Description Usage Arguments Value Examples
This function extracts regions of interest from data set on which you want to make predictions.
1 2 | extract_data(newexpression = NULL, newregiondata = NULL,
predictordata = NULL)
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newexpression |
expression data set from which to extract coverage data
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newregiondata |
GRanges object containing region info
for expression data |
predictordata |
object output from build_predictor |
covmat_test An n x m data.frame of the selected regions from the data set specified by 'expression'
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 | library('GenomicRanges')
library('dplyr')
## Make up some some region data
regions <- GRanges(seqnames = 'chr2', IRanges(
start = c(28971710:28971712, 29555081:29555083, 29754982:29754984),
end = c(29462417:29462419, 29923338:29923340, 29917714:29917716)))
## make up some expression data for 9 rows and 30 people
data(sysdata, package='phenopredict')
## includes R object 'cm'
exp= cm[1:length(regions),1:30]
## generate some phenotype information
sex = as.data.frame(rep(c("male","female"),each=15))
age = as.data.frame(sample(1:100,30))
pheno = dplyr::bind_cols(sex,age)
colnames(pheno) <- c("sex","age")
## select regions to be used to build the predictor
inputdata <- filter_regions(expression=exp, regiondata=regions,
phenodata=pheno, phenotype="sex",
covariates=NULL,type="factor", numRegions=2)
## build phenotype predictor
predictor<-build_predictor(inputdata=inputdata ,phenodata=pheno,
phenotype="sex", covariates=NULL,type="factor", numRegions=2)
## determine resubstitution error
## carry out prediction in training data set
predictions_test<-test_predictor(inputdata=inputdata ,phenodata=pheno,
phenotype="sex", covariates=NULL,type="factor",predictordata=predictor)
## generate new expressiondata set for prediction
exp_new= cm_new[1:length(regions),1:30]
## extract test data
test_data<-extract_data(newexpression=exp_new,
newregiondata=predictor$regiondata, predictordata=predictor)
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