hmmratac: hmmratac
In macs3-project/MACSr: MACS: Model-based Analysis for ChIP-Seq

hmmratac

R Documentation

hmmratac

Description

Dedicated peak calling based on Hidden Markov Model for ATAC-seq data.

Usage

hmmratac(
  bam,
  outdir = ".",
  name = "NA",
  verbose = 2L,
  log = TRUE,
  cutoff_analysis_only = FALSE,
  em_skip = FALSE,
  em_means = list(50, 200, 400, 600),
  em_stddevs = list(20, 20, 20, 20),
  min_frag_p = 0.001,
  hmm_binsize = 10L,
  hmm_lower = 10L,
  hmm_upper = 20L,
  hmm_maxTrain = 1000,
  hmm_training_flanking = 1000,
  hmm_file = NULL,
  hmm_training_regions = NULL,
  hmm_randomSeed = 10151,
  hmm_modelonly = FALSE,
  prescan_cutoff = 1.2,
  openregion_minlen = 100,
  pileup_short = FALSE,
  keepduplicates = FALSE,
  blacklist = NULL,
  save_digested = FALSE,
  save_likelihoods = FALSE,
  save_states = FALSE,
  save_train = FALSE,
  decoding_steps = 1000,
  buffer_size = 1e+05,
  ...
)

Arguments

`bam`	Sorted BAM files containing the ATAC-seq reads. If multiple files are given as '-t A B C', then they will all be read and pooled together. REQUIRED.
`outdir`	If specified all output files will be written to that directory. Default: the current working directory
`name`	Name for this experiment, which will be used as a prefix to generate output file names. DEFAULT: "NA"
`verbose`	Set verbose level of runtime message. 0: only show critical message, 1: show additional warning message, 2: show process information, 3: show debug messages. DEFAULT:2
`log`	Whether to capture logs.
`cutoff_analysis_only`	Only run the cutoff analysis and output a report. After generating the report, the process will stop. The report will help user decide the three crucial parameters for `-l`, `-u`, and `-c`. So it's highly recommanded to run this first! Please read the report and instructions in `⁠Choices of cutoff values⁠` on how to decide the three crucial parameters.
`em_skip`	Do not perform EM training on the fragment distribution. If set, EM_MEANS and EM.STDDEVS will be used instead. Default: False
`em_means`	Comma separated list of initial mean values for the fragment distribution for short fragments, mono-, di-, and tri-nucleosomal fragments. Default: 50 200 400 600
`em_stddevs`	Comma separated list of initial standard deviation values for fragment distribution for short fragments, mono-, di-, and tri-nucleosomal fragments. Default: 20 20 20 20
`min_frag_p`	We will exclude the abnormal fragments that can't be assigned to any of the four signal tracks. After we use EM to find the means and stddevs of the four distributions, we will calculate the likelihood that a given fragment length fit any of the four using normal distribution. The criteria we will use is that if a fragment length has less than MIN_FRAG_P probability to be like either of short, mono, di, or tri-nuc fragment, we will exclude it while generating the four signal tracks for later HMM training and prediction. The value should be between 0 and 1. Larger the value, more abnormal fragments will be allowed. So if you want to include more 'ideal' fragments, make this value smaller. Default = 0.001
`hmm_binsize`	Size of the bins to split the pileup signals for training and decoding with Hidden Markov Model. Must >= 1. Smaller the binsize, higher the resolution of the results, slower the process. Default = 10
`hmm_lower`	Upper limit on fold change range for choosing training sites. Default: 20
`hmm_upper`	Lower limit on fold change range for choosing training sites. Default: 10
`hmm_maxTrain`	Maximum number of training regions to use. Default: 1000
`hmm_training_flanking`	Training regions will be expanded to both side with this number of basepairs. The purpose is to include more background regions. Default: 1000
`hmm_file`	A JSON file generated from previous HMMRATAC run to use instead of creating new one. When provided, HMM training will be skipped. Default: NA
`hmm_training_regions`	Filename of training regions (previously was BED_file) to use for training HMM, instead of using foldchange settings to select. Default: NA
`hmm_randomSeed`	Seed to set for random sampling of training regions. Default: 10151
`hmm_modelonly`	Stop the program after generating model. Use this option to generate HMM model ONLY, which can be later applied with `--model`. Default: False
`prescan_cutoff`	The fold change cutoff for prescanning candidate regions in the whole dataset. Then we will use HMM to predict states on these candidate regions. Higher the prescan cutoff, fewer regions will be considered. Must > 1. Default: 1.2
`openregion_minlen`	Minimum length of open region to call accessible regions. Must be larger than 0. If it is set as 0, it means no filtering on the length of the open regions called. Please note that, when bin size is small, setting a too small OPENREGION_MINLEN will bring a lot of false positives. Default: 100
`pileup_short`	By default, HMMRATAC will pileup all fragments in order to identify regions for training and candidate regions for decoding. When this option is on, it will pileup only the short fragments to do so. Although it sounds a good idea since we assume that open region should have a lot of short fragments, it may be possible that the overall short fragments are too few to be useful. Default: False
`keepduplicates`	Keep duplicate reads from analysis. By default, duplicate reads will be removed. Default: False
`blacklist`	Filename of blacklisted regions to exclude (previously was BED_file). Examples are those from ENCODE. Default: NA
`save_digested`	Save the digested ATAC signals of short-, mono-, di-, and tri- signals in three BedGraph files with the names NAME_short.bdg, NAME_mono.bdg, NAME_di.bdg, and NAME_tri.bdg. DEFAULT: False
`save_likelihoods`	Save the likelihoods to each state annotation in three BedGraph files, named with NAME_open.bdg for open states, NAME_nuc.bdg for nucleosomal states, and NAME_bg.bdg for the background states. DEFAULT: False
`save_states`	Save all open and nucleosomal state annotations into a BED file with the name NAME_states.bed. DEFAULT: False
`save_train`	Save the training regions and training data into NAME_training_regions.bed and NAME_training_data.txt. Default: False
`decoding_steps`	Number of candidate regions to be decoded at a time. The HMM model will be applied with Viterbi to find the optimal state path in each region. bigger the number, 'possibly' faster the decoding process, 'definitely' larger the memory usage. Default: 1000.
`buffer_size`	Buffer size for incrementally increasing internal array size to store reads alignment information. In most cases, you don't have to change this parameter. However, if there are large number of chromosomes/contigs/scaffolds in your alignment, it's recommended to specify a smaller buffer size in order to decrease memory usage (but it will take longer time to read alignment files). Minimum memory requested for reading an alignment file is about # of CHROMOSOME * BUFFER_SIZE * 8 Bytes. DEFAULT: 100000
`...`	More options for macs2.