bivariate.findCNVs: Find copy number variations (bivariate)

Description Usage Arguments Value

Description

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

Usage

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bivariate.findCNVs(binned.data, ID = NULL, eps = 0.1, 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)

Arguments

binned.data

A GRanges object with binned 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

Convergence threshold for the Baum-Welch algorithm.

init

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

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

The maximum number of iterations for the Baum-Welch algorithm. Set max.iter = -1 for no limit.

num.trials

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

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

Number of threads to use. Setting this to >1 may give increased performance.

count.cutoff.quantile

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

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

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

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

A aneuHMM object or file containing such an object from which initial starting parameters will be extracted.

Value

An aneuBiHMM object.



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