ANOVA-HD: Analysis of variance when both input and output layers are high-dimensional

Autor: Ana I. Vazquez, Gustavo de los Campos, Torsten Pook, Agustin Gonzalez-Reymundez, Henner Simianer, George I. Mias
Jazyk: angličtina
Rok vydání: 2020
Předmět:
Multivariate statistics
Heredity
Single Nucleotide Polymorphisms
Gene Expression
01 natural sciences
Linear span
Biochemistry
Linkage Disequilibrium
010104 statistics & probability
Statistics
Breast Tumors
Medicine and Health Sciences
Mathematics
0303 health sciences
Multidisciplinary
DNA methylation
Simulation and Modeling
Variance (accounting)
Genomics
Regression
Chromatin
Nucleic acids
Gene Expression Regulation
Neoplastic
Genetic Mapping
Oncology
Physical Sciences
Medicine
Epigenetics
Female
Analysis of variance
DNA modification
Monte Carlo Method
Chromatin modification
Research Article
Chromosome biology
Cell biology
DNA Copy Number Variations
Science
Variant Genotypes
Breast Neoplasms
Research and Analysis Methods
Polymorphism
Single Nucleotide
Set (abstract data type)
03 medical and health sciences
Breast Cancer
Genetics
Animals
Humans
0101 mathematics
030304 developmental biology
Analysis of Variance
Whole Genome Sequencing
Biology and Life Sciences
Cancers and Neoplasms
DNA
Orthogonal basis
Data set
Algebra
Linear Algebra
Eigenvectors
Chickens
Zdroj: PLoS ONE, Vol 15, Iss 12, p e0243251 (2020)
PLoS ONE
ISSN: 1932-6203
Popis: Modern genomic data sets often involve multiple data-layers (e.g., DNA-sequence, gene expression), each of which itself can be high-dimensional. The biological processes underlying these data-layers can lead to intricate multivariate association patterns. We propose and evaluate two methods to determine the proportion of variance of an output data set that can be explained by an input data set when both data panels are high dimensional. Our approach uses random-effects models to estimate the proportion of variance of vectors in the linear span of the output set that can be explained by regression on the input set. We consider a method based on an orthogonal basis (Eigen-ANOVA) and one that uses random vectors (Monte Carlo ANOVA, MC-ANOVA) in the linear span of the output set. Using simulations, we show that the MC-ANOVA method gave nearly unbiased estimates. Estimates produced by Eigen-ANOVA were also nearly unbiased, except when the shared variance was very high (e.g., >0.9). We demonstrate the potential insight that can be obtained from the use of MC-ANOVA and Eigen-ANOVA by applying these two methods to the study of multi-locus linkage disequilibrium in chicken (Gallus gallus) genomes and to the assessment of inter-dependencies between gene expression, methylation, and copy-number-variants in data from breast cancer tumors from humans (Homo sapiens). Our analyses reveal that in chicken breeding populations ~50,000 evenly-spaced SNPs are enough to fully capture the span of whole-genome-sequencing genomes. In the study of multi-omic breast cancer data, we found that the span of copy-number-variants can be fully explained using either methylation or gene expression data and that roughly 74% of the variance in gene expression can be predicted from methylation data.
Databáze: OpenAIRE
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