make_rand_sampling_curve: Make (randomised) sampling curve object for neuron or...

In jefferis/emsampling: Experimental functions to analyse EM connectomics data

Description

Make (randomised) sampling curve object for neuron or connection table

Usage

make_rand_sampling_curve(x, partners = c("auto", "out", "in"),
  sample = TRUE, volume = NULL, keep.input = TRUE)

Arguments

x	A catmaid_skids compatible neuron specification or a data.frame generated by catmaid_get_connector_table.
partners	Whether to examine the input or output partners for given neuron.
sample	Whether to randomise the connection order (this is the right thing to do if the input neuron has been completed but was not randomly sampled in the first place.)
volume	An optional neuropil volume to which connectors can be restricted after being fetched from CATMAID. Can be either a character vector naming the volume or a surface object that is (or can be converted as.mesh3d) class mesh3d e.g. hxsurf.
keep.input	Whether or not to keep the raw input as an attribute on the samplingcurve object.

Details

make_rand_sampling_curve is a thin wrapper for samplingcurve, which has the additional functionality of fetching (all) the synapses for one or more CATMAID neurons (specified using any form compatible with catmaid_skids).

Value

An object of class samplingcurve

Examples

sc=make_rand_sampling_curve(2560371, partners='out')
head(sc)
plot(sc, main='Randomised output sampling curve for rLPTCIN')
lines(sc, rand=20)

scin=make_rand_sampling_curve(2560371, partners='in')
plot(sc, main='Randomised input sampling curve for rLPTCIN')
lines(sc, rand=20)

jefferis/emsampling documentation built on July 21, 2019, 2:20 a.m.