Description Usage Arguments Value Author(s) References See Also Examples
Learn a REPTILE enhancer model based on epigenomic signature of known enhancers.
1 2 3 4 | reptile_train(epimark_region, label_region,
epimark_DMR = NULL, label_DMR = NULL,
family = "randomForest", ntree = 2000,
nodesize = 1)
|
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 |
A list containing two objects of class randomForest
.
D |
Classifier for DMRs. It is an |
R |
Classifier for query regions. It is an |
Yupeng He yupeng.he.bioinfo@gmail.com
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.
read_epigenomic_data
,
read_label
,
reptile_predict
1 2 3 4 5 6 7 8 9 10 11 12 |
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