Web-based interactive charts (using D3.js) for the analysis of experimental crosses to identify genetic loci (quantitative trait loci, QTL) contributing to variation in quantitative traits.
Add a new chart,
scat2scat. The idea is to summarize each of a
long series of scatterplots with a pair of numbers. Then a
scatterplot of those summary statistics is linked to the underlying
details: click on a point in the main scatterplot and have the
underlying scatterplot be shown.
Add a new chart,
itriplot, for plotting trinomial probabilities,
represented as points in an equilateral triangle.
idotplot so that
idotplot is the main
iplotPXG calls it.
Add some additional options, such as
Change the name of some options, such as
iplotCurves (in place of
digits argument for all plot functions, with the aim to
reduce the size of the datasets included in the resulting charts.
Removed the vignettes from the package (for complicated reasons); they're available at the R/qtlcharts website.
Fix proliferation of tool tips
For use with Shiny, clear SVG before drawing
Changed license and author list in order to post the package on CRAN, http://cran.r-project.org
iplot now use the names of the input data as
individual IDs if
indID is missing or
pxgtype="ci". In the case of phenotypes with missing values, the confidence intervals were incorrect.
scatterplotsthat controls whether scatterplots will be shown when clicking on pixels in the heatmap. (If
scatterplots=FALSE, we skip the scatterplots.)
iplotMScanone can plot just points (rather than curves) for the
LOD scores and QTL effects in the lower and right-hand panels.
Fix a bug in
iplotMScanone (x-axis labels in right-hand plot
weren't being shown)
setScreenSizefunction for controlling the default sizes of charts.
idotplot function for plotting a quantitative variable in
different categories. (It's just like
iplotPXG, but with data
values not connected to a cross object.)
Reorganized the d3panels code: just
d3panels.min.css rather than linking
to js code for individual panels.
To save a plot to a file, you now need to assign the result of a plot
function to an object and then use the
library(qtlcharts)library(qtl)data(hyper)hyper <- calc.genoprob(hyper, step=1)out <- scanone(hyper)chart <- iplotScanone(out, hyper)htmlwidgets::saveWidget(chart, file="hyper_scanone.html")
It's now simpler to include interactive charts within an R Markdown
document. You simply make the plots in a code chunk in the way that
you would at the R prompt. There's no longer a need to worry about