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Related Concept Videos

Comparing Copy Number Variations and SNPs02:26

Comparing Copy Number Variations and SNPs

Sequencing of the human genome has opened up several best-kept secrets of the genome. Scientists have identified thousands of genome variations that exist within a population. These variations can be a single nucleotide or a larger chromosomal variation.
Copy number variations or CNVs are the structural variations that cover more than 1kb of DNA sequence. The single nucleotide polymorphism (SNP), on the other hand, is a single nucleotide change or a point mutation that is found in more than 1%...
Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...
Correlation of Experimental Data01:23

Correlation of Experimental Data

Dimensional analysis simplifies complex physical problems and guides experimental investigations, but it does not provide complete solutions. It identifies the dimensionless groups that influence a phenomenon, but experimental data is needed to establish the specific relationships and validate theoretical predictions.
For example, a spherical particle moving through a viscous fluid experiences drag. Dimensional analysis shows that the drag force depends on the particle's diameter, velocity, and...
Coefficient of Correlation01:12

Coefficient of Correlation

The correlation coefficient, r, developed by Karl Pearson in the early 1900s, is numerical and provides a measure of strength and direction of the linear association between the independent variable x and the dependent variable y.
If you suspect a linear relationship between x and y, then r can measure how strong the linear relationship is.
What the VALUE of r tells us:
The value of r is always between –1 and +1: –1 ≤ r ≤ 1.
The size of the correlation r indicates the strength of the linear...
Genomics02:02

Genomics

Genomics is the science of genomes: it is the study of all the genetic material of an organism. In humans, the genome consists of information carried in 23 pairs of chromosomes in the nucleus, as well as mitochondrial DNA. In genomics, both coding and non-coding DNA is sequenced and analyzed. Genomics allows a better understanding of all living things, their evolution, and their diversity. It has a myriad of uses: for example, to build phylogenetic trees, to improve productivity and...
Calculating and Interpreting the Linear Correlation Coefficient01:11

Calculating and Interpreting the Linear Correlation Coefficient

The correlation coefficient, r, developed by Karl Pearson in the early 1900s, is numerical and provides a measure of strength and direction of the linear association between the independent variable, x, and the dependent variable, y. Hence, it is also known as the Pearson product-moment correlation coefficient. It can be calculated using the following equation:

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CorrelationCalculator and Filigree: Tools for Data-Driven Network Analysis of Metabolomics Data
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CorrelationCalculator and Filigree: Tools for Data-Driven Network Analysis of Metabolomics Data

Published on: November 10, 2023

Sparse canonical correlation analysis with application to genomic data integration.

Elena Parkhomenko1, David Tritchler, Joseph Beyene

  • 1Hospital for Sick Children Research Institute. elena@utstat.toronto.edu

Statistical Applications in Genetics and Molecular Biology
|February 19, 2009
PubMed
Summary
This summary is machine-generated.

Sparse Canonical Correlation Analysis (SCCA) identifies relationships between variable sets in high-dimensional genomic data. This method selects sparse subsets of variables, improving biological interpretability and computational efficiency for complex analyses.

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A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
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A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

Published on: December 10, 2012

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CorrelationCalculator and Filigree: Tools for Data-Driven Network Analysis of Metabolomics Data
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A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
12:39

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

Published on: December 10, 2012

Area of Science:

  • Genomics
  • Statistical Genetics
  • Bioinformatics

Background:

  • Large-scale genomic studies involve complex multivariate relationships between phenotypic and genotypic data.
  • Traditional canonical correlation analysis (CCA) struggles with high-dimensional data due to lack of biological plausibility and interpretability.
  • Insufficient sample sizes exacerbate computational issues and reduce generalizability in high-dimensional CCA.

Purpose of the Study:

  • To introduce Sparse Canonical Correlation Analysis (SCCA) for identifying multivariate relationships in high-dimensional data.
  • To develop an extension, adaptive SCCA, addressing limitations of standard SCCA.
  • To enhance biological interpretability and computational efficiency in genomic data analysis.

Main Methods:

  • Sparse Canonical Correlation Analysis (SCCA) maximizes correlation between variable subsets while performing variable selection.
  • Adaptive SCCA, an extension of SCCA, further refines variable selection.
  • Evaluation using simulated data and application to human gene expression data.

Main Results:

  • SCCA provides sparse solutions, selecting small subsets of variables for improved interpretability.
  • Adaptive SCCA offers an enhanced approach to variable selection in high-dimensional correlation analysis.
  • Both methods demonstrate utility in analyzing natural variation in human gene expression.

Conclusions:

  • SCCA and adaptive SCCA are effective for analyzing high-dimensional genomic data, offering interpretable and computationally feasible solutions.
  • These methods address key challenges in multivariate analysis of large-scale biological datasets.
  • The application to human gene expression highlights their practical relevance in biological research.