R/qtl2 QTL analysis for high-dimensional data and complex crosses
In R/qtl2 version 0.8, we merged the multiple packages qtl2geno, qtl2scan, qtl2plot, and qtl2db, into a single package qtl2. The multiple packages proved awkward and confusing. The qtl2convert package (for converting data among the R/qtl2, DOQTL, and R/qtl formats) will remain a separate package.
For discussion/questions about R/qtl2, join the rqtl2-disc google group. Or join rqtl-announce for announcements about R/qtl and R/qtl2. (We’ll try to keep the original rqtl-disc group for the discussion/questions about the original R/qtl only.)
Alternatively, you can install R/qtl2 from its source on GitHub. (But note that compiling the C++ code can be rather slow.)
On Windows, you’ll need Rtools.
On Mac OS X, you’ll need the command-line developer tools.
You then need to install the devtools package, plus a set of package dependencies: yaml, jsonlite, data.table, RcppEigen, RSQLite, and qtl. (Additional, secondary dependencies will also be installed.)
install.packages(c("devtools", "yaml", "jsonlite", "data.table", "RcppEigen", "RSQLite", "qtl"))
Finally, install R/qtl2 using
- user guide
- input file formats (also see the sample data files and the qtl2data repository)
- preparing DO mouse data for R/qtl2
- differences between R/qtl and R/qtl2
- developer guide
- HMM benchmarks
Sources on github: