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emcaps-1.0.0
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Code for the paper Genetically encoded barcodes for correlative volume electron microscopy
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | emcaps-1.0.0 |
| نام | emcaps |
| نسخه کتابخانه | 1.0.0 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | - |
| ایمیل نویسنده | - |
| آدرس صفحه اصلی | - |
| آدرس اینترنتی | https://pypi.org/project/emcaps/ |
| مجوز | MIT License Copyright (c) 2021 - 2022 Martin Drawitsch Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. |
 
# Source code for "Genetically encoded barcodes for correlative volume electron microscopy"
This repository contains all code for the sequential ML pipeline of the paper [Genetically encoded barcodes for correlative volume electron microscopy](https://www.nature.com/articles/s41587-023-01713-y).
Pretrained models can be found as release artifacts [here](https://github.com/StructuralNeurobiologyLab/emcaps/releases/tag/models). They are automatically downloaded and cached when needed by the code.
## Installation
### Option 1: From PyPI (with `pip`)
You can install the project with scripts and all dependencies by running
pip install emcaps
Notes:
- It is recommended to use a new [virtual environment](https://virtualenv.pypa.io/en/latest/) for this.
- If you encounter PyTorch issues with this setup, please [install PyTorch manually following the official instructions](https://pytorch.org/get-started/locally/)
- If you want to use the napari-based GUI, you will also need to [install one of qtpy's supported Qt backends](https://github.com/spyder-ide/qtpy/blob/master/README.md#requirements), for example PyQt5:
pip install pyqt5
This has to be done manually since none of the backends is compatible with all platforms and pip can't auto-select an optimal one.
### Option 2: From sources (with `pip` or `conda`)
First obtain the [project sources](https://github.com/StructuralNeurobiologyLab/emcaps) (either clone or [download zip](https://github.com/StructuralNeurobiologyLab/emcaps/archive/refs/heads/master.zip) and extract) and `cd` to the project root.
If you want to install all dependencies and the package itself with `pip`, just run
pip install .
Alternatively, if you want to install the dependencies with `conda`, run the following commands:
conda env create -f environment.yml
conda activate emcaps
pip install .
## Running the code
All scripts can be executed from the project root directory using `python3 -m`, for example:
$ python3 -m emcaps.inference.segment -h
Alternatively you can use the entry points provided by the pip installation:
$ emcaps-segment -h
## Entry points for testing on custom data
These entry points just require raw images and don't require GPU resources. Labels are not needed.
### Napari-based interactive GUI tool for segmentation and EMcapsulin particle classification
$ emcaps-encari
or
$ python3 -m emcaps.analysis.encari
### Performing batch inference on a directory of images or single image files
$ emcaps-segment segment.inp_path=<PATH_TO_FILE_OR_FOLDER>
or
$ python3 -m emcaps.inference.segment segment.inp_path=<PATH_TO_FILE_OR_FOLDER>
## Entry points for reproduction, retraining or evaluation
The following steps require a local copy of the [official dataset](#dataset) or a dataset in the same structure. A GPU is highly recommended.
### Splitting labeled image dataset into training and validation images and normalizing the data format
$ emcaps-splitdataset
or
$ python3 -m emcaps.utils.splitdataset
### Training new segmentation models
$ emcaps-segtrain
or
$ python3 -m emcaps.training.segtrain
### Segmentation inference and evaluation
Segment and optionally also perform particle-level classification if a model is available, render output visualizations (colored classification overlays etc.) and compute segmentation metrics.
$ emcaps-segment
or
$ python3 -m emcaps.inference.segment
For a usage example featuring config sweeps, see `_scripts/seg_cls_test.sh`
### Producing a patch dataset based on image segmentation
Based on segmentation (from a model or human annotation), extract particle-centered image patches and store them as separate files in addition to metadata. The resulting patch dataset can be used for training models for patch-based classification. In addition, A random sample of the validation patches is prepared for evaluation of human and model-based classification evaluation.
$ emcaps-patchifyseg
or
$ python3 -m emcaps.inference.patchifyseg
### Training new patch classifiers
Requires the outputs of `patchifyseg` (see above).
$ emcaps-patchtrain
or
$ python3 -m emcaps.training.patchtrain
### Quantitative evaluation of patch classification results
Requires the outputs of `patchifyseg` (see above).
$ emcaps-patcheval
or
$ python3 -m emcaps.inference.patcheval
For a usage example featuring config sweeps, see `_scripts/patcheval.sh`
### Rendering average images of patch collections and grouping patches by EMcapsulin types
Requires the outputs of `patchifyseg` (see above).
$ emcaps-averagepatches
or
$ python3 -m emcaps.analysis.averagepatches
## Configuration system
We are using a common configuration system for the runnable code, based on [Hydra](https://hydra.cc/docs/1.2/intro/) and [OmegaConf](https://omegaconf.readthedocs.io/en/2.2_branch/).
A central default config file with explanatory comments is located at `conf/conf.yaml`.
It is written to be as automatic and minimal as possible, but it can still be necessary to change some of the values for experiments or adapting to a different system.
For the syntax of such yaml-based config files please refer to the OmegaConf docs on [access and manipulation](https://omegaconf.readthedocs.io/en/2.2_branch/usage.html#access-and-manipulation) and [variable interpolation](https://omegaconf.readthedocs.io/en/2.2_branch/usage.html#variable-interpolation)
For running hydra-enabled code with custom configuration you can either point to a different config file with the `-cp` [CLI flag](https://hydra.cc/docs/1.2/advanced/hydra-command-line-flags/) or change config values directly on the CLI using [Hydra's override syntax](https://hydra.cc/docs/1.2/advanced/override_grammar/basic/)
## Dataset
If you want to train own models and/or do quantitative evaluation on the official data, please find the data [here](https://drive.google.com/drive/folders/1S-dwZx0kHY3HuIiAXMyFlezsfOJmwYut?usp=share_link) and extract it to `~/emc/emcapsulin`.
## Further notes
For more details see top-level docstrings in each file.
نیازمندی
| مقدار | نام |
|---|---|
| <0.2.0,>=0.1.0 | elektronn3 |
| >=1.0.3 | albumentations |
| >=1.2.0 | hydra-core |
| >=2.19.3 | imageio |
| >=8.4.0 | ipython |
| >=3.5.2 | matplotlib |
| >=1.22.4 | numpy |
| >=2.2.1 | omegaconf |
| >=1.4.3 | pandas |
| >=9.1.1 | Pillow |
| >=6.0 | PyYAML |
| >=0.19.3 | scikit-image |
| >=1.1.1 | scikit-learn |
| >=1.8.1 | scipy |
| >=0.11.2 | seaborn |
| >=62.6.0 | setuptools |
| >=1.12.1 | torch |
| >=4.64.0 | tqdm |
| >=4.2.0 | typing-extensions |
| >=1.1.2 | ubelt |
| >=3.0 | openpyxl |
| >=0.5.1 | magicgui |
| >=0.4.16 | napari |
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.10 | Python |
نحوه نصب
نصب پکیج whl emcaps-1.0.0:
pip install emcaps-1.0.0.whl
نصب پکیج tar.gz emcaps-1.0.0:
pip install emcaps-1.0.0.tar.gz