runDiagnostics: Data reduction procedure

Description Usage Arguments Details Value Author(s) See Also Examples

View source: R/genphen.R

Description

The methods implemented in genphen are statistically superior to the ones implemented by most classical (frequentist) tools for GWAS. A major challenge, however, of our method is the substantially increased computational cost when analyzing thousands of SNPs. Inspired by the biological assumption that the major fraction of the studied SNPs are non-informative (genetic noise) with respect to the selected phenotype, various data reduction techniques can be implemented to quickly scan the SNP and discard a substantial portion of the the SNPs deemed to be clearly non-informative.

Usage

1
runDiagnostics(genotype, phenotype, phenotype.type, rf.trees)

Arguments

genotype

Character matrix/data frame or a vector, containing SNPs/SAAPs as columns or alternatively as DNAMultipleAlignment or AAMultipleAlignment Biostrings object.

phenotype

Numerical vector.

phenotype.type

Character indicator of the type of the phenotype, with 'Q' for a quantitative, or 'D' for a dichotomous phenotype.

rf.trees

Number of random forest trees (default = 5,000).

Details

The data reduction procedure includes the following steps:

  1. The complete data (genotypes and a single phenotype) is used to train a random forest (RF) model, which will quantify the importance of each SNP/SAAP in explaining the phenotypeassociation between each SNP and the phenotype.

  2. We can then plot the distribution of variable importances, to get an insight into the structure of the importances values and potentially disect the signal from the noise.

  3. The main analysis can then be performed with runGenphen using a subset (based on their importance) of SNPs

Value

site

id of the site (e.g. position in the provided sequence alignment)

importance

Magnitude of importance (impurity) of the site, estimated with random forest implemented in R package ranger

Author(s)

Simo Kitanovski <simo.kitanovski@uni-due.de>

See Also

runGenphen, runPhyloBiasCheck

Examples

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
# genotypes:
data(genotype.saap)
# quantitative phenotype:
data(phenotype.saap)

# run diagnostics
diag <- runDiagnostics(genotype = genotype.saap,
                       phenotype = phenotype.saap,
                       phenotype.type = "Q",
                       rf.trees = 5000)

snaketron/genphen documentation built on Aug. 2, 2019, 2:07 p.m.