############################################################################## ############################################################################## ### ### Running command: ### ### /Library/Frameworks/R.framework/Resources/bin/R CMD build --keep-empty-dirs --no-resave-data AneuFinder ### ############################################################################## ############################################################################## * 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 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