Description Usage Arguments Value Author(s) See Also Examples
Predicting enhancer activities of query regions based on the enhancer
model from reptile_train
in training step. This function
calculates the enhancer scores of DMRs and query regions. It does not
try to generate combined enhancer scores.
1 2 3 4 | reptile_predict_genome_wide(reptile_model,
epimark_region,
epimark_DMR = NULL,
family = "randomForest")
|
reptile_model |
Enhancer model from |
epimark_region |
data.frame instance from read_epigenomic_data, which containing intensity and intensity deviation values of each mark for each query region |
epimark_DMR |
data.frame instance from read_epigenomic_data, which containing intensity and intensity deviation values of each mark for each DMR |
family |
classifier family used in the enhancer model Default: RandomForest Classifiers available: - RandomForest: random forest - Logistic: logistic regression |
A list containing two vectors
R |
Enhancer score of each query region |
DMR |
Enhancer score of each DMR |
Yupeng He yupeng.he.bioinfo@gmail.com
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 | 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=50)
## Prediction
## - REPTILE
pred <- reptile_predict(rsd_model,
rsd$test_data$region_epimark,
rsd$test_data$DMR_epimark)
## - Random guessing
pred_guess = runif(length(pred$D))
names(pred_guess) = names(pred$D)
## Evaluation
res_reptile <- reptile_eval_prediction(pred$D,
rsd$test_data$region_label)
res_guess <- reptile_eval_prediction(pred_guess,
rsd$test_data$region_label)
## - Print AUROC and AUPR
cat(paste0("REPTILE\n",
" AUROC = ",round(res_reptile$AUROC,digit=3),
"\n",
" AUPR = ",round(res_reptile$AUPR,digit=3))
,"\n")
cat(paste0("Random guessing\n",
" AUROC = ",round(res_guess$AUROC,digit=3),
"\n",
" AUPR = ",round(res_guess$AUPR,digit=3))
,"\n")
|
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