iCNV_detection {iCNV} | R Documentation |
Copy number variation detection tool for germline data. Able to combine intensity and BAF from SNP array and NGS data.
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)
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 |
(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.
# 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)