icnv's Introduction

iCNV

Integrated copy number variation detection toolset

Author

Zilu Zhou, Nancy R. Zhang

Maintainer

Zilu Zhou [email protected] Please comment on the Issues section for additional questions.

Description

iCNV is a normalization and germline copy number variation detection procedure for mutiple study designs: WES only, WGS only, SNP array only, or any combination of SNP and sequencing data. iCNV applies platform specific normalization, utilizes allele specific reads from sequencing and integrates matched NGS and SNP-array data by a Hidden Markov Model (HMM).

Installation

Install from GitHub (Recommended)

# try http:// if https:// URLs are not supported
source("https://bioconductor.org/biocLite.R")
biocLite("CODEX")

# Install iCNV
install.packages("devtools")
library(devtools)
install_github("zhouzilu/iCNV")

Update

iCNV has made a lot of changes on 1/29/2019 for stability, bug fixing, computation power and functionality. We strongly recommend you update iCNV to the newest version using the following command.

Update instruction

# Remove iCNV
remove.packages('iCNV')

# reinstall iCNV
install.packages("devtools")
library(devtools)
install_github("zhouzilu/iCNV")

Workflow overview

Number in the parentheses referring to different section in Vignettes and function details can be found here

        NGS                                             |           Array
BAM    BED(UCSC for WES or bed_generator.R for WGS 2.2) |    SNP Intensity(in standard format)
 |                |                                     |             |
 |----------------|                                     |             |
 |                |                                     |             |icnv_array_input (2.4)
 |SAMTools(2.3)   |CODEX(2.2)                           |             |
 |                |                                     |             |-----------|
Variants BAF(vcf) PLR                                   |        Array LRR   Array BAF
 |                |                                     |             |           |
 |                |                                     |             |SVD(2.4)   |
 |                |                                     |             |           |
 |                |                                     |     Normalized LRR      |
 |                |                                     |             |           |
 -----------------------------------------------------------------------------------
                                          |
                                          |iCNV_detection(2.5-2.6)
                                          |
                                     CNV calling
                                          |
                                          |icnv_output_to_gb()
                                          |
                              Genome Browser input

Demo code & Vignettes with rich displays

Citation

Zilu Zhou, Weixin Wang, Li-San Wang, Nancy Ruonan Zhang; Integrative DNA copy number detection and genotyping from sequencing and array-based platforms, Bioinformatics, Volume 34, Issue 14, 15 July 2018, Pages 2349–2355, https://doi.org/10.1093/bioinformatics/bty104

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icnv's Issues

'cnv' is not recognized as an internal or external command

iCNV_detection(snp_lrr=snp_lrr, snp_baf = snp_baf, 
                             snp_lrr.pos = snp_lrr.pos,snp_baf.pos = snp_baf.pos,
                             projname="projectname",CN=1,mu=c(-3,0,2),cap=TRUE,visual = 2)

gives error:

'cnv' is not recognized as an internal or external command,
operable program or batch file.

command works if using example with ngs data from vignette.

R version 4.0.3 (2020-10-10)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 19042)

Matrix products: default

locale:
[1] LC_COLLATE=English_Canada.1252  LC_CTYPE=English_Canada.1252    LC_MONETARY=English_Canada.1252 LC_NUMERIC=C                   
[5] LC_TIME=English_Canada.1252    

attached base packages:
[1] stats4    parallel  stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] iCNV_0.99.27                      CODEX_1.22.0                      BSgenome.Hsapiens.UCSC.hg19_1.4.3 BSgenome_1.58.0                  
 [5] rtracklayer_1.49.5                Rsamtools_2.6.0                   Biostrings_2.58.0                 XVector_0.30.0                   
 [9] GenomicRanges_1.42.0              GenomeInfoDb_1.26.2               IRanges_2.24.1                    S4Vectors_0.28.1                 
[13] BiocGenerics_0.36.0               magrittr_2.0.1                    readr_1.4.0                       devtools_2.3.2                   
[17] usethis_2.0.0                    

loaded via a namespace (and not attached):
 [1] lattice_0.20-41             prettyunits_1.1.1           ps_1.5.0                    assertthat_0.2.1            rprojroot_2.0.2            
 [6] digest_0.6.27               R6_2.5.0                    evaluate_0.14               pillar_1.4.7                zlibbioc_1.36.0            
[11] rlang_0.4.10                curl_4.3                    rstudioapi_0.13             callr_3.5.1                 Matrix_1.2-18              
[16] rmarkdown_2.6               desc_1.2.0                  BiocParallel_1.24.1         stringr_1.4.0               RCurl_1.98-1.2             
[21] DelayedArray_0.16.0         compiler_4.0.3              xfun_0.20                   pkgconfig_2.0.3             pkgbuild_1.2.0             
[26] htmltools_0.5.0             SummarizedExperiment_1.20.0 tibble_3.0.5                GenomeInfoDbData_1.2.4      matrixStats_0.57.0         
[31] XML_3.99-0.5                fansi_0.4.2                 crayon_1.3.4                withr_2.4.0                 GenomicAlignments_1.26.0   
[36] bitops_1.0-6                grid_4.0.3                  jsonlite_1.7.2              lifecycle_0.2.0             stringi_1.5.3              
[41] cli_2.2.0                   fs_1.5.0                    remotes_2.2.0               testthat_3.0.1              ellipsis_0.3.1             
[46] vctrs_0.3.6                 tools_4.0.3                 Biobase_2.50.0              glue_1.4.2                  purrr_0.3.4                
[51] hms_1.0.0                   MatrixGenerics_1.2.0        yaml_2.2.1                  processx_3.4.5              pkgload_1.1.0              
[56] sessioninfo_1.1.1           memoise_1.1.0               knitr_1.30

Using packrat for dependencies

Using packrat for R version/package dependencies will tremendously help users :)

https://rstudio.github.io/packrat/

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zhouzilu / icnv Goto Github PK