Synthesize cells in a given spatial context.
Project description
Region Grower
Synthesize neuronal morphologies in a given spatial context provided by an atlas.
Introduction
This package provides a general tools to synthesize cells in a given spatial context (a brain Atlas) with embarrassingly parallel computation (with distributed computing via dask-mpi). This package is difficult to use on its own, as it requires several specific inputs from atlas and synthesis. It is advised to use it via synthesis-workflow, which is a workflow generating most of the inputs of region-grower.
Installation
This package should be installed using pip:
pip install region-grower
Usage
This package provides one main command which provides several tools. Two tools to generate input parameters and input distributions and another to synthesize the cells.
Generate distributions
Generate the TMD distribution file:
region-grower generate-distributions --help
Input data
The command region-grower generate-distributions
needs the following inputs:
- a folder containing the cells which are used to generate the parameters (see the
input_folder
and--ext
parameters). - a
dat
file (see thedat_file
parameter) with 3 columns:- morphology names,
- any integer value (this column is not used by
region-grower
) - mtypes.
- optionally a
JSON
file containing the specific parameters used for diametrization (see the--diametrizer-config
parameter).
Output data
The command region-grower generate-distributions
will create the following outputs:
- a
JSON
file containing the formatted distributions (see the--parameter-filename
parameter).
Generate parameters
Generate the TMD parameter file:
region-grower generate-parameters --help
Input data
The command region-grower generate-parameters
needs the following inputs:
- a folder containing the cells which are used to generate the parameters (see the
input_folder
and--ext
parameters). - a
dat
file (see thedat_file
parameter) with 3 columns:- morphology names,
- any integer value (this column is not used by
region-grower
) - mtypes.
- optionally a
JSON
file containing the specific parameters used for diametrization (see the--diametrizer-config
parameter).
Output data
The command region-grower generate-parameters
will create the following outputs:
- a
JSON
file containing the formatted parameters (see the--parameter-filename
parameter).
Synthesize cells
Synthesize morphologies into an given atlas according to the given TMD parameters and distributions:
region-grower synthesize-morphologies --help
Input data
The command region-grower synthesize-morphologies
needs the following inputs:
- a
sonata
file containing the positions of the cells that must be synthesized. - a
JSON
file containing the parameters used to synthesize the cells (see the--tmd-parameters
parameter). This file should follow the schema given in :ref:Parameters
. - a
JSON
file containing the distributions used to synthesize the cells (see the--tmd-distributions
parameter). This file should follow the schema given in :ref:Parameters
. - a
TSV
file giving which morphology should be used for axon grafting and the optional scaling factor (see the--morph-axon
parameter). The morphologies referenced in this file should exist in the directory given with the--base-morph-dir
parameter. - a directory containing an Atlas.
Output data
The command region-grower synthesize-morphologies
will create the following outputs:
- a
sonata
file containing all the positions and orientations of the synthesized cells (see--out-cells
parameter). - a directory containing all the synthesized morphologies (see
--out-morph-dir
and--out-morph-ext
parameters). - a
YAML
file containing the apical point positions (see--out-apical
parameter). - a
YAML
file containing the Neuron IDs of the sections containing the apical points (see--out-apical-nrn-sections
parameter).
Funding & Acknowledgment
The development of this software was supported by funding to the Blue Brain Project, a research center of the École polytechnique fédérale de Lausanne (EPFL), from the Swiss government's ETH Board of the Swiss Federal Institutes of Technology.
Copyright (c) 2022-2024 Blue Brain Project/EPFL
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