This package provides access to the CATMAID API for R users. At present it provides low level functions for appropriately authenticated GET/POST requests, optionally parsing JSON responses. There are also intermediate level functions that retrieve skeleton (i.e. neuron) information, connectivity information for one or more neurons as well as a number of other API endpoints. Finally, there is a high level function to convert neurons to the representation of the nat (NeuroAnatomy Toolbox) R package, enabling a wide variety of analyses.

It is strongly recommended to read through the instructions below, the package overview documentation and then skim the reference documentation index , which groups the available functions into useful categories.

## Quick start

# install
if (!require("devtools")) install.packages("devtools")
# nb repo is rcatmaid, but R package name is catmaid
devtools::install_github("jefferis/rcatmaid")

# use
library(catmaid)

# general help starting point
?catmaid

# examples
example(catmaid_fetch)
example(catmaid_get_compact_skeleton)
example(catmaid_get_neuronnames)

# use with nat
library(nat)
open3d()
# nb this also plots the connectors (i.e. synapses)
# red = presynapses, cyan = postsynapses
plot3d(nl, WithConnectors=TRUE)

## Fancier example

This produces a 3D plot of the first and second order olfactory neurons coloured according to the peripheral odorant receptor.

# fetch olfactory receptor neurons
orns[,'Or']= factor(sub(" ORN.*", "", orns[,'name']))

# repeat for their PN partners, note use of search by annotation
pns=read.neurons.catmaid("annotation:ORN PNs$", .progress='text') pns[,'Or']= factor(sub(" PN.*", "", pns[,'name'])) # plot, colouring by odorant receptor plot3d(orns, col=Or) # note that we plot somata with a radius of 1500 nm plot3d(pns, col=Or, soma=1500) ## Even fancier example This follows on from the previous example. It identifies downstream partner neurons of the ORNs and plots them in 3d coloured by their synaptic strength. It then carries out morphological clustering with NBLAST and plots the partner neurons according to those clusters. # find all the ORN downstream partners with at least 2 synapses orn_partners=catmaid_query_connected(orns[,'skid'], minimum_synapses = 2) # keep the ones not already in our set of PNs # there are lots! non_pn_downstream_ids=setdiff(unique(orn_partners$outgoing$partner), pns[,'skid']) # download and plot those neurons non_pn_downstream=read.neurons.catmaid(non_pn_downstream_ids, .progress='text') plot3d(non_pn_downstream, col='grey', soma=1000) # remove the last set of plotted neurons npop3d() ## Plot, but colouring partners by number of synapses they receive from ORNs # first collect those synapse numbers library(dplyr) totsyndf=orn_partners$outgoing %>%
group_by(partner) %>%
summarise(totsyn=sum(syn.count)) %>%
arrange(desc(totsyn))
hist(totsyndf$totsyn) # now do the plot clear3d() # matlab style palette jet.colors <- colorRampPalette(c("#00007F", "blue", "#007FFF", "cyan", "#7FFF7F", "yellow", "#FF7F00", "red", "#7F0000")) # plot colouring by synapse number on a log scale # note that it is necessary to convert totsyndf$partner to a character
# vector to ensure that they are not treated as integer indices
plot3d(as.character(totsyndf$partner), db=c(pns, non_pn_downstream), col=jet.colors(10)[cut(totsyndf$totsyn, breaks = 2^(0:10))], soma=1000)

# Now let's cluster these other connected neurons
library(nat.nblast)
# convert to nblast-compatible format
# nb also convert from nm to um, resample to 1µm spacing and use k=5
# nearest neighbours of each point to define tangent vector
non_pn_downstream.dps=dotprops(non_pn_downstream/1e3, k=5, resample=1, .progress='text')
# now compute all x all NBLAST scores and cluster
non_pn_downstream.aba=nblast_allbyall(non_pn_downstream.dps, .progress='text')
non_pn_downstream.hc=nhclust(scoremat = non_pn_downstream.aba)
# plot result of clusterting as dendrogram, labelled by neuron name (rather than id)
plot(non_pn_downstream.hc, label=non_pn_downstream[,'name'])
# open new window
nopen3d()
# plot in 3d cutting into 2 clusters essentially left right
plot3d(non_pn_downstream.hc,db=non_pn_downstream, k=2, soma=1000)
clear3d()
# 4 clusters - note local and projection neurons, gustatory neurons
plot3d(non_pn_downstream.hc,db=non_pn_downstream, k=4, soma=1000)

## Authentication

You will obviously need to have the login details of a valid CATMAID instance to try this out. As of December 2015 CATMAID is moving to token based authentication. For this you will need to get an API token when you are logged into the CATMAID web client in your browser. See http://catmaid.github.io/dev/api.html#api-token for details.

Once you have the login information you can use the catmaid_login function to authenticate. The minimal information is your server URL and your CATMAID token.

catmaid_login(server="https://mycatmaidserver.org/catmaidroot",
token="9944b09199c62bcf9418ad846dd0e4bbdfc6ee4b")

Note that the CATMAID servers that I am aware of require two layers of password protection, an outer HTTP auth type user/password combination as well as an inner CATMAID-specific token based login. The outer HTTP auth type user/password combination may be specific to you or generic to the project.

### Setting environment variables in your .Renviron file

It is recommended that you set your login details by including code like this in in your .Renviron file:

catmaid.server="https://mycatmaidserver.org/catmaidroot"

# additional security for mycatmaidserver.org/catmaidroot page
catmaid.authname="Calvin"
catmaid.authpassword="hobbes"

Be sure to leave one blank line at the end of the .Renviron file, or it will not work.

In this way authentication will happen transparently as required by all functions that interact with the specified CATMAID server.

### Setting environment variables in your .Rprofile file

Alternatively you can set package options in your .Rprofile file, but the environment variable approach is now recommended as it handles a few edge cases where options are not read by R processes e.g. when building vignettes.

options(catmaid.server="https://mycatmaidserver.org/catmaidroot",
catmaid.token="9944b09199c62bcf9418ad846dd0e4bbdfc6ee4b")

Once again, be sure to leave one blank line at the end of the .Rprofile file, or it will not work.

### Cached authentication

Whether you use options in your .Renviron as described above or you login explicitly at the start of a session using catmaid_login the access credentials will be cached for the rest of the session. You can still authenticate explicitly to a different CATMAID server (using catmaid_login) if you wish.

### Multiple servers

If you use more than one CATMAID server but always do so in different sessions or rmarkdown scripts then you can save an appropriate .Renviron file in the project folder.

If you need to talk to more than one CATMAID server in a single session then you must use catmaid_login to login into each server

# log in to default server specified in .Renviron/.Rprofile
conn2=catmaid_login(server='https://my.otherserver.com', ...)

and then use the returned connection objects with any calls you make e.g.

# fetch neuron from server 1
# fetch neuron from server 2
n2=read.neuron(123, conn=conn2)

n.b. you must use connection objects to talk to both servers because if no connection object is specified, the last connection will be re-used.

## Installation

Currently there isn’t a released version on CRAN but can use the devtools package to install the development version:

if (!require("devtools")) install.packages("devtools")
devtools::install_github("jefferis/rcatmaid")

Note: Windows users need Rtools and devtools to install this way.

## Acknowledgements

Based on python code presently visible at:

by Albert Cardona and Philipp Schlegel. Released under the GPL-3 license.