Description Usage Arguments Value
R codes based on Rohan Fernando and Dorian Garrick, Bayesian Methods Applied to GWAS. All rights and credits go to Rohan Fernando and Dorian Garrick. The code is not efficient for large scale genomic data.
1 2 3 4 5 6 7 8 9 | BayesCpi(genofile = "data/bayes_geno.txt",
train_pheno = "data/bayes_train_pheno.txt", test_pheno = NULL,
mergeby = "uid", trait = "TA", seed = 12347, chainLength = 1000,
probFixed = 0.999, estimatePi = "yes", dfEffectVar = 4, nuRes = 4,
varGenotypic = 1, varResidual = 1, windowSize = 10,
outputFrequency = 100)
read_geno_pheno(genofile = "data/bayes_geno.txt",
train_pheno = "data/bayes_train_pheno.txt", test_pheno = NULL, mergeby)
|
genofile |
A data.frame contains snpid information, must contain col: mergeby. [data.frame, ["uid", ...]]. |
train_pheno |
A training data.frame contains columns of phenotypic traits and fixed effects. Can be used for GWAS. [data.frame, ["uid", "pheno", ...]]. |
test_pheno |
A testing data.frame contains columns of phenotypic traits and fixed effects. For GS. [data.frame, ["uid", "pheno", ...], =NULL]. |
mergeby |
The column id to merge the pheno and geno data. [chr, ="uid"] |
seed |
Seed for the random number generator. [integer, =12347]. |
chainLength |
Number of iterations for the MCMC chain. [interger, =11000]. |
probFixed |
Parameter "pi" the probability SNP effect is zero [num, =.99]. |
estimatePi |
Wether to estimate pi or not. [chr, ="yes"]. |
dfEffectVar |
Hyper parameter (degrees of freedom) for locus effect variance. [integer, =4]. |
nuRes |
Hyper parameter (degrees of freedom) for residual variance. [integer, =4]. |
varGenotypic |
Used to derive hyper parameter (scale) for locus effect variance. [num, =1]. |
varResidual |
Used to derive hyper parameter (scale) for residual variance. [num, =1]. |
windowSize |
Number of consecutive markers in a genomic window. [integer, =10]. |
outputFrequency |
Frequency for reporting performance and for computing genetic variances. [integer, =100] |
return GWAS results.
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