Description Usage Arguments Value Author(s) Examples

View source: R/predict_scClassify.R

Testing scClassify model (joint training)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | ```
predict_scClassifyJoint(
exprsMat_test,
trainRes,
cellTypes_test,
k = 10,
prob_threshold = 0.7,
cor_threshold_static = 0.5,
cor_threshold_high = 0.7,
features = "limma",
algorithm = "WKNN",
similarity = "pearson",
cutoff_method = c("dynamic", "static"),
parallel = FALSE,
BPPARAM = BiocParallel::SerialParam(),
verbose = FALSE
)
``` |

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

`trainRes` |
A 'scClassifyTrainModel' or a 'list' indicates scClassify training model |

`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. |

`parallel` |
A logical input indicates whether running in paralllel or not |

`BPPARAM` |
A |

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

list of results

Yingxin Lin

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | ```
data("scClassify_example")
wang_cellTypes <- scClassify_example$wang_cellTypes
exprsMat_wang_subset <- scClassify_example$exprsMat_wang_subset
data("trainClassExample_xin")
data("trainClassExample_wang")
trainClassExampleJoint <- scClassifyTrainModelList(trainClassExample_wang,
trainClassExample_xin)
pred_res_joint <- predict_scClassifyJoint(exprsMat_test = exprsMat_wang_subset,
trainRes = trainClassExampleJoint,
cellTypes_test = wang_cellTypes,
algorithm = "WKNN",
features = c("limma"),
similarity = c("pearson"),
prob_threshold = 0.7,
verbose = FALSE)
table(pred_res_joint$jointRes$cellTypes, wang_cellTypes)
``` |

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