https://github.com/neurodata/connectocross
Connectocross: statistical characterizations and comparisons of nanoscale connectomes across taxa (A paper in progress)
https://github.com/neurodata/connectocross
connectome
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Connectocross: statistical characterizations and comparisons of nanoscale connectomes across taxa (A paper in progress)
- Host: GitHub
- URL: https://github.com/neurodata/connectocross
- Owner: neurodata
- License: mit
- Created: 2020-11-04T15:33:34.000Z (over 5 years ago)
- Default Branch: main
- Last Pushed: 2021-05-15T15:37:58.000Z (about 5 years ago)
- Last Synced: 2025-04-19T23:28:38.240Z (about 1 year ago)
- Topics: connectome
- Language: Jupyter Notebook
- Homepage: https://docs.neurodata.io/connectocross/
- Size: 134 MB
- Stars: 3
- Watchers: 3
- Forks: 3
- Open Issues: 16
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Connectocross: statistical characterizations and comparisons of nanoscale connectomes across taxa
## Datasets
---
### C. elegans male and hermaphrodite, full body
| | |
|-------|----------|
| Paper | [Link](https://www.nature.com/articles/s41586-019-1352-7) |
| Data | [Link](https://wormwiring.org/) |
| Raw data location | |
| # nodes | ~300 |
| # edges | |
| # synapses | |
| # graphs | 2 |
Notes
- has chemical and gap junction graphs
- has some single-cell transcriptomics
- has cell lineage
### C. elegans timeseries, nerve ring
| | |
|-------|----------|
| Paper | [Link](https://www.biorxiv.org/content/10.1101/2020.04.30.066209v2) |
| Data | |
| Raw data location | |
| # nodes | ~50 - 150 per graph?|
| # edges | |
| # synapses | |
| # graphs | 8 |
Notes
- time series of graphs (though from different animals)
- 2 animals at the last timepoint
- I have code to pull data
### Drosophila larva brain
| | |
|-------|----------|
| Paper | not yet available |
| Data | we have it |
| Raw data location | CATMAID |
| # nodes | 2971 |
| # edges | ~100k |
| # synapses ~300k |
| # graphs | 1 |
Notes:
- Have incomplete cell lineage
- I think Marta's lab has some single cell scRNAseq
- Have edge type split by axo, dendrite
### Drosophila adult brain chunk (hemibrain)
| | |
|-------|----------|
| Paper | [Link](https://www.biorxiv.org/content/10.1101/2020.01.21.911859v1) |
| Data | [Link](https://www.janelia.org/project-team/flyem/hemibrain) |
| Raw data location | neuPrint |
| # nodes | 20 - 25k, 67k more small objects |
| # edges | |
| # synapses | 64M |
| # graphs | 1 |
### Drosophila adult brain sparse (FAFB)
| | |
|-------|----------|
| Paper | [Link](https://www.cell.com/cell/fulltext/S0092-8674(18)30787-6) |
| Data | [Link to overview,](https://temca2data.org/) [Link to CATMAID](https://fafb.catmaid.virtualflybrain.org/)|
| Raw data location | CATMAID |
| # nodes | |
| # edges | |
| # synapses | |
| # graphs | 1 |
### Platynereis larva full
| | |
|-------|----------|
| Paper | [Link](https://www.biorxiv.org/content/10.1101/2020.08.21.260984v2) |
| Data | not yet available (I think) |
| Raw data location | CATMAID |
| # nodes | 2728 |
| # edges | 11437 |
| # synapses | |
| # graphs | 1 |
### MiCRONS
| | |
|-------|--------|
### Bryan Jones Retina
### Cionia intestinalis
| | |
|-------|--------|
| Paper | [Link](https://elifesciences.org/articles/16962) |
| Data | |
| # nodes | ~200? |
| # edges | |
| # synapses | |
| # graphs | |
## Simple a priori models
a.k.a. look at the data, more or less
### Simplest statistics
Things that we always want to know about a graph. Usually:
- Number of nodes
- Number of edges
- For a connectome, maybe number of actual synapses
### Density (ER)
- compute the density (p) for each connectome, can simply plot each.
### Left/right (SBM/DCSBM)
- Test different hypotheses about $\hat{B}$ (see statistical connectomics)
- is it more densely connected within block than between? To what extent?
- maybe can compare this for many of the connectomes. probably not all
- core-periphery
- etc.
### Left/right + any known metadata (SBM/DCSBM)
- If any putative cell types are known, use those
- now we get a more refined SBM than the above, maybe interesting, maybe not?
- cell type data may not be available for all of the above
- can do similar tests, results may or may not be different
### General low rank (RDPG)
- Scree plots
- estimation of rank (ZG2)
- not sure that this will be interesting to compare across connectome or not. would
have to normalize for the number of nodes somehow, i'd think.
### Distribution of weights, degrees
- Can just look at distribution of edge weight for each, i guess where weight is number of synapses
- in/out degree distribution, marginals and joint, is easy enough to plot.
- again, don't know whether it'll be meaningful to compare across connectome or not
## More complicated a priori models
### Homotypic affinity
- can test for whether cell pairs (or blocks?) are more likely than chance to connect (homotypic affinity)
- requires having cell pairs
- probably only maggot and c. elegans
### Testing left vs right, quantify correlation, spectral similarity, GM performance, etc.
### Testing for gaia's directedness (or just quantifying to what extent it happens)
- degree of reciprocal feedback? had thought about something along the lines of testing
for the difference between left and right latent positions. but maybe a simpler first
statistic to compute is: P(edge from j to i | edge from i to j)
## A posteriori models
### Spectral clustering and estimating an SBM, DCSBM, DDSBM
- can try to incorporate homotypic affinity also... or correlation L/R
- figure 3 from maggot paper
### Feedforward layout and proportion of feedforward edges
## Models with biological metadata
### Testing for Peter's rule via the contact graph
- is the adjacency a noisy version of the contact graph?
- how does rank change as we jitter xyz of synapses
- could we also just swap synapses in an epsilon ball and see how structure changes?
### Spectral clustering that uses morphology
### Configuration models that swap synapses within an epsilon ball
### Can we cluster edges via connectivity + space?
- had talked about trying to cluster the line graph
- spectral embedding of the line graph looked bad when I tried it. Need to follow up.
## Niche models that may not work for all data
### Different hypotheses for a multilayer SBM-like model
- maggot data
### Matching FAFB and hemibrain or either to maggot
- could be spectral, could be GM
- results maybe bad?
- could use morphology, could not
### Spectral coarsening between maggot and adult