FANC_auto_recon

FANC (pronounced "fancy") is the Female Adult Nerve Cord, a GridTape-TEM dataset of an adult Drosophila melanogaster's ventral nerve cord. The dataset was first published in Phelps, Hildebrand, Graham et al. 2021 Cell, after which we applied automated methods for reconstructing neurons, synapses, and nuclei to accelerate reconstruction of the ventral nerve cord connectome, as described in Azevedo, Lesser, Mark, Phelps et al. 2022 bioRxiv.

This repository contains:

• A python package for interacting with the connectome data (see the folder fanc/, and installation instructions below)
• Other files and information related to the automated reconstructions (e.g. nuclei_prediction/, synapse_prediction/)
• Information for the collaborative proofreading community (see the wiki).

Have any questions? Please open an issue or contact Jasper Phelps (jasper.s.phelps@gmail.com).

Installing and configuring the fanc python package

Before you start

As is always the case in python, consider making a virtual environment (using your preference of virtualenv/virtualenvwrapper or conda) before installing.

Installation option 1: pip install from PyPI

pip install fanc-fly

Installation option 2: pip install directly from GitHub

The code on GitHub will sometimes be slightly more up to date than the version on PyPI

pip install git+https://github.com/htem/FANC_auto_recon.git

Installation option 3: Clone then install

This is the best option if you want to make changes yourself to the code

cd ~/repos  # or wherever you keep your repos
git clone https://github.com/htem/FANC_auto_recon.git
cd FANC_auto_recon
pip install -e .

Troubleshooting

Depending on your Python 3 version and your operating system, you may need to battle some bugs in order to get the pip install commands above to succeed.

If you get something that looks like

.. ERROR:: Could not find a local HDF5 installation.
   You may need to explicitly state where your local HDF5 headers and
   library can be found by setting the ``HDF5_DIR`` environment
   variable or by using the ``--hdf5`` command-line option.

and you're on a Mac, install brew (https://brew.sh) if you haven't yet, then use brew to install HDF5 with brew install hdf5, then put HDF5_DIR=/opt/homebrew/opt/hdf5 in front of your pip install command (e.g. HDF5_DIR=/opt/homebrew/opt/hdf5 pip install fanc-fly).

If you get an error that contains

Error compiling Cython file:
...
Cython.Compiler.Errors.CompileError: tables/utilsextension.pyx

try to pip install the latest version of tables from GitHub by running HDF5_DIR=/opt/homebrew/opt/hdf5 pip install git+https://github.com/PyTables/PyTables, or alternatively, use conda to install it (conda install tables). After you get this package installed successfully, try installing fanc-fly again.

Provide credentials

Access to the latest reconstruction of FANC is restricted to authorized users. If you are a member of the FANC community (see Collaborative community on this repo's wiki) and have been granted access, you can generate an API key by visiting https://global.daf-apis.com/auth/api/v1/create_token and logging in with your FANC-authorized google account. Copy the key that is displayed, then run the following commands in python to save your key to the appropriate file:

import fanc
fanc.save_cave_credentials("THE API KEY YOU COPIED")

Alternatively, you can manually do what the command above accomplishes, which is to create a text file at ~/.cloudvolume/secrets/cave-secret.json with these contents:

{
  "token": "THE API KEY YOU COPIED",
  "fanc_production_mar2021": "THE API KEY YOU COPIED"
}

You can verify that your API key has been saved successfully by running:

import fanc
client = fanc.get_caveclient()

Optional installation steps for additional functionality

Install Elastix to transform neurons into alignment with the VNC template

The mesh manipulation and coordinate transform code requires pytransformix, which is itself a Python wrapper for Elastix. Therefore, Elastix must be installed and its lib and bin paths must be appended to the LD_LIBRARY_PATH and PATH environment variables. See pytransformix documentation for specific instructions.

Provide CATMAID credentials to pull data from CATMAID

You can get your CATMAID API key by logging into https://radagast.hms.harvard.edu/catmaidvnc then hovering over "You are [Your Name]" in the top-right corner, then clicking "Get API token".

Save your CATMAID API key by running:

import fanc
fanc.catmaid.save_catmaid_credentials("YOUR CATMAID API KEY")

You can verify that your API key has been saved successfully by running:

import fanc
fanc.catmaid.connect()

Documentation

• First go through fanc_python_package_examples.ipynb
• Then check out other notebooks in example_notebooks/
• Finally you can browse the code, check out modules that have names that interest you, and read the docstrings.