PAcluster: Clustering polyadenylation site data using canonical correlation analysis

2017 ◽  
Vol 15 (05) ◽  
pp. 1750018 ◽  
Author(s):  
Guoli Ji ◽  
Qianmin Lin ◽  
Yuqi Long ◽  
Congting Ye ◽  
Wenbin Ye ◽  
...  
Keyword(s):  
Correlation Analysis ◽  
Canonical Correlation Analysis ◽  
Canonical Correlation ◽  
Alternative Polyadenylation ◽  
Gene Clusters ◽  
R Package ◽  
Distance Measures ◽  
Specific Gene ◽  
Performance Indexes ◽  
Biological Dataset

Alternative polyadenylation (APA) is a pervasive mechanism that contributes to gene regulation. Increasing sequenced poly(A) sites are placing new demands for the development of computational methods to investigate APA regulation. Cluster analysis is important to identify groups of co-expressed genes. However, clustering of poly(A) sites has not been extensively studied in APA, where most APA studies failed to consider the distribution, abundance, and variation of APA sites in each gene. Here we constructed a two-layer model based on canonical correlation analysis (CCA) to explore the underlying biological mechanisms in APA regulation. The first layer quantifies the general correlation of APA sites across various conditions between each gene and the second layer identifies genes with statistically significant correlation on their APA patterns to infer APA-specific gene clusters. Using hierarchical clustering, we comprehensively compared our method with four other widely used distance measures based on three performance indexes. Results showed that our method significantly enhanced the clustering performance for both synthetic and real poly(A) site data and could generate clusters with more biological meaning. We have implemented the CCA-based method as a publically available R package called PAcluster, which provides an efficient solution to the clustering of large APA-specific biological dataset.

scholarly journals Construction of Condition-Specific Gene Regulatory Network Using Kernel Canonical Correlation Analysis

2021 ◽  
Vol 12 ◽  
Author(s):  
Dabin Jeong ◽  
Sangsoo Lim ◽  
Sangseon Lee ◽  
Minsik Oh ◽  
Changyun Cho ◽  
...  
Keyword(s):  
Correlation Analysis ◽  
Canonical Correlation Analysis ◽  
Canonical Correlation ◽  
Computational Method ◽  
Transcriptional Networks ◽  
Specific Gene ◽  
Transcriptome Data ◽  
Data Set ◽  
Kernel Canonical Correlation Analysis ◽  
Kernel Cca

Gene expression profile or transcriptome can represent cellular states, thus understanding gene regulation mechanisms can help understand how cells respond to external stress. Interaction between transcription factor (TF) and target gene (TG) is one of the representative regulatory mechanisms in cells. In this paper, we present a novel computational method to construct condition-specific transcriptional networks from transcriptome data. Regulatory interaction between TFs and TGs is very complex, specifically multiple-to-multiple relations. Experimental data from TF Chromatin Immunoprecipitation sequencing is useful but produces one-to-multiple relations between TF and TGs. On the other hand, co-expression networks of genes can be useful for constructing condition transcriptional networks, but there are many false positive relations in co-expression networks. In this paper, we propose a novel method to construct a condition-specific and combinatorial transcriptional network, applying kernel canonical correlation analysis (kernel CCA) to identify multiple-to-multiple TF–TG relations in certain biological condition. Kernel CCA is a well-established statistical method for computing the correlation of a group of features vs. another group of features. We, therefore, employed kernel CCA to embed TFs and TGs into a new space where the correlation of TFs and TGs are reflected. To demonstrate the usefulness of our network construction method, we used the blood transcriptome data for the investigation on the response to high fat diet in a human and an arabidopsis data set for the investigation on the response to cold/heat stress. Our method detected not only important regulatory interactions reported in previous studies but also novel TF–TG relations where a module of TF is regulating a module of TGs upon specific stress.

scholarly journals FlashPCA: fast sparse canonical correlation analysis of genomic data

2016 ◽  
Author(s):  
Gad Abraham ◽  
Michael Inouye
Keyword(s):  
Correlation Analysis ◽  
Canonical Correlation Analysis ◽  
Canonical Correlation ◽  
Genomic Data ◽  
R Package ◽  
Rapid Analysis ◽  
Nucleotide Polymorphisms ◽  
Single Nucleotide ◽  
Sparse Canonical Correlation Analysis ◽  
Gene Expression Levels

SummarySparse canonical correlation analysis (SCCA) is a useful approach for correlating one set of measurements, such as single nucleotide polymorphisms (SNPs), with another set of measurements, such as gene expression levels. We present a fast implementation of SCCA, enabling rapid analysis of hundreds of thousands of SNPs together with thousands of phenotypes. Our approach is implemented both as an R package flashpcaR and within the standalone commandline tool flashpca.Availability and implementationhttps://github.com/gabraham/[email protected]

scholarly journals Conditional canonical correlation estimation based on covariates with random forests

2021 ◽  
Author(s):  
Cansu Alakuş ◽  
Denis Larocque ◽  
Sébastien Jacquemont ◽  
Fanny Barlaam ◽  
Charles-Olivier Martin ◽  
...  
Keyword(s):  
Correlation Analysis ◽  
Canonical Correlation Analysis ◽  
Canonical Correlation ◽  
R Package ◽  
Significance Test ◽  
Supplementary Information ◽  
Canonical Correlations ◽  
Correlation Estimation ◽  
The Individual ◽  
Individual Trees

Abstract Motivation Investigating the relationships between two sets of variables helps to understand their interactions and can be done with canonical correlation analysis (CCA). However, the correlation between the two sets can sometimes depend on a third set of covariates, often subject-related ones such as age, gender or other clinical measures. In this case, applying CCA to the whole population is not optimal and methods to estimate conditional CCA, given the covariates, can be useful. Results We propose a new method called Random Forest with Canonical Correlation Analysis (RFCCA) to estimate the conditional canonical correlations between two sets of variables given subject-related covariates. The individual trees in the forest are built with a splitting rule specifically designed to partition the data to maximize the canonical correlation heterogeneity between child nodes. We also propose a significance test to detect the global effect of the covariates on the relationship between two sets of variables. The performance of the proposed method and the global significance test is evaluated through simulation studies that show it provides accurate canonical correlation estimations and well-controlled Type-1 error. We also show an application of the proposed method with EEG data. Availability and implementation RFCCA is implemented in a freely available R package on CRAN (https://CRAN.R-project.org/package=RFCCA). Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals MOVIE: Multi-Omics Visualization of Estimated contributions

2018 ◽  
Author(s):  
Sean D. McCabe ◽  
Dan-Yu Lin ◽  
Michael I. Love
Keyword(s):  
Correlation Analysis ◽  
Canonical Correlation Analysis ◽  
Canonical Correlation ◽  
High Specificity ◽  
R Package ◽  
Data Type ◽  
The Cancer Genome Atlas ◽  
Supplementary Information ◽  
Data Types ◽  
Cancer Data

AbstractSummaryThe growth of multi-omics datasets has given rise to many methods for identifying sources of common variation across data types. The unsupervised nature of these methods makes it difficult to evaluate their performance. We present MOVIE, Multi-Omics Visualization of Estimated contributions, as a framework for evaluating the degree of overfitting and the stability of unsupervised multi-omics methods. MOVIE plots the contributions of one data type against another to produce contribution plots, where contributions are calculated for each subject and each data type from the results of each multi-omics method. The usefulness of MOVIE is demonstrated by applying existing multi-omics methods to permuted null data and breast cancer data from The Cancer Genome Atlas. Contribution plots indicated that principal components-based Canonical Correlation Analysis overfit null data, while Sparse multiple Canonical Correlation Analysis and Multi-Omics Factor Analysis provided stable results with high specificity for both the real and permuted null datasets.AvailabilityMOVIE is available as an R package at https://github.com/mccabes292/[email protected] informationSupplementary data are available at Bioinformatics online.

Analysis of Maximum Expiratory Flow Volume Curves Using Canonical Correlation Analysis

1985 ◽  
Vol 24 (02) ◽  
pp. 91-100 ◽  
Author(s):  
W. van Pelt ◽  
Ph. H. Quanjer ◽  
M. E. Wise ◽  
E. van der Burg ◽  
R. van der Lende
Keyword(s):  
Correlation Analysis ◽  
Canonical Correlation Analysis ◽  
Canonical Correlation ◽  
Flow Volume ◽  
Non Linear ◽  
Maximum Expiratory Flow ◽  
Expiratory Flow ◽  
Relationship Of ◽  
The Relationship ◽  
Age And Sex

SummaryAs part of a population study on chronic lung disease in the Netherlands, an investigation is made of the relationship of both age and sex with indices describing the maximum expiratory flow-volume (MEFV) curve. To determine the relationship, non-linear canonical correlation was used as realized in the computer program CANALS, a combination of ordinary canonical correlation analysis (CCA) and non-linear transformations of the variables. This method enhances the generality of the relationship to be found and has the advantage of showing the relative importance of categories or ranges within a variable with respect to that relationship. The above is exemplified by describing the relationship of age and sex with variables concerning respiratory symptoms and smoking habits. The analysis of age and sex with MEFV curve indices shows that non-linear canonical correlation analysis is an efficient tool in analysing size and shape of the MEFV curve and can be used to derive parameters concerning the whole curve.

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