iCNV_detection: CNV detection

In zhouzilu/iCNV: Integrated Copy Number Variation 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)

zhouzilu/iCNV documentation built on Feb. 16, 2020, 4:53 p.m.