{"id":19448579,"url":"https://github.com/neurodata/connectocross","last_synced_at":"2025-04-25T02:31:28.546Z","repository":{"id":51320539,"uuid":"310043883","full_name":"neurodata/connectocross","owner":"neurodata","description":"Connectocross: statistical characterizations and comparisons of nanoscale connectomes across taxa (A paper in progress)","archived":false,"fork":false,"pushed_at":"2021-05-15T15:37:58.000Z","size":140495,"stargazers_count":3,"open_issues_count":16,"forks_count":3,"subscribers_count":3,"default_branch":"main","last_synced_at":"2025-04-19T23:28:38.240Z","etag":null,"topics":["connectome"],"latest_commit_sha":null,"homepage":"https://docs.neurodata.io/connectocross/","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/neurodata.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2020-11-04T15:33:34.000Z","updated_at":"2021-05-15T15:38:01.000Z","dependencies_parsed_at":"2022-08-24T07:11:11.044Z","dependency_job_id":null,"html_url":"https://github.com/neurodata/connectocross","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/neurodata%2Fconnectocross","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/neurodata%2Fconnectocross/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/neurodata%2Fconnectocross/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/neurodata%2Fconnectocross/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/neurodata","download_url":"https://codeload.github.com/neurodata/connectocross/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":250741944,"owners_count":21479703,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["connectome"],"created_at":"2024-11-10T16:27:43.343Z","updated_at":"2025-04-25T02:31:27.756Z","avatar_url":"https://github.com/neurodata.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Connectocross: statistical characterizations and comparisons of nanoscale connectomes across taxa\n\n## Datasets\n---\n\n### C. elegans male and hermaphrodite, full body\n|  |  |\n|-------|----------|\n| Paper | [Link](https://www.nature.com/articles/s41586-019-1352-7) |\n| Data | [Link](https://wormwiring.org/) |\n| Raw data location |  |\n| # nodes | ~300 |\n| # edges |  | \n| # synapses | | \n| # graphs | 2 |\n\nNotes \n- has chemical and gap junction graphs\n- has some single-cell transcriptomics\n- has cell lineage\n\n\n### C. elegans timeseries, nerve ring\n|  |  |\n|-------|----------|\n| Paper | [Link](https://www.biorxiv.org/content/10.1101/2020.04.30.066209v2) |\n| Data |  |\n| Raw data location |  |\n| # nodes | ~50 - 150 per graph?|\n| # edges | | \n| # synapses | | \n| # graphs | 8 |\n\nNotes\n- time series of graphs (though from different animals)\n- 2 animals at the last timepoint\n- I have code to pull data\n\n### Drosophila larva brain\n|  |  |\n|-------|----------|\n| Paper | not yet available |\n| Data | we have it |\n| Raw data location | CATMAID |\n| # nodes | 2971 |\n| # edges | ~100k | \n| # synapses ~300k | \n| # graphs | 1 |\n\nNotes: \n- Have incomplete cell lineage \n- I think Marta's lab has some single cell scRNAseq\n- Have edge type split by axo, dendrite\n\n### Drosophila adult brain chunk (hemibrain)\n|  |  |\n|-------|----------|\n| Paper | [Link](https://www.biorxiv.org/content/10.1101/2020.01.21.911859v1) |\n| Data | [Link](https://www.janelia.org/project-team/flyem/hemibrain) |\n| Raw data location | neuPrint |\n| # nodes | 20 - 25k, 67k more small objects |\n| # edges |  | \n| # synapses | 64M | \n| # graphs | 1 |\n\n### Drosophila adult brain sparse (FAFB)\n|  |  |\n|-------|----------|\n| Paper | [Link](https://www.cell.com/cell/fulltext/S0092-8674(18)30787-6) |\n| Data | [Link to overview,](https://temca2data.org/) [Link to CATMAID](https://fafb.catmaid.virtualflybrain.org/)|\n| Raw data location | CATMAID |\n| # nodes |  |\n| # edges |  | \n| # synapses | |\n| # graphs | 1 |\n\n### Platynereis larva full \n|  |  |\n|-------|----------|\n| Paper | [Link](https://www.biorxiv.org/content/10.1101/2020.08.21.260984v2) |\n| Data | not yet available (I think) |\n| Raw data location | CATMAID |\n| # nodes | 2728 |\n| # edges | 11437 | \n| # synapses | | \n| # graphs | 1 |\n\n### MiCRONS\n| | | \n|-------|--------|\n\n### Bryan Jones Retina \n\n### Cionia intestinalis\n| | |\n|-------|--------|\n| Paper | [Link](https://elifesciences.org/articles/16962) |\n| Data | |\n| # nodes | ~200? |\n| # edges | |\n| # synapses | |\n| # graphs | | \n\n\n## Simple a priori models\na.k.a. look at the data, more or less\n\n### Simplest statistics\nThings that we always want to know about a graph. Usually: \n- Number of nodes\n- Number of edges\n- For a connectome, maybe number of actual synapses\n\n### Density (ER)\n- compute the density (p) for each connectome, can simply plot each.\n\n### Left/right (SBM/DCSBM)\n- Test different hypotheses about $\\hat{B}$ (see statistical connectomics) \n   - is it more densely connected within block than between? To what extent?\n      - maybe can compare this for many of the connectomes. probably not all\n   - core-periphery\n   - etc.\n\n### Left/right + any known metadata (SBM/DCSBM)\n- If any putative cell types are known, use those\n- now we get a more refined SBM than the above, maybe interesting, maybe not? \n   - cell type data may not be available for all of the above\n- can do similar tests, results may or may not be different\n\n### General low rank (RDPG)\n- Scree plots\n- estimation of rank (ZG2)\n- not sure that this will be interesting to compare across connectome or not. would\n  have to normalize for the number of nodes somehow, i'd think.\n\n### Distribution of weights, degrees\n- Can just look at distribution of edge weight for each, i guess where weight is number of synapses\n- in/out degree distribution, marginals and joint, is easy enough to plot.\n   - again, don't know whether it'll be meaningful to compare across connectome or not\n\n## More complicated a priori models\n\n### Homotypic affinity\n- can test for whether cell pairs (or blocks?) are more likely than chance to connect (homotypic affinity)\n- requires having cell pairs\n   - probably only maggot and c. elegans\n\n### Testing left vs right, quantify correlation, spectral similarity, GM performance, etc.\n\n### Testing for gaia's directedness (or just quantifying to what extent it happens)\n- degree of reciprocal feedback? had thought about something along the lines of testing \n  for the difference between left and right latent positions. but maybe a simpler first\n  statistic to compute is: P(edge from j to i | edge from i to j) \n\n## A posteriori models\n\n### Spectral clustering and estimating an SBM, DCSBM, DDSBM\n- can try to incorporate homotypic affinity also... or correlation L/R\n- figure 3 from maggot paper\n\n### Feedforward layout and proportion of feedforward edges\n\n## Models with biological metadata\n\n### Testing for Peter's rule via the contact graph\n- is the adjacency a noisy version of the contact graph?\n- how does rank change as we jitter xyz of synapses\n- could we also just swap synapses in an epsilon ball and see how structure changes?\n\n### Spectral clustering that uses morphology\n\n### Configuration models that swap synapses within an epsilon ball\n\n### Can we cluster edges via connectivity + space? \n- had talked about trying to cluster the line graph \n- spectral embedding of the line graph looked bad when I tried it. Need to follow up.\n\n## Niche models that may not work for all data \n\n### Different hypotheses for a multilayer SBM-like model\n- maggot data\n\n### Matching FAFB and hemibrain or either to maggot\n- could be spectral, could be GM\n- results maybe bad?\n- could use morphology, could not\n\n### Spectral coarsening between maggot and adult\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fneurodata%2Fconnectocross","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fneurodata%2Fconnectocross","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fneurodata%2Fconnectocross/lists"}