Hierarchical annotation of immune cells in scRNA-Seq data based on ssGSEA algorithm. Fork for large datasets with QOL improvements.
byarielm.fyi
1Package: scImmuCC
2Type: Package
3Title: Hierarchical annotation for immune cell types in scRNA-Seq data
4Version: 1.0.0
5Author: c(person("Ying", "Jiang",
6 email = "jiangy_lavender@163.com",
7 role = c("aut", "cre", "cph")),
8 person("Aiping", "Wu",
9 email = "wap@ism.cams.cn",
10 role = c("ths")))
11Maintainer: YingJiang<jiangy_lavender@163.com>
12Description: Annotating immune cells based on markers is one of the commonly used methods for single cell RNA-Seq data annotation. This package aims to annotate immune cells in scRNA-Seq data using a hierarchical strategy based on the ssGSEA algorithm to calculate enrichment scores. The enrichment score of each type of cell is annotated separately by hierarchical calculation, thereby reducing the interference of gene expression profiles between similar subtypes, and achieving more accurate annotation of immune cell subtypes. The input file is an expression matrix with barcodes as column names and gene names as row namess. The optional parameter is Non_Immune, which is whether the user data contains non-immune cells. The default value is TRUE.
13License: MIT + file LICENSE
14Encoding: UTF-8
15LazyData: true
16Data:
17 test_data,
18 layer0_genelist,
19 layer1_genelist,
20 Tcell_genelist,
21 Bcell_genelist,
22 DC_genelist,
23 NK_genelist,
24 Monocyte_genelist,
25 Macrophage_genelist,
26 ILC_genelist,
27 CD4_genelist,
28 CD8_genelist
29Depends:
30 R (>= 4.1.1),
31 Seurat,
32 GSVA
33Imports:
34 GSVA,
35 Seurat,
36 ggplot2,
37 dplyr,
38 Matrix
39Suggests:
40 testthat (>= 3.0.0),
41 knitr,
42 rmarkdown
43Config/testthat/edition: 3
44RoxygenNote: 7.2.3
45VignetteBuilder: knitr