iCNV_detection {iCNV} | R Documentation

## CNV detection

### Description

Copy number variation detection tool for germline data. Able to combine intensity and BAF from SNP array and NGS data.

### Usage

    iCNV_detection(ngs_plr = NULL, snp_lrr = NULL, ngs_baf = NULL,
      snp_baf = NULL, ngs_plr.pos = NULL, snp_lrr.pos = NULL,
      ngs_baf.pos = NULL, snp_baf.pos = NULL, maxIt = 50, visual = 0,
      projname = "iCNV.", CN = 0, mu = c(-3, 0, 2), cap = FALSE)

### Arguments

`ngs_plr` | A list of NGS intensity data. Each entry is an individual. If no NGS data, no need to specify.
`snp_lrr` | A list of SNP array intensity data. Each entry is an individual. If no SNP array data, no need to specify.
`ngs_baf` | A list of NGS BAF data. Each entry is an individual. If no NGS data, no need to specify.
`snp_baf` | A list of SNP array BAF data. Each entry is an individual. If no SNP array data, no need to specify.
`ngs_plr.pos` | A list of NGS intensity postion data. Each entry is an individual with dimension= (#of bins or exons, 2(start and end position)). If no NGS data, no need to specify.
`snp_lrr.pos` | A list of SNP array intensity postion data. Each entry is an individual with length=#of SNPs. If no SNP array data, no need to specify.
`ngs_baf.pos` | A list of NGS BAF postion data. Each entry is an individual with length=#of BAFs. If no NGS data, no need to specify.
`snp_baf.pos` | A list of SNP array BAF postion data. Each entry is an individual with length=#of BAFs. If no SNP array data, no need to specify.
`maxIt` | An integer number indicate the maximum number of EM iteration if not converged during parameter inference. Type integer. Default 50.
`visual` | An indicator variable with value 0,1,2. 0 indicates no visualization, 1 indicates basic visualization, 2 indicates complete visualization (Note visual 2 only work for single platform and integer CN inferenced). Type integer. Default 0
`projname` | A string as the name of this project. Type character. Default 'iCNV.'
`CN` | An indicator variable with value 0,1 for whether wants to infer exact copy number. 0 no exact CN, 1 exact CN. Type integer. Default 0.
`mu` | A length tree vectur specify means of intensity in mixture normal distribution (Deletion, Diploid, Duplification). Default c(-3,0,2)
`cap` | A boolean decides whether we cap insane intensity value due to double deletion or mutiple amplification. Type logical. Default False

### Value

(1) CNV inference, contains CNV inference, Start and end position for each inference, Conditional probability for each inference, mu for mixture normal, sigma for mixture normal, probability of CNVs, Z score for each inference.

(2) exact copy number for each CNV inference, if CN=1.

### Examples

    # icnv call without genotype (just infer deletion, duplication)
    projname <- 'icnv.demo.'
    icnv_res0 <- iCNV_detection(ngs_plr,snp_lrr,
                             ngs_baf,snp_baf,
                             ngs_plr.pos,snp_lrr.pos,
                             ngs_baf.pos,snp_baf.pos,
                             projname=projname,CN=0,mu=c(-3,0,2),cap=TRUE,visual = 1)
    # icnv call with genotype inference and complete plot
    projname <- 'icnv.demo.geno.'
    icnv_res1 <- iCNV_detection(ngs_plr,snp_lrr,
                             ngs_baf,snp_baf,
                             ngs_plr.pos,snp_lrr.pos,
                             ngs_baf.pos,snp_baf.pos,
                             projname=projname,CN=1,mu=c(-3,0,2),cap=TRUE,visual = 2)

[Package iCNV version 1.4.0 Index]