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aicscytoparam-0.1.6
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Cytoplasm parameterization using spherical harmonics
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | aicscytoparam-0.1.6 |
| نام | aicscytoparam |
| نسخه کتابخانه | 0.1.6 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Matheus Viana |
| ایمیل نویسنده | matheus.viana@alleninstitute.org |
| آدرس صفحه اصلی | https://github.com/AllenCell/aics-cytoparam |
| آدرس اینترنتی | https://pypi.org/project/aicscytoparam/ |
| مجوز | Allen Institute Software License |
# 3D Cell Parameterization
[](https://github.com/AllenCell/aics-cytoparam/actions)
[](https://AllenCell.github.io/aics-cytoparam/)
[](https://codecov.io/gh/AllenCell/aics-cytoparam)
### Spherical harmonics coefficients-based parameterization of the cytoplasm and nucleoplasm for 3D cells
---
## Installation
**Stable Release:** `pip install aicscytoparam`<br>
**Development Head:** `pip install git+https://github.com/AllenCell/aics-cytoparam.git`
## How to use
Here we outline an example of how to use `aicscytoparam` to create a parameterization of a 3D cell. In this case, the 3D cells will be represented by a cell segementation, nuclear segmentation and a fluorescent protein (FP) image representing the fluorescent signal of a tagged protein.
```python
# Import required packages
import numpy as np
import matplotlib.pyplot as plt
from aicscytoparam import cytoparam
from skimage import morphology as skmorpho
```
```python
# First create a cuboid cell with an off-center cuboid nucleus
# and get the spherical harmonics coefficients of this cell and nucleus:
w = 100
mem = np.zeros((w, w, w), dtype = np.uint8)
mem[20:80, 20:80, 20:80] = 1
nuc = np.zeros((w, w, w), dtype = np.uint8)
nuc[40:60, 40:60, 30:50] = 1
# Create an FP signal located in the top half of the cell and outside the
# nucleus:
gfp = np.random.rand(w**3).reshape(w,w,w)
gfp[mem==0] = 0
gfp[:, w//2:] = 0
gfp[nuc>0] = 0
# Vizualize a center xy cross-section of our cell:
plt.imshow((mem + nuc)[w//2], cmap='gray')
plt.imshow(gfp[w // 2], cmap='gray', alpha=0.25)
plt.axis('off')
```
```python
# Use aicsshparam to expand both cell and nuclear shapes in terms of spherical
# harmonics:
coords, coeffs_centroid = cytoparam.parameterize_image_coordinates(
seg_mem=mem,
seg_nuc=nuc,
lmax=16, # Degree of the spherical harmonics expansion
nisos=[32, 32] # Number of interpolation layers
)
coeffs_mem, centroid_mem, coeffs_nuc, centroid_nuc = coeffs_centroid
# Run the cellular mapping to create a parameterized intensity representation
# for the FP image:
gfp_representation = cytoparam.cellular_mapping(
coeffs_mem=coeffs_mem,
centroid_mem=centroid_mem,
coeffs_nuc=coeffs_nuc,
centroid_nuc=centroid_nuc,
nisos=[32, 32],
images_to_probe=[('gfp', gfp)]
).data.squeeze()
# The FP image is now encoded into a representation of its shape:
print(gfp_representation.shape)
```
`(65, 8194)`
```python
# Now we want to morph the FP image into a round cell.
# First we create the round cell:
from skimage import morphology as skmorpho
mem_round = skmorpho.ball(w // 3) # radius of our round cell
nuc_round = skmorpho.ball( w// 3) # radius of our round nucleus
# Erode the nucleus so it becomes smaller than the cell
nuc_round = skmorpho.binary_erosion(
nuc_round, selem=np.ones((20, 20, 20))
).astype(np.uint8)
# Vizualize a center xy cross-section of our round cell:
plt.imshow((mem_round + nuc_round)[w // 3], cmap='gray')
plt.axis('off')
```
```python
# Next we need to parameterize the coordinates of our round
# cell:
coords_round, _ = cytoparam.parameterize_image_coordinates(
seg_mem=mem_round,
seg_nuc=nuc_round,
lmax=16,
nisos=[32, 32]
)
# Now we are ready to morph the FP image into our round cell:
gfp_morphed = cytoparam.morph_representation_on_shape(
img=mem_round + nuc_round,
param_img_coords=coords_round,
representation=gfp_representation
)
# Visualize the morphed FP image:
plt.imshow((mem_round + nuc_round)[w // 3], cmap='gray')
plt.imshow(gfp_morphed[w // 3], cmap='gray', alpha=0.25)
plt.axis('off')
```
## Reference
For an example of how this package was used to analyse a dataset of over 200k single-cell images at the Allen Institute for Cell Science, please check out our paper in [bioaRxiv](https://www.biorxiv.org/content/10.1101/2020.12.08.415562v1).
## Development
See [CONTRIBUTING.md](CONTRIBUTING.md) for information related to developing the code.
## Questions?
If you have any questions, feel free to leave a comment in our Allen Cell forum: [https://forum.allencell.org/](https://forum.allencell.org/).
***Free software: Allen Institute Software License***
نیازمندی
| مقدار | نام |
|---|---|
| >=0.0.14 | aicsshparam |
| - | tqdm |
| - | numpy |
| >=9.0.0 | vtk |
| - | aicsimageio |
| - | scipy |
| >=0.15.0 | scikit-image |
| >=0.0.14 | aicsshparam |
| - | tqdm |
| - | numpy |
| >=9.0.0 | vtk |
| - | aicsimageio |
| - | scipy |
| >=0.15.0 | scikit-image |
| >=5.2 | pytest-runner |
| >=19.10b0 | black |
| >=2.1.4 | codecov |
| >=3.8.3 | flake8 |
| >=3.2.1 | flake8-debugger |
| >=5.4.3 | pytest |
| >=2.9.0 | pytest-cov |
| >=0.11 | pytest-raises |
| >=1.0.1 | bump2version |
| >=5.1 | coverage |
| >=7.15.0 | ipython |
| >=0.2.7 | m2r2 |
| >=3.4.3 | Sphinx |
| >=0.5.1 | sphinx-rtd-theme |
| >=3.15.2 | tox |
| >=3.1.1 | twine |
| >=0.34.2 | wheel |
| >=5.2 | pytest-runner |
| >=19.10b0 | black |
| >=2.1.4 | codecov |
| >=3.8.3 | flake8 |
| >=3.2.1 | flake8-debugger |
| >=5.4.3 | pytest |
| >=2.9.0 | pytest-cov |
| >=0.11 | pytest-raises |
| >=1.0.1 | bump2version |
| >=5.1 | coverage |
| >=7.15.0 | ipython |
| >=0.2.7 | m2r2 |
| >=3.4.3 | Sphinx |
| >=0.5.1 | sphinx-rtd-theme |
| >=3.15.2 | tox |
| >=3.1.1 | twine |
| >=0.34.2 | wheel |
| >=5.2 | pytest-runner |
| >=19.10b0 | black |
| >=2.1.4 | codecov |
| >=3.8.3 | flake8 |
| >=3.2.1 | flake8-debugger |
| >=5.4.3 | pytest |
| >=2.9.0 | pytest-cov |
| >=0.11 | pytest-raises |
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.7 | Python |
نحوه نصب
نصب پکیج whl aicscytoparam-0.1.6:
pip install aicscytoparam-0.1.6.whl
نصب پکیج tar.gz aicscytoparam-0.1.6:
pip install aicscytoparam-0.1.6.tar.gz