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Write a LDna object from a tidy data frame

Source: R/write_ldna.R
write_ldna.Rd

Write a LDna object from a biallelic tidy data frame. Used internally in genometranslator and might be of interest for users.

Usage

write_ldna(
  data,
  filename = NULL,
  parallel.core = parallel::detectCores() - 1,
  ...
)

Arguments

data

A tidy data frame object in the global environment or a tidy data frame in wide or long format in the working directory. How to get a tidy data frame ? Look into genometranslator tidy_genome. The genotypes are biallelic.

filename

(optional) The file name of the LDna lower matrix file. Radiator will append .ldna.rds to the filename. If filename chosen is already present in the working directory, the default radiator_datetime.ldna.rds is chosen. With default, filename = NULL, no file is generated, only an object in the Global Environment. To read the data back into R, use readRDS("filename.ldna.rds"). Default: filename = NULL.

parallel.core

Default: parallel.core = parallel::detectCores() - 1.

...

(optional) To pass further argument for fine-tuning the function (see details).

Details

The function requires SNPRelate to prepare the data for LDna.

To install SNPRelate: install.packages("BiocManager") BiocManager::install("SNPRelate")

To install LDna: devtools::install_github("petrikemppainen/LDna")

Dependencies

Required package dependencies are declared in DESCRIPTION and installed with genometranslator. Run genometranslator_dependencies() to inspect core packages, optional packages, and external executables.

This writer uses the optional Bioconductor package SNPRelate to calculate the LD matrix. It writes data for LDna; it does not install or run the separate LDna software.

Data filtering

This writer does not silently filter markers or individuals. It may validate requirements imposed by the destination format and stop with an informative error when the input is unsuitable. It is the user's responsibility to filter and quality-control the data appropriately for the intended analysis before generating the output. Use radr or another suitable workflow when filtering is required.

References

Kemppainen P, Knight CG, Sarma DK et al. (2015) Linkage disequilibrium network analysis (LDna) gives a global view of chromosomal inversions, local adaptation and geographic structure. Molecular Ecology Resources, 15, 1031-1045.

Zheng X, Levine D, Shen J, Gogarten SM, Laurie C, Weir BS. (2012) A high-performance computing toolset for relatedness and principal component analysis of SNP data. Bioinformatics. 28: 3326-3328. doi:10.1093/bioinformatics/bts606

Author

Thierry Gosselin thierrygosselin@icloud.com