createTADdata: Function to create a data matrix used for building a...

In preciseTAD: preciseTAD: A machine learning framework for precise TAD boundary prediction

Description

Function to create a data matrix used for building a predictive model to classify boundary regions from functional genomic elements

Usage

createTADdata(
  bounds.GR,
  resolution,
  genomicElements.GR,
  featureType = "distance",
  resampling,
  trainCHR,
  predictCHR = NULL
)

Arguments

bounds.GR	a GRanges object with chromosomal coordinates of TAD boundaries used to identify positive cases (can be obtained using extractBoundaries). Required.
resolution	Numeric, the width to bin the genome at, should match the resolution that TADs were called at. Required.
genomicElements.GR	a GRangesList object containing GRanges objects for each ChIP-seq data to leverage in the random forest model (can be obtained using the bedToGRangesList). Required.
featureType	Character, controls how the feature space is constructed (one of either "binary" (overlap yes/no), "oc" (overlap counts, the number of overlaps), "op" (overlap percent, the percent of bin width covered by the genomic annotation), or "distance" (log2-transformed distance from the center of the nearest genomic annotation to the center of the bin); default is "distance"). Required.
resampling	Character, controls if and how the data should be resampled to create balanced classes of boundary vs. nonboundary regions (one of either "none" - no re-sampling, "ros" - Random Over-Sampling, "rus" - Random Under-Sampling, or "smote" - Synthetic Minority Over-sampling TEchnique). Required.
trainCHR	Character vector of chromosomes to use to build the binned data matrix for training. Required.
predictCHR	Character vector of chromosomes to use to build the binned data matrix for testing. Default in NULL, indicating no test data is created. If trainCHR=predictCHR then a 7:3 split is created.

Value

A list object containing two data.frames: 1) the training data, 2) the test data (only if predictCHR is not NULL, otherwise it is NA). "y" is an indicator whether the corresponding bin is a TAD boundary, and the subsequent columns have the association measures between bins and the genomic annotations

Examples

# Create training data for CHR21 and testing data for CHR22 with
# 5 kb binning, oc-type predictors from 26 different transcription factor
# binding sites from the GM12878 cell line, and random under-sampling

# 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)

tadData <- createTADdata(bounds.GR = bounds.GR,
                         resolution = 5000,
                         genomicElements.GR = tfbsList,
                         featureType = "oc",
                         resampling = "rus",
                         trainCHR = "CHR21",
                         predictCHR = "CHR22")

preciseTAD documentation built on Nov. 8, 2020, 6:51 p.m.