biHMM.findCNVs: Find copy number variations (bivariate)

In ataudt/aneufinder: Analysis of Copy Number Variation in Single-Cell-Sequencing Data

Find copy number variations (bivariate)

Description

biHMM.findCNVs finds CNVs using read count information from both strands.

Usage

biHMM.findCNVs(binned.data, ID = NULL, eps = 0.01, init = "standard",
  max.time = -1, max.iter = -1, num.trials = 1, eps.try = NULL,
  num.threads = 1, count.cutoff.quantile = 0.999,
  states = c("zero-inflation", paste0(0:10, "-somy")),
  most.frequent.state = "1-somy", algorithm = "EM", initial.params = NULL,
  verbosity = 1)

Arguments

binned.data	A GRanges-class object with binned read counts. Alternatively a GRangesList object with offsetted read counts.
ID	An identifier that will be used to identify this sample in various downstream functions. Could be the file name of the binned.data for example.
eps	method-HMM: Convergence threshold for the Baum-Welch algorithm.
init	method-HMM: One of the following initialization procedures: standard The negative binomial of state '2-somy' will be initialized with mean=mean(counts), var=var(counts). This procedure usually gives good convergence. random Mean and variance of the negative binomial of state '2-somy' will be initialized with random values (in certain boundaries, see source code). Try this if the standard procedure fails to produce a good fit.
max.time	method-HMM: The maximum running time in seconds for the Baum-Welch algorithm. If this time is reached, the Baum-Welch will terminate after the current iteration finishes. Set max.time = -1 for no limit.
max.iter	method-HMM: The maximum number of iterations for the Baum-Welch algorithm. Set max.iter = -1 for no limit.
num.trials	method-HMM: The number of trials to find a fit where state most.frequent.state is most frequent. Each time, the HMM is seeded with different random initial values.
eps.try	method-HMM: If code num.trials is set to greater than 1, eps.try is used for the trial runs. If unset, eps is used.
num.threads	method-HMM: Number of threads to use. Setting this to >1 may give increased performance.
count.cutoff.quantile	method-HMM: A quantile between 0 and 1. Should be near 1. Read counts above this quantile will be set to the read count specified by this quantile. Filtering very high read counts increases the performance of the Baum-Welch fitting procedure. However, if your data contains very few peaks they might be filtered out. Set count.cutoff.quantile=1 in this case.
states	method-HMM: A subset or all of c("zero-inflation","0-somy","1-somy","2-somy","3-somy","4-somy",...). This vector defines the states that are used in the Hidden Markov Model. The order of the entries must not be changed.
most.frequent.state	method-HMM: One of the states that were given in states. The specified state is assumed to be the most frequent one. This can help the fitting procedure to converge into the correct fit.
algorithm	method-HMM: One of c('baumWelch','EM'). The expectation maximization ('EM') will find the most likely states and fit the best parameters to the data, the 'baumWelch' will find the most likely states using the initial parameters.
initial.params	method-HMM: A aneuHMM object or file containing such an object from which initial starting parameters will be extracted.
verbosity	method-HMM: Integer specifying the verbosity of printed messages.

Value

An aneuBiHMM object.

ataudt/aneufinder documentation built on April 18, 2023, 4:20 a.m.