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CytOpT-0.0.9
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CytOpT
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | CytOpT-0.0.9 |
| نام | CytOpT |
| نسخه کتابخانه | 0.0.9 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | ['Paul Freulon', 'Kalidou BA', 'Boris Hejblum'] |
| ایمیل نویسنده | boris.hejblum@u-bordeaux.fr |
| آدرس صفحه اصلی | - |
| آدرس اینترنتی | https://pypi.org/project/CytOpT/ |
| مجوز | MIT |
# `CytOpT` <a><img src='./tests/Figures/logo.png' align="right" height="139" /></a>
[](https://sistm.github.io/CytOpT-python/_build/html/index.html)
`CytOpT` uses regularized optimal transport to directly estimate the different cell population proportions from a biological sample characterized with flow cytometry measurements.
## Overview
`CytOpT` is a `python` package that provides a new algorithm relying regularized
optimal transport to directly estimate the different cell population proportions
from a biological sample characterized with flow cytometry measurements. Algorithm
is based on the regularized Wasserstein metric to compare cytometry measurements
from different samples, thus accounting for possible mis-alignment of a given cell
population across sample (due to technical variability from the technology of measurements).
The main function of the package is `CytOpT()`.
The methods implemented in this package are detailed in the following
article:
> Paul Freulon, Jérémie Bigot, Boris P. Hejblum.
> CytOpT: Optimal Transport with Domain Adaptation for Interpreting Flow Cytometry data
> https://arxiv.org/abs/2006.09003
## Getting started
Install the `CytOpT` package from pypi as follows:
```
pip install -r requirements.txt
pip install CytOpT # pip3 install CytOpT
```
## Example
### Packages
```
import numpy as np
import pandas as pd
import CytOpT.CytOpt as cytopt
import CytOpT.plots as cplt
```
### Preparing data
```
# Source Data
Stanford1A_values = pd.read_csv('./tests/data/W2_1_values.csv',
usecols=np.arange(1, 8))
Stanford1A_clust = pd.read_csv('./tests/data/W2_1_clust.csv',
usecols=[1])
# Target Data
Stanford3A_values = pd.read_csv('./tests/data/W2_7_values.csv',
usecols=np.arange(1, 8))
Stanford3A_clust = pd.read_csv('./tests/data/W2_7_clust.csv',
usecols=[1])
xSource = np.asarray(Stanford1A_values)
xTarget = np.asarray(Stanford3A_values)
labSource = np.asarray(Stanford1A_clust['x'])
labTarget = np.asarray(Stanford3A_clust['x'])
thetaTrue = np.zeros(10)
for k in range(10):
thetaTrue[k] = np.sum(labTarget == k + 1) / len(labTarget)
```
### Comparison of methods
#### Steps
- Classification using optimal transport with reweighted proportions.
- The target measure 𝛽 is reweighted in order to match the weight vector ℎ̂ estimated with 𝙲𝚢𝚝𝙾𝚙𝚝.
- Approximation of the optimal dual vector u. In order to compute an approximation of the optimal transportation plan, we need to approximate 𝑃𝜀 .
- Class proportions estimation with `𝙲𝚢𝚝𝙾pT()`
- Descent-Ascent procedure (`method="desasc"`)
- Minmax swapping procedure (`method="minmax"`)
```
# Initialization of parameters
nItGrad = 5000
nIter = 10000
nItSto = 10
pas_grad = 10
eps = 0.0001
monitoring = True
# Run Minmax and Desasc
res = cytopt.CytOpT(xSource, xTarget, labSource,thetaTrue=thetaTrue,
method="both", nItGrad=nItGrad, nIter=nIter, nItSto=nItSto,
stepGrad=pas_grad, eps=eps, monitoring=monitoring)
# CytOpT Minmax with default params
cytopt.CytOpT(xSource, xTarget, labSource, thetaTrue=thetaTrue, method='desasc')
# CytOpT Desasc with default params
cytopt.CytOpT(xSource, xTarget, labSource, thetaTrue=thetaTrue, method = 'minmax')
```
#### Plot all results
- ***KLPlot***: Display a bland plot in order to visually assess the agreement between CytOpt estimation
of the class proportions and the estimate of the class proportions provided through manual gating.
- ***barPlot***: Display a bland plot in order to visually assess the agreement between CytOpt estimation
of the class proportions and the estimate of the class proportions provided through manual gating.
- ***BlandAltman***: Display a bland plot in order to visually assess the agreement between CytOpt estimation
of the class proportions and the estimate of the class proportions provided through manual gating.
```
cplt.resultPlot(res, n0=10, nStop=min(nItGrad, nIter))
```
<img src="./tests/Figures/barplot.png" width="100%" />
<img src="./tests/Figures/KL.png" width="100%" />
```
cplt.BlandAltman(res['proportions'])
```
<img src="./tests/Figures/BA_results.png" width="100%" />
#### Bland Altman with Class and Center
```
# CytOpt estimation
Estimate_Prop = pd.read_csv('./tests/data/Res_Estimation_Stan1A.txt',
index_col=0)
# Benchmark estimation
True_Prop = pd.read_csv('./tests/data/True_proportion_Stan1A.txt',
index_col=0)
True_Prop = True_Prop.drop(['Baylor1A'])
Estimate_Prop = Estimate_Prop.drop(['Baylor1A'])
Estimate_Prop = np.asarray(Estimate_Prop)
True_Prop = np.asarray(True_Prop)
Classes = np.tile(np.arange(1, 11), 61)
Centre_1 = np.repeat(['Yale', 'UCLA', 'NHLBI', 'CIMR', 'Miami'], 10)
Centre_2 = np.repeat(['Standford', 'Yale', 'UCLA', 'NHLBI', 'CIMR', 'Baylor', 'Miami'], 10)
Centre = np.hstack((Centre_1, Centre_2, Centre_2, Centre_2,
Centre_2, Centre_2, Centre_2, Centre_2, Centre_2))
props = pd.DataFrame({'GoldStandard': True_Prop.ravel(), 'minmax': Estimate_Prop.ravel()})
cplt.BlandAltman(props, Class=Classes, Center=Centre)
```
<img src="./tests/Figures/BA.png" width="100%" />
#### [You can also look at some other examples with notebooks.](./tests/notebooks/Scripts)
نیازمندی
| مقدار | نام |
|---|---|
| ~=1.21.4 | numpy |
| ~=1.7.2 | scipy |
| ==59.4.0 | setuptools |
| ~=1.3.4 | pandas |
| ~=1.0.1 | scikit-learn |
| ~=3.5.0 | matplotlib |
| ~=0.11.2 | seaborn |
| <5.13 | pyqt5 |
| <5.13 | pyqtwebengine |
| ==0.17.2 | jedi |
| ==0.7.0 | parso |
| - | sphinx |
| - | sphinx-rtd-theme |
نحوه نصب
نصب پکیج whl CytOpT-0.0.9:
pip install CytOpT-0.0.9.whl
نصب پکیج tar.gz CytOpT-0.0.9:
pip install CytOpT-0.0.9.tar.gz