findCNVs.strandseq {AneuFinder} | R Documentation |
findCNVs.strandseq
classifies the binned read counts into several states which represent copy-numbers on each strand.
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("zero-inflation", paste0(0:10, "-somy")), most.frequent.state = "1-somy", method = "edivisive", algorithm = "EM", initial.params = NULL)
binned.data |
A GRanges-class 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 |
method-edivisive: The maximum number of random permutations to use in each iteration of the permutation test (see |
sig.lvl |
method-edivisive: The level at which to sequentially test if a proposed change point is statistically significant (see |
eps |
method-HMM: Convergence threshold for the Baum-Welch algorithm. |
init |
method-HMM: One of the following initialization procedures:
|
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.iter |
method-HMM: The maximum number of iterations for the Baum-Welch algorithm. Set |
num.trials |
method-HMM: The number of trials to find a fit where state |
eps.try |
method-HMM: If code num.trials is set to greater than 1, |
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 |
strand |
Find copy-numbers only for the specified strand. One of |
states |
method-HMM: A subset or all of |
most.frequent.state |
method-HMM: One of the states that were given in |
method |
Any combination of |
algorithm |
method-HMM: One of |
initial.params |
method-HMM: A |
An aneuBiHMM
object.
Aaron Taudt
## Get an example BED file with single-cell-sequencing 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 copy-numbers model <- findCNVs.strandseq(binned[[1]]) ## Check the fit plot(model, type='histogram') plot(model, type='profile')