preciseTAD {preciseTAD} | R Documentation

## Precise TAD boundary prediction at base-level resolution using density-based spatial clustering and partitioning techniques

### Description

Precise TAD boundary prediction at base-level resolution using density-based spatial clustering and partitioning techniques

### Usage

    preciseTAD(
      genomicElements.GR,
      featureType = "distance",
      CHR,
      chromCoords = NULL,
      tadModel,
      threshold = 1,
      verbose = TRUE,
      parallel = NULL,
      DBSCAN_params,
      flank,
      BaseProbs = FALSE
    )

### Arguments

`genomicElements.GR` | `GRangesList` object containing GRanges from each ChIP-seq BED file that was used to train a predictive model (can be obtained using the `bedToGRangesList`). Required.
`featureType` | Controls how the feature space is constructed (one of either "binary", "oc", "op", "signal, or "distance" (log2- transformed). Default is "distance".
`CHR` | Controls which chromosome to predict boundaries on at base-level resolution. Required.
`chromCoords` | List containing the starting bp coordinate and ending bp coordinate that defines the region of the linear genome to make predictions on. If chromCoords is not specified, then predictions will be made on the entire chromosome. Default is NULL.
`tadModel` | Model object used to obtain predicted probabilities at base-level resolution (examples include `gbm`, `glmnet`, `svm`, `glm`, etc). For a random forest model, can be obtained using `preciseTAD::randomForest`). Required.
`threshold` | Bases with predicted probabilities that are greater than or equal to this value are labeled as potential TAD boundaries. Values in the range of .95-1.0 are suggested. Default is 1. If a vector of different values is passed to the threshold paramenter then a grid is provided with eps (see DBSCAN_params parameter) threshold values and the optimal combination is chose as the combination that maximizes normalized enrichment – calculated as the number of peak regions that overlap with flanked predicted boundaries divided by the total number of predicted boundaries, averaged over all predictors (genomic elements).
`verbose` | Option to print progress. Default is TRUE.
`parallel` | Option to parallelise the process for obtaining predicted probabilities. Must be number to indicate the number of cores to use in parallel. Default is NULL.
`DBSCAN_params` | Parameters passed to `dbscan` in list form containing 1) eps and 2) MinPts. If a vector of different values is passed to eps then a grid is provided with the probability threshold (see threshold parameter) values and the optimal combination is chose as the combination that maximizes normalized enrichment – calculated as the number of peak regions that overlap with flanked predicted boundaries divided by the total number of predicted boundaries, averaged over all predictors (genomic elements). Required.
`flank` | Controls how much to flank the predicted TAD boundaries for calculating normalized enrichment. Required.
`BaseProbs` | Option to include the vector of probabilities for each base-level coordinate. Recommended to be used only when chromCoords is specified.

### Value

A list containing 4 elements including: 1) data frame with average (and standard deviation) normalized enrichment (NE) values for each combination of t and eps (only if multiple values are provided for at least paramenter; all subsequent summaries are applied to optimal combination of (t, eps)), 2) the genomic coordinates spanning each preciseTAD predicted region (PTBR), 3) the genomic coordinates of preciseTAD predicted boundaries points (PTBP), 4) a named list including summary statistics of the following: PTBRWidth - PTBR width, PTBRCoverage - the proportion of bases within a PTBR with probabilities that equal to or exceed the threshold (t=1 by default), DistanceBetweenPTBR - the genomic distance between the end of the previous PTBR and the start of the subsequent PTBR, NumSubRegions - the number of the subregions in each PTBR cluster, SubRegionWidth - the width of the subregion forming each PTBR, DistBetweenSubRegions - the genomic distance between the end of the previous PTBR-specific subregion and the start of the subsequent PTBR-specific subregion, NormilizedEnrichment - the normalized enrichment of the genomic annotations used in the model around flanked PTBPs, and BaseProbs - a numeric vector of probabilities for each corresponding base coordinate.

### Examples

    # 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 and filter to include only CTCF, RAD21,
    #SMC3, and ZNF143
    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 4 TFBS from
    # the GM12878 cell line, and random under-sampling
    set.seed(123)
    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
    set.seed(123)
    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 CHR22:17000000-18000000
    set.seed(123)
    pt <- preciseTAD(genomicElements.GR = tfbsList_filt,
                     featureType = "distance",
                     CHR = "CHR22",
                     chromCoords = list(17000000, 18000000),
                     tadModel = tadModel[[1]],
                     threshold = c(0.975, 0.99, 1.0),
                     verbose = TRUE,
                     parallel = NULL,
                     DBSCAN_params = list(c(5000, 10000,15000,20000,30000), 3),
                     flank = 5000)

[Package preciseTAD version 1.2.0 Index]