Description Usage Arguments Value Author(s)

View source: R/predict_scClassify.R

Testing scClassify model

1 2 3 4 5 6 | ```
predict_scClassify(exprsMat_test, trainRes, cellTypes_test, k = 10,
prob_threshold = 0.8, cor_threshold_static = 0.5,
cor_threshold_high = 0.7, features = "pearson",
algorithm = c("WKNN", "KNN", "DWKNN"), similarity = c("pearson",
"spearman", "cosine", "jaccard", "kendall", "weighted_rank",
"manhattan"), cutoff_method = c("dynamic", "static"), verbose = T)
``` |

`exprsMat_test` |
A list or a matrix indicates the expression matrices of the testing datasets |

`trainRes` |
A vector of cell types |

`cellTypes_test` |
A list or a vector indicates cell types of the testing datasets (Optional). |

`k` |
An integer indicates the number of neighbour |

`prob_threshold` |
A numeric indicates the probability threshold for KNN/WKNN/DWKNN. |

`cor_threshold_static` |
A numeric indicates the static correlation threshold. |

`cor_threshold_high` |
A numeric indicates the highest correlation threshold |

`features` |
A vector indicates the method to select features, set as "limma" by default. This should be one or more of "limma", "DV", "DD", "chisq", "BI". |

`algorithm` |
A vector indicates the KNN method that are used, set as "WKNN" by default. This should be one or more of "WKNN", "KNN", "DWKNN". |

`similarity` |
A vector indicates the similarity measure that are used, set as "pearson" by default. This should be one or more of "pearson", "spearman", "cosine", "jaccard", "kendall", "binomial", "weighted_rank","manhattan" |

`cutoff_method` |
A vector indicates the method to cutoff the correlation distribution. Set as "dynamic" by default. |

`verbose` |
A logical input indicates whether the intermediate steps will be printed |

list of results

Yingxin Lin

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