neurodata/connectocross

Connectocross: statistical characterizations and comparisons of nanoscale connectomes across taxa (A paper in progress)

connectocross's Issues

write dataset fetching functions, include as lightweight package

see sklearn's dataset fetcher's https://scikit-learn.org/stable/datasets/index.html#real-world-datasets

they download a dataset if necessary, and then load from local storage to memory in Python (I think)

Would want something similar that pulls the nicely formatted data from #2, which is saved in the spec specified by #1, and then loads it into python

Add list of available connectomes with references to a page in the documentation

• References can be added as bibtex (see the current references file)

run modularity

(decide if we need a) lightweight graph + metadata object

i often find something like this helpful.

Just a light graph object that stores adjacency matrix + pandas metadata on the nodes dataframe

havent done anything smart for multigraph or edges with features

likely a better way to implement the above

unsure if it is even necessary or networkx is enough. but end up using adjacency matrix representation so much that it was convenient

talk to connectome neurobiologists

get feedback on early outline. in particular:

• what connectomes specifically
• what questions are biologically interesting

define a file spec for attributed graphs

• should be well suited to the data we care about here as a first use case
• in the past we have discussed a csv edgelist for the graph itself with separate json(s) for metadata

fit a priori SBMs to the connectomes

Spencer:

• seems like something that is easy to do and we could do for all of them
• could just use the metadata, find some features that we care about.

Ben:

• maybe we fit using various node metadata columns and just report likelihood, number of parameters, bic or something like that
• maybe also just plot them and show that we can fit these models

make gallery with graph layouts for each connectome

write data pulling functions for the connectomes of interest - NeuPrint

for the connectomes listed in the readme (roughly, exact ones may change)

• want a simple, clear script to pull necessary graph + metadata from wherever it's hosted online
• saves that data to the format specified in #1

Note: Sub-issue of #2

Specifically, focus on datasets stored as NeuPrint files.

graph matching stuff

we could run graph matching on a lot of these connectomes
maybe even match some of them to other connectomes

write data pulling functions for the connectomes of interest

for the connectomes listed in the readme (roughly, exact ones may change)

• want a simple, clear script to pull necessary graph + metadata from wherever it's hosted online
• saves that data to the format specified in #1

Note: obviously a bit downstream of #1, but some work on pulling the data could probably start concurrently. configuring how to save to whatever format we pick is likely not the bottleneck for this issue.

figure out how to extract contact graphs from CATMAID

• email Albert, see if such a thing already exists
• If it's not there currently, write code to do it based on skeletons

write data pulling functions for the connectomes of interest - xlsx

for the connectomes listed in the readme (roughly, exact ones may change)

• want a simple, clear script to pull necessary graph + metadata from wherever it's hosted online
• saves that data to the format specified in #1

Note: Sub-issue of #2

Specifically, focus on datasets stored as .xlsx files.

adjacency matrix plots

• will probably have to figure out how to sort each one

make a table/markdown/something of edge/node/graph attributes for each connectome

fitting L/R SBMs, testing different L/R SBM hypotheses

(see statistical connectomics)

Write data pulling function for Cionia intestinalis

paper: https://elifesciences.org/articles/16962
data (xlsx): https://elifesciences.org/articles/16962/figures#content

• data is stored in multiple xlsx files with different metadata

Consistent styling

Palettes

As a group, please decide on a consistent color palette for species/dataset:
https://seaborn.pydata.org/tutorial/color_palettes.html
https://matplotlib.org/stable/tutorials/colors/colormaps.html

It may make sense to do something like have two different shades of the same color for multiple related datasets (e.g. two C. elegans could be two shades of blue or something) as long as this is less distinct than the species annotation.

I'd like this palette to just be saved somewhere as a json that any script can just import

Style

As a group, please decide on a consistent style for matplotlib. This is also something that can be saved and import easily. One example is here which you are welcome to use or modify.

E.g. each notebook you each separately make can then just call set_theme() and everyone's plots will look the same

simple statistics

• number of nodes
• number of edges
• max degree
• graph density

chart of the above for each connectome we have

L/R homotypic affinity?

to what extent are edges between L/R pairs more probable than any L/R connection?

(which datasets have L/R pairs?)

(If they don't have pairs, can we use graph matching to predict?)

write data pulling functions for the connectomes of interest - CATMAID

for the connectomes listed in the readme (roughly, exact ones may change)

• want a simple, clear script to pull necessary graph + metadata from wherever it's hosted online
• saves that data to the format specified in #1

Note: Sub-issue of #2

Specifically, focus on datasets stored as CATMAID files.