Package 'CytOpT' reference manual

Title:	Optimal Transport for Gating Transfer in Cytometry Data with Domain Adaptation
Description:	Supervised learning from a source distribution (with known segmentation into cell sub-populations) to fit a target distribution with unknown segmentation. It relies regularized optimal transport to directly estimate the different cell population proportions from a biological sample characterized with flow cytometry measurements. It 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). Supervised learning technique based on the Wasserstein metric that is used to estimate an optimal re-weighting of class proportions in a mixture model Details are presented in Freulon P, Bigot J and Hejblum BP (2021) <arXiv:2006.09003>.
Authors:	Boris Hejblum [aut, cre], Paul Freulon [aut], Kalidou Ba [aut, trl]
Maintainer:	Boris Hejblum <[email protected]>
License:	GPL (>= 2)
Version:	0.9.4
Built:	2024-09-16 03:01:40 UTC
Source:	https://github.com/sistm/cytopt-r

Function to 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.

Description

Function to 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.

Usage

barplot_prop(proportions, title = "", xaxis_angle = 45)
barplot_prop(proportions, title = "", xaxis_angle = 45)

Arguments

`proportions`	`data.frame` of (true and) estimated proportions from `CytOpt()`
`title`	plot title. Default is `""`, i.e. no title.
`xaxis_angle`	scalar indicating an angle to tilt the labels of x_axis. Default is `45`.

Value

a ggplot object

Examples

if(interactive()){

res <- CytOpT(X_s = HIPC_Stanford_1228_1A, X_t = HIPC_Stanford_1369_1A, 
             Lab_source = HIPC_Stanford_1228_1A_labels,
             eps = 0.0001, lbd = 0.0001, n_iter = 10000, n_stoc=10,
             step_grad = 10, step = 5, power = 0.99, 
             method='minmax')
barplot_prop(res$proportions)

}
if(interactive()){

res <- CytOpT(X_s = HIPC_Stanford_1228_1A, X_t = HIPC_Stanford_1369_1A, 
             Lab_source = HIPC_Stanford_1228_1A_labels,
             eps = 0.0001, lbd = 0.0001, n_iter = 10000, n_stoc=10,
             step_grad = 10, step = 5, power = 0.99, 
             method='minmax')
barplot_prop(res$proportions)

}

Bland & Altman plot

Description

Function to display a Bland & Altman 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. Requires that either theta_true or Lab_target was provided when running CytOpT().

Usage

Bland_Altman(proportions, additional_info_shape = NULL)
Bland_Altman(proportions, additional_info_shape = NULL)

Arguments

proportions

data.frame of true and estimated proportion returned from CytOpT().

additional_info_shape

vector of additional information to be used for shape in the plot. Not implemented yet.

#'@return a ggplot object

Examples

if(interactive()){

gold_standard_manual_prop <- c(table(HIPC_Stanford_1369_1A_labels) /
 length(HIPC_Stanford_1369_1A_labels))
res <- CytOpT(X_s = HIPC_Stanford_1228_1A, X_t = HIPC_Stanford_1369_1A, 
             Lab_source = HIPC_Stanford_1228_1A_labels,
             theta_true = gold_standard_manual_prop,
             eps = 0.0001, lbd = 0.0001, n_iter = 10000, n_stoc=10,
             step_grad = 10, step = 5, power = 0.99, 
             method='both')
Bland_Altman(res$proportions)

}
if(interactive()){

gold_standard_manual_prop <- c(table(HIPC_Stanford_1369_1A_labels) /
 length(HIPC_Stanford_1369_1A_labels))
res <- CytOpT(X_s = HIPC_Stanford_1228_1A, X_t = HIPC_Stanford_1369_1A, 
             Lab_source = HIPC_Stanford_1228_1A_labels,
             theta_true = gold_standard_manual_prop,
             eps = 0.0001, lbd = 0.0001, n_iter = 10000, n_stoc=10,
             step_grad = 10, step = 5, power = 0.99, 
             method='both')
Bland_Altman(res$proportions)

}

Function to estimate the type cell proportions in an unclassified cytometry data set denoted X_s by using the classification Lab_source from an other cytometry data set X_s. With this function the computation of the estimate of the class proportions is done with a descent ascent or minmax or two algorithms.

Description

Function to estimate the type cell proportions in an unclassified cytometry data set denoted X_s by using the classification Lab_source from an other cytometry data set X_s. With this function the computation of the estimate of the class proportions is done with a descent ascent or minmax or two algorithms.

Usage

CytOpT(
  X_s,
  X_t,
  Lab_source,
  Lab_target = NULL,
  theta_true = NULL,
  method = c("minmax", "desasc", "both"),
  eps = 1e-04,
  n_iter = 10000,
  power = 0.99,
  step_grad = 10,
  step = 5,
  lbd = 1e-04,
  n_out = 5000,
  n_stoc = 10,
  minMaxScaler = TRUE,
  monitoring = FALSE,
  thresholding = TRUE
)
CytOpT(
  X_s,
  X_t,
  Lab_source,
  Lab_target = NULL,
  theta_true = NULL,
  method = c("minmax", "desasc", "both"),
  eps = 1e-04,
  n_iter = 10000,
  power = 0.99,
  step_grad = 10,
  step = 5,
  lbd = 1e-04,
  n_out = 5000,
  n_stoc = 10,
  minMaxScaler = TRUE,
  monitoring = FALSE,
  thresholding = TRUE
)

Arguments

`X_s`	a cytometry dataframe with only `d` numerical variables for `ns` observations. The columns correspond to the different biological markers measured. One line corresponds to the cytometry measurements performed on one cell. The classification of this Cytometry data set must be provided with the Lab_source parameters.
`X_t`	a cytometry dataframe with only `d` numerical variables for `nt` observations. The columns correspond to the different biological markers measured. One line corresponds to the cytometry measurements performed on one cell. The CytOpT algorithm targets the cell type proportion in this Cytometry data set
`Lab_source`	a vector of length `ns` Classification of the X_s cytometry data set
`Lab_target`	a vector of length `nt` Classification of the X_s cytometry data set
`theta_true`	If available, gold-standard proportions in the target data set `X_t` derived from manual gating. It allows to assess the gap between the estimate and the gold-standard. Default is `NULL`, in which case no assessment is performed.
`method`	a character string indicating which method to use to compute the cytopt, either `'minmax'`, `'desasc'` or `'both'` for comparing both Min-max swapping and descent-ascent procedures. Default is `'minmax'`.
`eps`	a float value of regularization parameter of the Wasserstein distance. Default is `1e-04`
`n_iter`	an integer Constant that iterate method select. Default is `10000`
`power`	a float constant the step size policy of the gradient ascent method is step/n^power. Default is `0.99`
`step_grad`	an integer number step size of the gradient descent algorithm of the outer loop. Default is `10`
`step`	an integer constant that multiply the step-size policy. Default is `5`
`lbd`	a float constant that multiply the step-size policy. Default is `1e-04`
`n_out`	an integer number of iterations in the outer loop. This loop corresponds to the gradient descent algorithm to minimize the regularized Wasserstein distance between the source and target data sets. Default is `1000`
`n_stoc`	an integer number of iterations in the inner loop. This loop corresponds to the stochastic algorithm that approximates a maximizer of the semi dual problem. Default is `10`
`minMaxScaler`	a logical flag indicating to whether to scale observations between 0 and 1. Default is `TRUE`.
`monitoring`	a logical flag indicating to possibly monitor the gap between the estimated proportions and the manual gold-standard. Default is `FALSE`.
`thresholding`	a logical flag indicating whether to threshold negative values. Default is `TRUE`.

Value

a object of class CytOpt, which is a list of two elements:

proportions a data.frame with the (optionally true and) estimated proportions for each method
monitoring a list of estimates over the optimization iterations for each method (listed within)

Examples

if(interactive()){

res <- CytOpT(X_s = HIPC_Stanford_1228_1A, X_t = HIPC_Stanford_1369_1A, 
             Lab_source = HIPC_Stanford_1228_1A_labels,
             method='minmax')
summary(res)
plot(res)

}
if(interactive()){

res <- CytOpT(X_s = HIPC_Stanford_1228_1A, X_t = HIPC_Stanford_1369_1A, 
             Lab_source = HIPC_Stanford_1228_1A_labels,
             method='minmax')
summary(res)
plot(res)

}

Function to estimate the type cell proportions in an unclassified cytometry data set denoted X_s by using the classification Lab_source from an other cytometry data set X_s. With this function the computation of the estimate of the class proportions is done with a descent ascent algorithm.

Description

Usage

cytopt_desasc_r(
  X_s,
  X_t,
  Lab_source,
  theta_true = NULL,
  eps = 1e-04,
  n_out = 5000,
  n_stoc = 10,
  step_grad = 10,
  monitoring = FALSE
)
cytopt_desasc_r(
  X_s,
  X_t,
  Lab_source,
  theta_true = NULL,
  eps = 1e-04,
  n_out = 5000,
  n_stoc = 10,
  step_grad = 10,
  monitoring = FALSE
)

Arguments

`X_s`	a cytometry dataframe. The columns correspond to the different biological markers tracked. One line corresponds to the cytometry measurements performed on one cell. The classification of this Cytometry data set must be provided with the Lab_source parameters.
`X_t`	a cytometry dataframe. The columns correspond to the different biological markers tracked. One line corresponds to the cytometry measurements performed on one cell. The CytOpT algorithm targets the cell type proportion in this Cytometry data set
`Lab_source`	a vector of length `n` Classification of the X_s cytometry data set
`theta_true`	If available, gold-standard proportions in the target data set `X_t` derived from manual gating. It allows to assess the gap between the estimate and the gold-standard. Default is `NULL`, in which case no assessment is performed.
`eps`	an float value of regularization parameter of the Wasserstein distance. Default is `1e-04`.
`n_out`	an integer number of iterations in the outer loop. This loop corresponds to the gradient descent algorithm to minimize the regularized Wasserstein distance between the source and target data sets. Default is `5000`.
`n_stoc`	an integer number of iterations in the inner loop. This loop corresponds to the stochastic algorithm that approximates a maximizer of the semi-dual problem. Default is `10`.
`step_grad`	an integer number step size of the gradient descent algorithm of the outer loop. Default is `10`.
`monitoring`	boolean indicating whether Kullback-Leibler divergence should be monitored and store throughout the optimization iterations. Default is `FALSE`.

Value

A list with the following elements:h_hat

Function to estimate the type cell proportions in an unclassified cytometry data set denoted X_s by using the classification Lab_source from an other cytometry data set X_s. With this function an additional regularization parameter on the class proportions enables a faster computation of the estimator.

Description

Function to estimate the type cell proportions in an unclassified cytometry data set denoted X_s by using the classification Lab_source from an other cytometry data set X_s. With this function an additional regularization parameter on the class proportions enables a faster computation of the estimator.

Usage

cytopt_minmax_r(
  X_s,
  X_t,
  Lab_source,
  theta_true = NULL,
  eps = 1e-04,
  lbd = 1e-04,
  n_iter = 10000,
  step = 5,
  power = 0.99,
  monitoring = FALSE
)
cytopt_minmax_r(
  X_s,
  X_t,
  Lab_source,
  theta_true = NULL,
  eps = 1e-04,
  lbd = 1e-04,
  n_iter = 10000,
  step = 5,
  power = 0.99,
  monitoring = FALSE
)

Arguments

`X_s`	Cytometry data set. The columns correspond to the different biological markers tracked. One line corresponds to the cytometry measurements performed on one cell. The classification of this Cytometry data set must be provided with the Lab_source parameters.
`X_t`	Cytometry data set. The columns correspond to the different biological markers tracked. One line corresponds to the cytometry measurements performed on one cell. The CytOpT algorithm targets the cell type proportion in this Cytometry data set.
`Lab_source`	Classification of the X_s Cytometry data set
`theta_true`	If available, gold-standard proportions in the target data set `X_t` derived from manual gating. It allows to assess the gap between the estimate and the gold-standard. Default is `NULL`, in which case no assessment is performed.
`eps`	Regularization parameter of the Wasserstein distance
`lbd`	an float constant that multiply the step-size policy. Default is `1e-04`.
`n_iter`	an integer Constant that iterate method select. Default is `10000`.
`step`	Constant that multiply the step-size policy. Default is `5`.
`power`	the step size policy of the gradient ascent method is step/n^power. Default is `0.99`.
`monitoring`	boolean indicating whether Kullback-Leibler divergence should be monitored and store throughout the optimization iterations. Default is `FALSE`.

Value

A list with the following elements:Results_Minmax

HIPC_Stanford data

Description

HIPC T cell data set from HIPC program for patients 1228 and 1369 (replicate 1A from Stanford).

Usage

data(HIPC_Stanford)
data(HIPC_Stanford)

Format

The data are composed of 4 objects:

HIPC_Stanford_1228_1A:: a data.frame of 31342 cells and 7 markers.
HIPC_Stanford_1228_1A_labels:: a factor vector with the cell type of each of the 31342 observed cells.
HIPC_Stanford_1369_1A:: a data.frame of 33992 cells and 7 markers.
HIPC_Stanford_1369_1A_labels:: a factor vector with the cell type of each of the 33992 observed cells.

Details

This immunophenotyping T cell panel from the Lyoplate HIPC dataset was used as part of the FlowCAP III Lyoplate challenge.

Flow cytometry data set from the HIPC T-cell panel study. In the HIPC T-cell panel study, Flow cytometry was measured in 3 samples for each 3 patients (IDs: 1228, 1349 and 1369) with 3 replicates each (1A, 2B and 3C) in 7 centers (NHLBI, Yale, UCLA, CIMR, Baylor, Stanford and Miami), i.e. 63 data sets in total. Manual gating was performed in the different centers to cluster te observed cells into one of 10 cellular populations:

CD8 Effector
CD8 Naive
CD8 Central Memory
CD8 Effector Memory
CD8 Activated
CD4 Effector
CD4 Naive
CD4 Central Memory
CD4 Effector Memory
CD4 Activated

Source

https://www.immuneprofiling.org/hipc/page/show https://www.immunespace.org/ https://www.immunespace.org/project/HIPC/Lyoplate/begin.view?pageId=study.DATA_ANALYSIS

References

Maecker HT, McCoy JP & Nussenblatt R (2012). Standardizing immunophenotyping for the human immunology project. Nature Reviews Immunology, 12(3):191–200. DOI: 10.1038/nri3158

Finak G, Langweiler M, Jaimes M, Malek M, Taghiyar J, Korin Y, Raddassi K, Devine L, Obermoser G, Pekalski ML, Pontikos N, Diaz A, Heck S, Villanova F, Terrazzini N, Kern F, Qian Y, Stanton R, Wang K, Brandes A, Ramey J, Aghaeepour N, Mosmann T, Scheuermann RH, Reed E, Palucka K, Pascual V, Blomberg BB, Nestle F, Nussenblatt RB, Brinkman RR, Gottardo R, Maecker H & McCoy JP (2016). Standardizing Flow Cytometry Immunophenotyping Analysis from the Human ImmunoPhenotyping Consortium. Scientific Reports. 10(6):20686. DOI: 10.1038/srep20686.

Kullback-Leibler divergence plot

Description

A plotting function for displaying Kullback-Liebler (KL) divergence across iterations of the optimization algorithm(s).

Usage

KL_plot(
  monitoring,
  n_0 = 10,
  n_stop = 1000,
  title = "Kullback-Liebler divergence trace"
)
KL_plot(
  monitoring,
  n_0 = 10,
  n_stop = 1000,
  title = "Kullback-Liebler divergence trace"
)

Arguments

`monitoring`	`list` of monitoring estimates from `CytOpt()` output.
`n_0`	first iteration to plot. Default is 10.
`n_stop`	last iteration to plot. Default is 1000.
`title`	plot title. Default is `"Kullback-Liebler divergence trace"`.

Value

a ggplot object

Examples


if(interactive()){

gold_standard_manual_prop <- c(table(HIPC_Stanford_1369_1A_labels) / 
 length(HIPC_Stanford_1369_1A_labels))
res <- CytOpT(X_s = HIPC_Stanford_1228_1A, X_t = HIPC_Stanford_1369_1A, 
             Lab_source = HIPC_Stanford_1228_1A_labels,
             theta_true = gold_standard_manual_prop,
             eps = 0.0001, lbd = 0.0001, n_iter = 10000, n_stoc=10,
             step_grad = 10, step = 5, power = 0.99, 
             method='both', monitoring = TRUE)
plot(res)

}
if(interactive()){

gold_standard_manual_prop <- c(table(HIPC_Stanford_1369_1A_labels) / 
 length(HIPC_Stanford_1369_1A_labels))
res <- CytOpT(X_s = HIPC_Stanford_1228_1A, X_t = HIPC_Stanford_1369_1A, 
             Lab_source = HIPC_Stanford_1228_1A_labels,
             theta_true = gold_standard_manual_prop,
             eps = 0.0001, lbd = 0.0001, n_iter = 10000, n_stoc=10,
             step_grad = 10, step = 5, power = 0.99, 
             method='both', monitoring = TRUE)
plot(res)

}

Computes a classification on the target data

Description

Computes a classification on the target data thanks to the approximation of the transport plan and the classification of the source data. Transport plan is approximated with the stochastic algorithm.

Usage

Label_Prop_sto_r(
  X_s,
  X_t,
  Lab_source,
  eps = 1e-04,
  const = 0.1,
  n_iter = 4000,
  minMaxScaler = TRUE,
  monitoring = TRUE,
  thresholding = TRUE
)
Label_Prop_sto_r(
  X_s,
  X_t,
  Lab_source,
  eps = 1e-04,
  const = 0.1,
  n_iter = 4000,
  minMaxScaler = TRUE,
  monitoring = TRUE,
  thresholding = TRUE
)

Arguments

`X_s`	a cytometry dataframe. The columns correspond to the different biological markers tracked. One line corresponds to the cytometry measurements performed on one cell. The classification of this Cytometry data set must be provided with the Lab_source parameters.
`X_t`	a cytometry dataframe. The columns correspond to the different biological markers tracked. One line corresponds to the cytometry measurements performed on one cell. The CytOpT algorithm targets the cell type proportion in this Cytometry data set
`Lab_source`	a vector of length `n` Classification of the X_s cytometry data set
`eps`	an float value of regularization parameter of the Wasserstein distance. Default is `1e-04`
`const`	an float constant. Default is `1e-01`
`n_iter`	an integer Constant that iterate method select. Default is `4000`
`minMaxScaler`	a logical flag indicating to possibly Scaler
`monitoring`	a logical flag indicating to possibly monitor the gap between the estimated proportions and the manual gold-standard. Default is `FALSE`
`thresholding`	a logical flag.

Value

a ggplot object

a vector of length nrow(X_t) with the propagated labels

Examples


if(interactive()){

res <- Label_Prop_sto_r(X_s = HIPC_Stanford_1228_1A, X_t = HIPC_Stanford_1369_1A, 
             Lab_source = HIPC_Stanford_1228_1A_labels)

}

if(interactive()){

res <- Label_Prop_sto_r(X_s = HIPC_Stanford_1228_1A, X_t = HIPC_Stanford_1369_1A, 
             Lab_source = HIPC_Stanford_1228_1A_labels)

}

CytOpt plot

Description

plot S3 method for CytOpt object

Usage

## S3 method for class 'CytOpt'
plot(x, ...)
## S3 method for class 'CytOpt'
plot(x, ...)

Arguments

`x`	an object of class `CytOpt` to plot.
`...`	further arguments passed to or from other methods. Not implemented.

Value

a ggplot object, potentially composed through patchwork

Examples

if(interactive()){

res <- CytOpT(X_s = HIPC_Stanford_1228_1A, X_t = HIPC_Stanford_1369_1A, 
             Lab_source = HIPC_Stanford_1228_1A_labels,
             eps = 0.0001, lbd = 0.0001, n_iter = 10000, n_stoc=10,
             step_grad = 10, step = 5, power = 0.99, 
             method='minmax')
plot(res)

}
if(interactive()){

res <- CytOpT(X_s = HIPC_Stanford_1228_1A, X_t = HIPC_Stanford_1369_1A, 
             Lab_source = HIPC_Stanford_1228_1A_labels,
             eps = 0.0001, lbd = 0.0001, n_iter = 10000, n_stoc=10,
             step_grad = 10, step = 5, power = 0.99, 
             method='minmax')
plot(res)

}

CytOpt print

Description

print S3 method for CytOpt object

Usage

## S3 method for class 'CytOpt'
print(x, ...)
## S3 method for class 'CytOpt'
print(x, ...)

Arguments

`x`	an object of class `CytOpt` to print.
`...`	further arguments passed to or from other methods. Not implemented.

Value

the proportions data.frame from x

Examples

if(interactive()){

res <- CytOpT(X_s = HIPC_Stanford_1228_1A, X_t = HIPC_Stanford_1369_1A, 
             Lab_source = HIPC_Stanford_1228_1A_labels,
             eps = 0.0001, lbd = 0.0001, n_iter = 10000, n_stoc=10,
             step_grad = 10, step = 5, power = 0.99, 
             method='minmax')
print(res)

}
if(interactive()){

res <- CytOpT(X_s = HIPC_Stanford_1228_1A, X_t = HIPC_Stanford_1369_1A, 
             Lab_source = HIPC_Stanford_1228_1A_labels,
             eps = 0.0001, lbd = 0.0001, n_iter = 10000, n_stoc=10,
             step_grad = 10, step = 5, power = 0.99, 
             method='minmax')
print(res)

}

CytOpt print summary

Description

print S3 method for summary.CytOpt object

Usage

## S3 method for class 'summary.CytOpt'
print(x, ...)
## S3 method for class 'summary.CytOpt'
print(x, ...)

Arguments

`x`	an object of class `summary.CytOpt` to print.
`...`	further arguments passed to or from other methods. Not implemented.

CytOpt summary

Description

summary S3 method for CytOpt object

Usage

## S3 method for class 'CytOpt'
summary(object, ...)
## S3 method for class 'CytOpt'
summary(object, ...)

Arguments

`object`	an object of class `CytOpt` to summarized.
`...`	further arguments passed to or from other methods. Not implemented.

Value

a list object

Examples

if(interactive()){

res <- CytOpT(X_s = HIPC_Stanford_1228_1A, X_t = HIPC_Stanford_1369_1A, 
             Lab_source = HIPC_Stanford_1228_1A_labels,
             eps = 0.0001, lbd = 0.0001, n_iter = 10000, n_stoc=10,
             step_grad = 10, step = 5, power = 0.99, 
             method='minmax', monitoring=TRUE)
summary(res)

}
if(interactive()){

res <- CytOpT(X_s = HIPC_Stanford_1228_1A, X_t = HIPC_Stanford_1369_1A, 
             Lab_source = HIPC_Stanford_1228_1A_labels,
             eps = 0.0001, lbd = 0.0001, n_iter = 10000, n_stoc=10,
             step_grad = 10, step = 5, power = 0.99, 
             method='minmax', monitoring=TRUE)
summary(res)

}

Package 'CytOpT'

Help Index

Function to 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.

Description

Usage

Arguments

Value

Examples

Bland & Altman plot

Description

Usage

Arguments

See Also

Examples

Description

Usage

Arguments

Value

Examples

Function to estimate the type cell proportions in an unclassified cytometry data set denoted X_s by using the classification Lab_source from an other cytometry data set X_s. With this function the computation of the estimate of the class proportions is done with a descent ascent algorithm.

Description

Usage

Arguments

Value

Description

Usage

Arguments

Value

HIPC_Stanford data

Description

Usage

Format

Details

Source

References

Kullback-Leibler divergence plot

Description

Usage

Arguments

Value

Examples

Computes a classification on the target data

Description

Usage

Arguments

Value

Examples

CytOpt plot

Description

Usage

Arguments

Value

Examples

CytOpt print

Description

Usage

Arguments

Value

Examples

CytOpt print summary

Description

Usage

Arguments

CytOpt summary

Description

Usage

Arguments

Value

Examples