HMMRATAC: Perform HMMRATAC

In Bioconductor/HMMRATAC: HMMRATAC - a semi-supervised machine learning approach for identifying open chromatin regions from ATAC-Seq data using Hidden Markov Models

Description

Perform HMMRATAC

Usage

MMRATAC <- function(bam,
                     index,
                     genome,
                     means = c(50, 200, 400, 600),
                     stddev = c(20, 20, 20, 20),
                     fragem = TRUE,
                     minmapq = 30,
                     upper = 20,
                     lower = 10,
                     zscore = 100,
                     zscoreprescan = 0,
                     output_dir = tempdir(),
                     blacklist = tempfile(fileext=".bed"),
                     peaks = TRUE,
                     kmeans = 3,
                     training,
                     bedgraph = FALSE,
                     minlen = 200,
                     score = c("max", "ave", "med", "fc", "zscore", "all"),
                     bgscore = FALSE,
                     trim = 0,
                     window = 25000000,
                     model,
                     modelonly = FALSE)

Arguments

bam	Sorted BAM file containing the ATAC-seq reads.
index	Index file for the sorted BAM File.
genome	Two column, tab delimited file containing genome size stats.
means	numeric vector of initial mean values for the fragment distribution. Default = c(50,200,400,600).
stddev	numeric vector of initial standard deviation values for fragment distribution. Default = c(20,20,20,20).
fragem	logical(1) of whether to perform EM training on the fragment distribution. Default = TRUE.
minmapq	numeric(1) of ,inimum mapping quality of reads to keep. Default = 30.
upper	numeric(1) of Upper limit on fold change range for choosing training sites. Default = 20.
lower	numeric(1) of lower limit on fold change range for choosing training sites. Default = 10.
zscore	numeric(1) of Zscored read depth to mask during Viterbi decoding. Default = 100.
zscoreprescan	numeric(1) of Minimum zscored read depth to be included in Viterbi decoding. Default = 0.
output	character(1) base name (including directory path) of output files. Default: file.path(tempfile(), basename(bam)).
blacklist	character(1) of name of bed file of blacklisted regions to exclude.
peaks	logical(1) of whether to report peaks in bed format. Default = TRUE.
kmeans	numeric(1) of number of states in model. Default = 3. If not k=3, recommend NOT calling peaks, use bedgraph.
training	character(1) of name of BED file of training regions to use for training model, instead of foldchange settings.
bedgraph	logical(1) Whether to report whole genome bedgraph of all state anntations. Default = FALSE.
minlen	numeric(1) of minimum length of open region to call peak. Note: peaks must be set. Default = 200.
score	character(1) either "max", "ave", "med", "fc", "zscore", "all". What type of score system to use for peaks. Can be used for ranking peaks. Default = max.
bgscore	logical(1) of whether to add the HMMR score to each state annotation in bedgraph. Note: this adds considerable time. Default = FALSE.
trim	numeric(1) of how many signals from the end to trim off (ie starting with tri signal then di etc). This may be useful if your data doesn't contain many large fragments. Default = 0.
window	numeric(1) of size of the bins to split the genome into for Viterbi decoding. To save memory, the genome is split into <int> long bins and viterbi decoding occurs across each bin. Default = 25000000. Note: For machines with limited memory, it is recomended to reduce the size of the bins.
model	character(1) of the name of a binary model file (generated from previous HMMR run) to use instead of creating new one.
modelonly	logical(1) of whether or not to stop the program after generating model. Default = false.
overwrite	logical(1) overwrite existing 'output' files?
verbose	logical(1) report progress?

Value

matrix A matrix(?)

Examples

example_data <- system.file(package="HMMRATACData", "data")
example_files <- dir(example_data, recursive = TRUE, full = TRUE)
basename(example_files)

output_directory <- tempfile()
dir.create(output_directory)
output <- file.path(
    output_directory,
    sub(".bam$", "", basename(example_files)[[1]])
)
output

outputs <- HMMRATAC(
   bam = example_files[[1]],
   index = example_files[[2]],
   genome = example_files[[3]],
   output = output,
   window = 2500000
)
outputs

Bioconductor/HMMRATAC documentation built on July 31, 2020, 3:07 p.m.