eva_cal

calculate the F1 score of each cell population, mean of F1 score and overall accuracy

An entirely data-driven cell type annotation tools, which requires training data to learn the classifier, but not biological knowledge to make subjective decisions. It consists of three steps: preprocessing training and test data, model fitting on training data, and cell classification on test data. See Xiangling Ji,Danielle Tsao, Kailun Bai, Min Tsao, Li Xing, Xuekui Zhang.(2022)<doi:10.1101/2022.02.19.481159> for more details.

Xuekui Zhang

scAnnotate

An Automated Cell Type Annotation Tool for Single-Cell
RNA-Sequencing Data

Xiangling Ji

Danielle Tsao

Kailun Bai

Min Tsao

Li Xing

eva_cal function

<dl><dt>prediction</dt>
<dd>A vector of annotate cell type labels</dd>
<dt>cell_label</dt>
<dd>A vector of original cell type labels</dd></dl>

Arguments

eva_cal — eva_cal

<dl>

<dt>prediction</dt>
<dd>A vector of annotate cell type labels</dd>


<dt>cell_label</dt>
<dd>A vector of original cell type labels</dd>

</dl>

eva_cal: eva_cal

Description

Usage

Value

Arguments

Examples