SNP.CNV: CNV Analysis Pipeline for SNP array Data
In saasCNV: Somatic Copy Number Alteration Analysis Using Sequencing and SNP Array Data

Description Usage Arguments Details Value Author(s) References See Also Examples

All analysis steps are integrate into a pipeline. The results, including visualization plots are placed in a directory as specified by user.

SNP.CNV(snp, output.dir, sample.id, 
    do.GC.adjust = FALSE,
    gc.file = system.file("extdata","GC_1kb_hg19.txt.gz",package="saasCNV"),
    min.chr.probe = 100, min.snps = 10, 
    joint.segmentation.pvalue.cutoff = 1e-04, max.chpts = 30, 
    do.merge = TRUE, use.null.data = TRUE, 
    num.perm = 1000, maxL = NULL, 
    merge.pvalue.cutoff = 0.05, 
    do.cnvcall.on.merge = TRUE, 
    cnvcall.pvalue.cutoff = 0.05, 
    do.boundary.refine = FALSE, 
    do.plot = TRUE, cex = 0.3, 
    ref.num.probe = NULL, 
    do.gene.anno = FALSE, 
    gene.anno.file = NULL, 
    seed = NULL, verbose = TRUE)

`snp`	a data frame constructed from a text file with LRR and BAF information.
`output.dir`	the directory to which all the results will be located.
`sample.id`	sample ID to be displayed in the data frame of the results and the title of some diagnosis plots.
`do.GC.adjust`	logical. If GC content adjustment on `log2ratio` to be carried out. Default is `FALSE`. See `GC.adjust` for details.
`gc.file`	the location of tab-delimit file with GC content (averaged per 1kb window) information. See `GC.adjust` for details.
`min.chr.probe`	the minimum number of probes tagging a chromosome for it to be passed to the subsequent analysis.
`min.snps`	the minimum number of probes a segment needs to span.
`joint.segmentation.pvalue.cutoff`	the p-value cut-off one (or a pair) of change points to be determined as significant in each cycle of joint segmentation.
`max.chpts`	the maximum number of change points to be detected for each chromosome.
`do.merge`	logical. If segments merging step to be carried out. Default is `TRUE`.
`use.null.data`	logical. If only data for probes located in normal copy segments to be used for bootstrapping. Default is `TRUE`. If a more aggressive merging is needed, it can be switched to `FALSE`.
`num.perm`	the number of replicates drawn by bootstrap.
`maxL`	integer. The maximum length in terms of number of probes a bootstrapped segment may span. Default is `NULL`. If `NULL`, It will be automatically specified as 1/100 of the number of data points.
`merge.pvalue.cutoff`	a p-value cut-off for merging. If the empirical p-value is greater than the cut-off value, the two adjacent segments under consideration will be merged.
`do.cnvcall.on.merge`	logical. If CNV call to be done for the segments after merging step. Default is `TRUE`. If `TRUE`, CNV call will be done on the segments resulting directly from joint segmentation without merging step.
`cnvcall.pvalue.cutoff`	a p-value cut-off for CNV calling.
`do.boundary.refine`	logical. If the segment boundaries based on the grid of heterozygous probes to be refined by all probes with LRR data. Default is `FALSE`. We do not recommend to perform this step except in the case that the segment boundaries need to be aligned well on the same grid of probes for downstream analysis.
`do.plot`	logical. If diagnosis plots to be output. Default is `TRUE`.
`cex`	a numerical value giving the amount by which plotting text and symbols should be magnified relative to the default. It can be adjusted in order to make the plot legible.
`ref.num.probe`	integer. The reference number of probes against which a segment is compared in order to determine the cex of the segment to be displayed. Default is `NULL`. If `NULL`, It will be automatically specified as 1/100 of the number of data points.
`do.gene.anno`	logical. If gene annotation step to be performed. Default is `FALSE`.
`gene.anno.file`	a tab-delimited file containing gene annotation information. For example, RefSeq annotation file which can be found at UCSC genome browser.
`seed`	integer. Random seed can be set for reproducibility of results.
`verbose`	logical. If more details to be output. Default is `TRUE`.

See the vignettes of the package for more details.

The results, including visualization plots are placed in subdirectories of the output directory output.dir as specified by user.

Zhongyang Zhang <zhongyang.zhang@mssm.edu>

Zhongyang Zhang and Ke Hao. (2015) SAAS-CNV: A Joint Segmentation Approach on Aggregated and Allele Specific Signals for the Identification of Somatic Copy Number Alterations with Next-Generation Sequencing Data. PLoS Computational Biology, 11(11):e1004618.

NGS.CNV, snp.cnv.data, joint.segmentation, merging.segments cnv.call, diagnosis.seg.plot.chr, genome.wide.plot, diagnosis.cluster.plot, snp.refine.boundary

## Not run: 
## the pipeline for SNP array analysis
## download snp_table.txt.gz
url <- "https://zhangz05.u.hpc.mssm.edu/saasCNV/data/snp_table.txt.gz"
tryCatch({download.file(url=url, destfile="snp_table.txt.gz")
         }, error = function(e) {
          download.file(url=url, destfile="snp_table.txt.gz", method="curl")
         })
## If download.file fails to download the data, please manually download it from the url.

snp_table <- read.delim(file="snp_table.txt.gz", as.is=TRUE)

## download refGene_hg19.txt.gz
url <- "https://zhangz05.u.hpc.mssm.edu/saasCNV/data/refGene_hg19.txt.gz"
tryCatch({download.file(url=url, destfile="refGene_hg19.txt.gz")
         }, error = function(e) {
          download.file(url=url, destfile="refGene_hg19.txt.gz", method="curl")
         })
## If download.file fails to download the data, please manually download it from the url.

sample.id <- "SNP_0116"
output.dir <- file.path(getwd(), "test_saasCNV")
	
SNP.CNV(snp=snp_table, output.dir=output.dir, sample.id=sample.id,
        min.chr.probe=100,
        min.snps=10,
        joint.segmentation.pvalue.cutoff=1e-4,
        max.chpts=30,
        do.merge=TRUE, use.null.data=TRUE, num.perm=1000, maxL=5000, 
        merge.pvalue.cutoff=0.05,
        do.cnvcall.on.merge=TRUE, 
        cnvcall.pvalue.cutoff=0.05,
        do.boundary.refine=TRUE,
        do.plot=TRUE, cex=0.3, ref.num.probe=5000,
        do.gene.anno=TRUE,
        gene.anno.file="refGene_hg19.txt.gz",
        seed=123456789,
        verbose=TRUE)

## End(Not run)