The goal of sc.utils is to integrate useful function for single cell sequencing data analysis
And the development version from GitHub with:
# install.packages("devtools")
devtools::install_github("Byronxy/sc.utils")This is a basic example which shows you how to solve a common problem:
library(sc.utils)
## basic example code
| FileName | Description | Usage | ID Format |
|---|---|---|---|
| All cell markers | All cell markers of different cell types from different tissues in human and mouse. | data(“cell_markers_all_entrez”,package = “sc.utils”, envir = environment()) | EntrezID |
| All cell markers | All cell markers of different cell types from different tissues in human and mouse. | data(“cell_markers_all_symbal”,package = “sc.utils”, envir = environment()) | SymbolID |
| Human cell markers | Cell markers of different cell types from different tissues in human. | data(“cell_markers_human_entrez”,package = “sc.utils”, envir = environment()) | EntrezID |
| Human cell markers | Cell markers of different cell types from different tissues in human. | data(“cell_markers_human_symbal”,package = “sc.utils”, envir = environment()) | SymbolID |
| Mouse cell markers | Cell markers of different cell types from different tissues in mouse. | data(“cell_markers_mouse_entrez”,package = “sc.utils”, envir = environment()) | EntrezID |
| Mouse cell markers | Cell markers of different cell types from different tissues in mouse. | data(“cell_markers_mouse_symbal”,package = “sc.utils”, envir = environment()) | SymbolID |
| Single cell markers | Cell markers derived from single-cell sequencing researches in human and mouse. | data(“cell_markers_singlecell_entrez”,package = “sc.utils”, envir = environment()) | EntrezID |
| Single cell markers | Cell markers derived from single-cell sequencing researches in human and mouse. | data(“cell_markers_singlecell_symbal”,package = “sc.utils”, envir = environment()) | SymbolID |
#entrez
data("cell_markers_all_entrez",package = "sc.utils", envir = environment())
data("cell_markers_human_entrez",package = "sc.utils", envir = environment())
data("cell_markers_mouse_entrez",package = "sc.utils", envir = environment())
data("cell_markers_singlecell_entrez",package = "sc.utils", envir = environment())
#symbol
data("cell_markers_all_symbal",package = "sc.utils", envir = environment())
data("cell_markers_human_symbal",package = "sc.utils", envir = environment())
data("cell_markers_mouse_symbal",package = "sc.utils", envir = environment())
data("cell_markers_singlecell_symbal",package = "sc.utils", envir = environment())-
gbm_single_cell_geneset.gmt
-
SHH_MB_hg_signature.gmt
library(GSEABase)
#> Loading required package: annotate
#> Loading required package: XML
#> Loading required package: graph
#>
#> Attaching package: 'graph'
#> The following object is masked from 'package:XML':
#>
#> addNode
gmtFile <- paste(file.path(system.file('examples', package='sc.utils')), "SHH_MB_hg_signature.gmt", sep="/")
geneSets <- getGmt(gmtFile)#cell cycle
data("cellcycle_hg",package = "sc.utils", envir = environment())
data("cellcycle_mm",package = "sc.utils", envir = environment())
#gbm single cell signature
data("gbm_single_cell_geneset",package = "sc.utils", envir = environment())
data("gbm_single_cell_geneset_list",package = "sc.utils", envir = environment())
#shh-mg signature
data("shh_mb_hg_single_cell_geneset_list",package = "sc.utils", envir = environment())
data("shh_mb_mm_single_cell_geneset_list",package = "sc.utils", envir = environment())To avoid unexpected noise and expression artefacts by dissociation, a total of 1,514 genes associated with mitochondria (50 genes), heat-shock protein (178 genes), ribosome (1,253 genes) and dissociation (33 genes) were excluded.
data("gs_MT_HSP_RB_DS",package = "sc.utils", envir = environment())FeaturePlot_gene_pos()
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