Welcome to the natverse, we have tools for brains!

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The natverse package is a wrapper for all of the commonly used NeuroAnatomy Toolbox packages. This is convenient both for package installation and for loading/attaching these packages without many calls to library().

See http://natverse.org for more details and a guide for installation. For tutorials on how to use the natverse see the natverse/nat.examples repository on GitHub.

What does the natverse do?

General concepts in neuroanatomy and the morphological peculiarities of your favourite neurons can often easily be explained with a pencil, some paper and a tea break. However, substantial time and effort have to be sunk in front of one’s computer, reading documentation, battling installation issues, exploring GUIs and fighting with data visualisation tools to actually work with neuron morphology data. Complex, informative analyses have been enabled by two key sets of technologies; bettered automatic, and manual and collaborative, reconstruction methodologies and sophisticated registration tools that enable the creation of, and alignment to, atlas-like standardised brain spaces, otherwise known as template spaces. Registering neurons based on a standard reference significantly aids the discovery and classification of neuronal cell types because it allows type classification relative to the arbours of other neuronal types. At its most advanced, the field aims for the complete reconstruction of individual neurons in dense, nanometer resolution datasets, the alignment of tens of thousands of image stacks from as many brains into a single reference ‘brain space’ and the automatic classification of neuronal cell types. Bridgings between brain spaces enable comparisons between different datasets; between sexes, similar species, health states and between different data resolutions, e.g. corresponding light microscopy (LM) data and data from electron micrographs (EM).

The natverse has been designed to help with all of this. It is as suite of R packages for neuroinformatic, and in particular neuromorphological analyses. The R programming language is perhaps the premier environment for statistical data analysis, is well supported by the integrated development environment RStudio and is a strong choice for data visualisation. It already hosts a wealth of popular, packaged morphometry related functions. R is also a pillar of the bioinformatics field. R code can be called using, for example, Julia, Matlab, Java, Perl, Ruby, F# and Python.