***The most up to date information is available on the RMINC on Github page. It should be the FIRST place you explore for information. The README (scroll down on the homepage) contains numerous examples/tutorials. Including how to do voxel-wise statistics, visualizing with RMINC, etc.***
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The main development home for RMINC can be found here: RMINC on Github - that page includes access to the source code, downloads, bug reporting, and facilities for asking questions (and getting them answered, of course).
The main RMINC documentation comes in the form of a mini-book ; find the most recent version here: https://launchpad.net/rminc/+download, or a direct link to the pdf here: VBMstats.pdf (Note: this document was last updated on April 16, 2008).
Some of the latest tutorials from GitHub
Visualizing 3D Objects with RMINC: https://rawgit.com/Mouse-Imaging-Centre/RMINC/master/inst/docdocumentation/RMINC_rgl.html
Exploring your data using the shiny app (launch_shinyRMINC):
# instructions on how to use the shiny app: ?launch_shinyRMINC
Analyzing volume differences in neuroanatomy with a single set of labels
> anatGetAll function (filenames, atlas = NULL, method = "jacobians", defs = "/projects/mice/jlerch/cortex-label/c57_brain_atlas_labels.csv", dropLabels = FALSE, side = "both")
- filenames: Instead of the scaled_jacobian files, you would instead include the labels for each brain generated by MAGeT.
- atlas: This argument is NOT USED when each file has its own set of labels. Unfortunately, you still need to specify something or the function will fail. An example of what to specify is the labels for the first file – filenames$labels
- method: method = "labels" must be specified
- defs: This is critical if the label definitions for the files you are looking at differ from the standard set of 62, described in Dorr, et. al. This will also need to be specified for users who wish use the set described in Dorr et.al, but are not using the machines at MICe.
- dropLabels and side can continue to use the defaults.
#anatGetAll call, with slightly different arguments volumes <- anatGetAll(filenames$labels, filenames$labels, method="labels", defs="brain_label_mappings.csv") #combining structures, anatLm, anatFDR proceed as above: volumes_combined <- anatCombineStructures(volumes, defs="brain_label_mappings.csv") anatLm(~ genotype, filenames, volumes_combined) anatFDR(anatLm(~ genotype, filenames, volumes_combined))