CytOpT 0.0.5

CytOpT

CytOpT

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 = 5000
nItSto = 10
pas_grad = 10
eps = 0.0005
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=4000)

cplt.BlandAltman(res['proportions'])

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)

You can also look at some other examples with notebooks.