juicer_func {preciseTAD} | R Documentation |
Helper function for transforming a GRanges object into matrix form to be saved as .txt or .BED file and imported into juicer
juicer_func(grdat)
grdat |
A GRanges object representing boundary coordinates |
A dataframe that can be saved as a BED file to import into juicer
## Not run: # Read in ARROWHEAD-called TADs at 5kb data(arrowhead_gm12878_5kb) # Extract unique boundaries bounds.GR <- extractBoundaries(domains.mat = arrowhead_gm12878_5kb, preprocess = FALSE, CHR = c("CHR21", "CHR22"), resolution = 5000) # Read in GRangesList of 26 TFBS data(tfbsList) tfbsList_filt <- tfbsList[which(names(tfbsList) %in% c("Gm12878-Ctcf-Broad", "Gm12878-Rad21-Haib", "Gm12878-Smc3-Sydh", "Gm12878-Znf143-Sydh"))] # Create the binned data matrix for CHR1 (training) and CHR22 (testing) # using 5 kb binning, distance-type predictors from 26 different TFBS from # the GM12878 cell line, and random under-sampling tadData <- createTADdata(bounds.GR = bounds.GR, resolution = 5000, genomicElements.GR = tfbsList_filt, featureType = "distance", resampling = "rus", trainCHR = "CHR21", predictCHR = "CHR22") # Perform random forest using TADrandomForest by tuning mtry over 10 values # using 3-fold CV tadModel <- TADrandomForest(trainData = tadData[[1]], testData = tadData[[2]], tuneParams = list(mtry = 2, ntree = 500, nodesize = 1), cvFolds = 3, cvMetric = "Accuracy", verbose = TRUE, model = TRUE, importances = TRUE, impMeasure = "MDA", performances = TRUE) # Apply preciseTAD on a specific 2mb section of CHR22:17000000-19000000 pt <- preciseTAD(genomicElements.GR = tfbsList_filt, featureType = "distance", CHR = "CHR22", chromCoords = list(17000000, 19000000), tadModel = tadModel[[1]], threshold = 1.0, verbose = TRUE, parallel = TRUE, cores = 2, splits = 2, DBSCAN_params = list(10000, 3), flank = NULL) # Transform into juicer format juicer_func(pt[[2]]) ## End(Not run)