cytopt_desasc_r {CytOpT} | R Documentation |

## 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

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.

### 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
)
```

### 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 |

`theta_true` |
If available, gold-standard proportions in the target data
set |

`eps` |
an float value of regularization parameter of the Wasserstein distance. Default is |

`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 |

`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 |

`step_grad` |
an integer number step size of the gradient descent algorithm
of the outer loop. Default is |

`monitoring` |
boolean indicating whether Kullback-Leibler divergence should be
monitored and store throughout the optimization iterations. Default is |

### Value

A list with the following elements:`h_hat`

*CytOpT*version 0.9.4 Index]