Description Usage Arguments Value Author(s) Examples

Train and test scClassify model

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 | ```
scClassify(
exprsMat_train = NULL,
cellTypes_train = NULL,
exprsMat_test = NULL,
cellTypes_test = NULL,
tree = "HOPACH",
algorithm = "WKNN",
selectFeatures = "limma",
similarity = "pearson",
cutoff_method = c("dynamic", "static"),
weighted_ensemble = FALSE,
weights = NULL,
weighted_jointClassification = TRUE,
cellType_tree = NULL,
k = 10,
topN = 50,
hopach_kmax = 5,
pSig = 0.01,
prob_threshold = 0.7,
cor_threshold_static = 0.5,
cor_threshold_high = 0.7,
returnList = TRUE,
parallel = FALSE,
BPPARAM = BiocParallel::SerialParam(),
verbose = FALSE
)
``` |

`exprsMat_train` |
A matrix of log-transformed expression matrix of reference dataset |

`cellTypes_train` |
A vector of cell types of reference dataset |

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

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

`tree` |
A vector indicates the method to build hierarchical tree, set as "HOPACH" by default. This should be one of "HOPACH" and "HC" (using hclust). |

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

`selectFeatures` |
A vector indicates the gene selection method, set as "limma" by default. This should be one or more of "limma", "DV", "DD", "chisq", "BI" and "Cepo". |

`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. |

`weighted_ensemble` |
A logical input indicates in ensemble learning, whether the results is combined by a weighted score for each base classifier. |

`weights` |
A vector indicates the weights for ensemble |

`weighted_jointClassification` |
A logical input indicates in joint classification using multiple training datasets, whether the results is combined by a weighted score for each training model. |

`cellType_tree` |
A list indicates the cell type tree provided by user. (By default, it is NULL) (Only for one training data input) |

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

`topN` |
An integer indicates the top number of features that are selected |

`hopach_kmax` |
An integer between 1 and 9 specifying the maximum number of children at each node in the HOPACH tree. |

`pSig` |
A numeric indicates the cutoff of pvalue for features |

`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 |

`returnList` |
A logical input indicates whether the output will be class of list |

`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 |

A list of the results, including testRes storing the results of the testing information, and trainRes storing the training model inforamtion.

Yingxin Lin

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | ```
data("scClassify_example")
xin_cellTypes <- scClassify_example$xin_cellTypes
exprsMat_xin_subset <- scClassify_example$exprsMat_xin_subset
wang_cellTypes <- scClassify_example$wang_cellTypes
exprsMat_wang_subset <- scClassify_example$exprsMat_wang_subset
scClassify_res <- scClassify(exprsMat_train = exprsMat_xin_subset,
cellTypes_train = xin_cellTypes,
exprsMat_test = list(wang = exprsMat_wang_subset),
cellTypes_test = list(wang = wang_cellTypes),
tree = "HOPACH",
algorithm = "WKNN",
selectFeatures = c("limma"),
similarity = c("pearson"),
returnList = FALSE,
verbose = FALSE)
``` |

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