Description Usage Arguments Value Author(s) Examples
findCNVs.strandseq
classifies the binned read counts into several states which represent copynumbers on each strand.
1 2 3 4 5 6 7  findCNVs.strandseq(binned.data, ID = NULL, R = 10, sig.lvl = 0.1,
eps = 0.01, init = "standard", max.time = 1, max.iter = 1000,
num.trials = 5, eps.try = max(10 * eps, 1), num.threads = 1,
count.cutoff.quantile = 0.999, strand = "*",
states = c("zeroinflation", paste0(0:10, "somy")),
most.frequent.state = "1somy", method = "edivisive", algorithm = "EM",
initial.params = NULL)

binned.data 
A GRangesclass 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 
R 
methodedivisive: The maximum number of random permutations to use in each iteration of the permutation test (see 
sig.lvl 
methodedivisive: The level at which to sequentially test if a proposed change point is statistically significant (see 
eps 
methodHMM: Convergence threshold for the BaumWelch algorithm. 
init 
methodHMM: One of the following initialization procedures:

max.time 
methodHMM: The maximum running time in seconds for the BaumWelch algorithm. If this time is reached, the BaumWelch will terminate after the current iteration finishes. Set 
max.iter 
methodHMM: The maximum number of iterations for the BaumWelch algorithm. Set 
num.trials 
methodHMM: The number of trials to find a fit where state 
eps.try 
methodHMM: If code num.trials is set to greater than 1, 
num.threads 
methodHMM: Number of threads to use. Setting this to >1 may give increased performance. 
count.cutoff.quantile 
methodHMM: 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 BaumWelch fitting procedure. However, if your data contains very few peaks they might be filtered out. Set 
strand 
Find copynumbers only for the specified strand. One of 
states 
methodHMM: A subset or all of 
most.frequent.state 
methodHMM: One of the states that were given in 
method 
Any combination of 
algorithm 
methodHMM: One of 
initial.params 
methodHMM: A 
An aneuBiHMM
object.
Aaron Taudt
1 2 3 4 5 6 7 8 9 10  ## Get an example BED file with singlecellsequencing reads
bedfile < system.file("extdata", "KK150311_VI_07.bam.bed.gz", package="AneuFinderData")
## Bin the file into bin size 1Mp
binned < binReads(bedfile, assembly='mm10', binsize=1e6,
chromosomes=c(1:19,'X','Y'), pairedEndReads=TRUE)
## Find copynumbers
model < findCNVs.strandseq(binned[[1]])
## Check the fit
plot(model, type='histogram')
plot(model, type='profile')

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