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### Running command:
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### /home/biocbuild/bbs-3.20-bioc/R/bin/R CMD build --keep-empty-dirs --no-resave-data --md5 AneuFinder
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* checking for file ‘AneuFinder/DESCRIPTION’ ... OK
* preparing ‘AneuFinder’:
* checking DESCRIPTION meta-information ... OK
* cleaning src
* installing the package to build vignettes
* creating vignettes ... ERROR
--- re-building ‘AneuFinder.Rnw’ using knitr
Aneufinder package:AneuFinder R Documentation
_W_r_a_p_p_e_r _f_u_n_c_t_i_o_n _f_o_r _t_h_e '_A_n_e_u_F_i_n_d_e_r' _p_a_c_k_a_g_e
_D_e_s_c_r_i_p_t_i_o_n:
This function is an easy-to-use wrapper to bin the data, find
copy-number-variations, locate breakpoints, plot genomewide
heatmaps, distributions, profiles and karyograms.
_U_s_a_g_e:
Aneufinder(inputfolder, outputfolder, configfile = NULL, numCPU = 1,
reuse.existing.files = TRUE, binsizes = 1e+06, stepsizes = binsizes,
variable.width.reference = NULL, reads.per.bin = NULL,
pairedEndReads = FALSE, assembly = NULL, chromosomes = NULL,
remove.duplicate.reads = TRUE, min.mapq = 10, blacklist = NULL,
use.bamsignals = FALSE, reads.store = FALSE, correction.method = NULL,
GC.BSgenome = NULL, method = c("edivisive"), strandseq = FALSE,
R = 10, sig.lvl = 0.1, eps = 0.01, max.time = 60, max.iter = 5000,
num.trials = 15, states = c("zero-inflation", paste0(0:10, "-somy")),
confint = NULL, refine.breakpoints = FALSE, hotspot.bandwidth = NULL,
hotspot.pval = 0.05, cluster.plots = TRUE)
_A_r_g_u_m_e_n_t_s:
inputfolder: Folder with either BAM or BED files.
outputfolder: Folder to output the results. If it does not exist it
will be created.
configfile: A file specifying the parameters of this function (without
'inputfolder', 'outputfolder' and 'configfile'). Having the
parameters in a file can be handy if many samples with the
same parameter settings are to be run. If a 'configfile' is
specified, it will take priority over the command line
parameters.
numCPU: The numbers of CPUs that are used. Should not be more than
available on your machine.
reuse.existing.files: A logical indicating whether or not existing
files in 'outputfolder' should be reused.
binsizes: An integer vector with bin sizes. If more than one value is
given, output files will be produced for each bin size.
stepsizes: A vector of step sizes the same length as 'binsizes'. Only
used for 'method="HMM"'.
variable.width.reference: A BAM file that is used as reference to
produce variable width bins. See 'variableWidthBins' for
details.
reads.per.bin: Approximate number of desired reads per bin. The bin
size will be selected accordingly. Output files are produced
for each value.
pairedEndReads: Set to 'TRUE' if you have paired-end reads in your BAM
files (not implemented for BED files).
assembly: Please see 'getChromInfoFromUCSC' for available assemblies.
Only necessary when importing BED files. BAM files are
handled automatically. Alternatively a data.frame with
columns 'chromosome' and 'length'.
chromosomes: If only a subset of the chromosomes should be imported,
specify them here.
remove.duplicate.reads: A logical indicating whether or not duplicate
reads should be removed.
min.mapq: Minimum mapping quality when importing from BAM files. Set
'min.mapq=NA' to keep all reads.
blacklist: A 'GRanges-class' or a bed(.gz) file with blacklisted
regions. Reads falling into those regions will be discarded.
use.bamsignals: If 'TRUE' the 'bamsignals' package will be used for
binning. This gives a tremendous performance increase for the
binning step. 'reads.store' and 'calc.complexity' will be set
to 'FALSE' in this case.
reads.store: Set 'reads.store=TRUE' to store read fragments as RData in
folder 'data' and as BED files in 'BROWSERFILES/data'. This
option will force 'use.bamsignals=FALSE'.
correction.method: Correction methods to be used for the binned read
counts. Currently only ''GC''.
GC.BSgenome: A 'BSgenome' object which contains the DNA sequence that
is used for the GC correction.
method: Any combination of 'c('HMM','dnacopy','edivisive')'. Option
'method='HMM'' uses a Hidden Markov Model as described in
doi:10.1186/s13059-016-0971-7 to call copy numbers. Option
''dnacopy'' uses 'segment' from the 'DNAcopy' package to call
copy numbers similarly to the method proposed in
doi:10.1038/nmeth.3578, which gives more robust but less
sensitive results compared to the HMM. Option ''edivisive''
(DEFAULT) works like option ''dnacopy'' but uses the
'e.divisive' function from the 'ecp' package for
segmentation.
strandseq: A logical indicating whether the data comes from Strand-seq
experiments. If 'TRUE', both strands carry information and
are treated separately.
R: method-edivisive: The maximum number of random permutations
to use in each iteration of the permutation test (see
'e.divisive'). Increase this value to increase accuracy on
the cost of speed.
sig.lvl: method-edivisive: The level at which to sequentially test if
a proposed change point is statistically significant (see
'e.divisive'). Increase this value to find more breakpoints.
eps: method-HMM: Convergence threshold for the Baum-Welch
algorithm.
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.
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.
confint: Desired confidence interval for breakpoints. Set
'confint=NULL' to disable confidence interval estimation.
Confidence interval estimation will force 'reads.store=TRUE'.
refine.breakpoints: A logical indicating whether breakpoints from the
HMM should be refined with read-level information.
'refine.breakpoints=TRUE' will force 'reads.store=TRUE'.
hotspot.bandwidth: A vector the same length as 'binsizes' with
bandwidths for breakpoint hotspot detection (see 'hotspotter'
for further details). If 'NULL', the bandwidth will be chosen
automatically as the average distance between reads.
hotspot.pval: P-value for breakpoint hotspot detection (see
'hotspotter' for further details). Set 'hotspot.pval = NULL'
to skip hotspot detection.
cluster.plots: A logical indicating whether plots should be clustered
by similarity.
_V_a_l_u_e:
'NULL'
_A_u_t_h_o_r(_s):
Aaron Taudt
_E_x_a_m_p_l_e_s:
## Not run:
## The following call produces plots and genome browser files for all BAM files in "my-data-folder"
Aneufinder(inputfolder="my-data-folder", outputfolder="my-output-folder")
## End(Not run)
Warning: ggrepel: 2 unlabeled data points (too many overlaps). Consider increasing max.overlaps
Error: processing vignette 'AneuFinder.Rnw' failed with diagnostics:
Running 'texi2dvi' on 'AneuFinder.tex' failed.
LaTeX errors:
! Package xcolor Error: Undefined color `fgcolor'.
See the xcolor package documentation for explanation.
Type H <return> for immediate help.
...
! Emergency stop.
...
l.61 ...}{rgb}{0.941, 0.941, 0.941}\color{fgcolor}
\begin{kframe}
! ==> Fatal error occurred, no output PDF file produced!
--- failed re-building ‘AneuFinder.Rnw’
SUMMARY: processing the following file failed:
‘AneuFinder.Rnw’
Error: Vignette re-building failed.
Execution halted
