reptile_train: Learn a REPTILE enhancer model

In REPTILE: Regulatory DNA Element Prediction

Description

Learn a REPTILE enhancer model based on epigenomic signature of known enhancers.

Usage

reptile_train(epimark_region, label_region,
              epimark_DMR = NULL, label_DMR = NULL,
              family = "randomForest", ntree = 2000,
              nodesize = 1)

Arguments

epimark_region	data.frame instance from read_epigenomic_data, which containing intensity and intensity deviation values of each mark for each query region.
label_region	factor instance from read_label, containing the label of each query region. The possible values and their meanings of a label are: 0 (not enhancer), 1 (enhancer) and NA (unknwon and it will be ignored).
epimark_DMR	data.frame instance from read_epigenomic_data, which containing intensity and intensity deviation values of each mark for each DMR. If either this value or label_DMR is NULL, the output enhancer model will not inlclude a classifier for predicting the enhancer activities of DMRs. Default: NULL
label_DMR	factor instance from read_label, containing the label of each DMR. The possible values and their meanings of a label are: 0 (not enhancer), 1 (enhancer) and NA (unknwon and it will be ignored). If either this value or label_DMR is NULL, the output enhancer model will not inlclude a classifier for predicting the enhancer activities of DMRs. Default: NULL
family	classifier family used in the enhancer model Default: RandomForest Classifiers available: - RandomForest: random forest - Logistic: logistic regression
ntree	Number of tree to be constructed in the random forest model. See the function randomForest() in "randomForest" package for more information. Default: 2000
nodesize	Minimum size of terminal nodes. See the function randomForest() in "randomForest" package for more information. Default: 1

Value

A list containing two objects of class randomForest.

D	Classifier for DMRs. It is an randomForest object or glm object when family is set to be "Logistic".
R	Classifier for query regions. It is an randomForest object or glm object when family is set to be "Logistic".

Author(s)

Yupeng He [e-mail]

References

Breiman, L. (2001), Random Forests, Machine Learning 45(1), 5-32.

A. Liaw and M. Wiener (2002), Classification and Regression by randomForest, R News 2(3), 18–22.

See Also

read_epigenomic_data, read_label, reptile_predict

Examples

library("REPTILE")
data("rsd")

## Training
rsd_model <- reptile_train(rsd$training_data$region_epimark,
                           rsd$training_data$region_label,
                           rsd$training_data$DMR_epimark,
                           rsd$training_data$DMR_label,
                           ntree=5)

print(rsd_model$D)
print(rsd_model$R)

REPTILE documentation built on May 2, 2019, 5:06 a.m.