assess_cell_quality_SVM {cellity} | R Documentation

## Assess quality of a cell - SVM version

### Description

Assess quality of a cell - SVM version

### Usage

    assess_cell_quality_SVM(training_set_features, training_set_labels,
      ensemble_param, test_set_features)

### Arguments

`training_set_features` | A training set containing features (cells x features) for prediction
`training_set_labels` | Annotation of each individual cell if high or low quality (1 or 0 respectively)
`ensemble_param` | Dataframe of parameters for SVM
`test_set_features` | Dataset to predict containing features (cells x features)

### Details

This function takes a traning set + annotation to predict a test set. It requires that hyper-parameters have been optimised.

### Value

Returns a dataframe indicating which cell is low or high quality (0 or 1 respectively)

data.frame with decision on quality of cells

### Examples

    data(param_mES_all)
    data(training_mES_features)
    data(training_mES_labels)
    data(mES1_features)
    data(mES1_labels)
    mES1_features_all <- mES1_features[[1]]
    training_mES_features_all <- training_mES_features[[1]]
    mES1_quality_SVM <- assess_cell_quality_SVM( training_mES_features_all,
    training_mES_labels[,2], param_mES_all, mES1_features_all)

[Package cellity version 1.8.0 Index]