews the main ingredients of our method for nonlinear PCA-based analysis of spatio-temporal data.  ... 
doi:10.1109/tgrs.2020.2969813 fatcat:pwv4zaxyrzhdfpopo3lh4iwtey
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