predict_scClassify: Testing scClassify model

Description Usage Arguments Value Author(s)

View source: R/predict_scClassify.R

Description

Testing scClassify model

Usage

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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)

Arguments

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

Value

list of results

Author(s)

Yingxin Lin


SydneyBioX/scdney documentation built on Aug. 22, 2019, 10:55 a.m.