from PReLIM import PReLIM
import numpy as np
# Note: This toy example does not contain enough data for the model
# to actually make imputations due to train/validation splits. More bins must be used.
# Step 1: Collect methylation matrices, 1 is methylated, 0 is unmethylated, -1 is unknown. Each column is a cpg site, each row is a read
bin1 = np.array([[1,0],[0,-1],[-1,1],[0,0]],dtype=float)
bin2 = np.array([[1,0],[1,0],[-1,1],[0,0],[0,1],[1,1],[0,0]],dtype=float)
bin3 = np.array([[-1,1],[0,-1],[-1,1],[0,0]],dtype=float)
# Put bins into a list
bins = [bin1, bin2, bin3]
# Step 2: Created a model with correnct density for given bins
model = PReLIM(cpgDensity=2)
# Step 3: Train the model and save it if you wish
model.train(bins, model_file="my_model_file")
# Step 4: Use model for imputating a single bin, or...
imputed_bin1 = model.impute(bin1)
# ... use batch imputation to impute many bins at once (recommended)
imputed_bins = model.impute_many(bins)