The goal of coconat is to provide dataset agnostic support for connectome analysis.

It is intended to have a mix of end-user functionality and functions that are principally intended as building blocks for more specialised packages such as https://natverse.org/neuprintr/ or https://natverse.org/fafbseg/.

It is also a key building block for the https://natverse.org/coconat package, which provides a small number of powerful functions for comparative connectomics analysis of multiple dense reconstructions of drosophila brain and VNC data.

Installation

You can install the development version of coconat from GitHub with:

# install.packages("devtools")
devtools::install_github("natverse/coconat")

Example

This is a basic example which shows you how to solve a common problem:

library(coconat)
# da2ds=neuprintr::neuprint_connection_table('DA2_lPN', details=TRUE, partners='out', conn = hbconn)
da2ds=readRDS(system.file('sampledata/da2ds.rds', package = 'coconat'))
head(da2ds)
#>       bodyid    partner prepost weight      name    type
#> 1 1796817841 5812982273       1     28   KCg-m_R   KCg-m
#> 2 1796817841  574377845       1     28 LHAV3f1_R LHAV3f1
#> 3 1797505019 1202410042       1     27   KCg-m_R   KCg-m
#> 4 1796818119  632402333       1     26   KCg-m_R   KCg-m
#> 5 1827516355 5813055865       1     26 LHAD1f2_R LHAD1f2
#> 6 1796818119 5813080766       1     24   KCg-m_R   KCg-m

library(Matrix)
am=partner_summary2adjacency_matrix(da2ds, inputcol = 'bodyid', outputcol = 'partner')
image(am)

am2=partner_summary2adjacency_matrix(subset(da2ds, weight>=15), 
                                     inputcol = 'bodyid', outputcol = 'partner')
image(am2)

Now we can also cosine cluster

We can do this for the inputs (the 5 PNs should look much the same)

heatmap(cosine_sim(am, transpose = T))

Or the outputs

heatmap(cosine_sim(am))

But it would of course be more useful if we did this based on a richer set of inputs