Small shiny app to perform visual validation of CNV calls. Plots are generated on the fly. CNVs, samples and SNPs table are in the usual format for my packages and pipelines, described in details here.
If you use this tool in your work please cite "Accurate and Effective Detection of Recurrent Copy Number Variants in Large SNP Genotype Datasets", DOI: https://doi.org/10.1002/cpz1.621.
The plot has three row:
- middle row show the BAF pattern for the CNV and 4-6 lengths on each side
- bottom row shows the LRR patter for this same region plus a dotted segment to indicate where the CNV call is.
- top row shows the same as the bottom but even more zoomed out
If a specific locus is selected, locus boundaries are marked with tick marks in all three rows.
To use the app just run
Rscript app.R /path/to/workingdir /path/to/cnvs.txt /path/to/samplee.txt /path/to/snps.txt.
Input files can be anywhere, all path must be full (starting from / with
no symlinks), the results will be saved in workingdir.
It is possible to filter CNVs based on previous visual inspection or on CNV type (deletions or duplications).
The buttons do what you expect them to do ;)
Save will write the results table in the workingdir as project_name_vi_res.txt.
To filter CNVs follow the instructions in on the left and the then click "Run Filtering!" at the very bottom.
If the app freezes and the console report and error on these lines:
Warning: Error in if: missing value where TRUE/FALSE needed
3: runApp
2: print.shiny.appobj
1: <Anonymous>
then it means there are no CNVs in the selected locus with the current filters set. If the SNPs and length filters results in an empty table, a message will appear in the middle of the plot.
The vo column is coded as follows:
- 1 : true
- 2 : false
- 3 : unknown
- 4 : true but incorrect boundaries
- -9 : new
- -7 : error
Just clone the repo, app.R is all is needed to run the program.
R dependencies:
- shiny
- data.table
- ggplot
- CNValidatron
Linux dependencies:
- tabix
- a browser