reptile_predict {REPTILE}

Predicting enhancer activity of given regions

Description

Predicting enhancer activities of query regions based on the enhancer model from reptile_train in training step. This function calculates the combined enhancer score for each query region (given region) as the maximum among the score of whole query region and the scores of DMRs within it. This function is for generating genome-wide enhancer predictions.

Usage

reptile_predict(reptile_model,
                epimark_region,
                epimark_DMR = NULL,
                family = "randomForest")

Arguments

reptile_model

Enhancer model from reptile_train. It is a list containing two objects of class randomForest or glm when family is set to be "Logistic"

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

Value

A list containing three vectors

D

Combined enhancer score of each query region

R

Enhancer score of each query region

DMR

Enhancer score of each DMR

Author(s)

Yupeng He [e-mail]

See Also

reptile_predict_genome_wide

reptile_train

read_epigenomic_data

read_label

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=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")