scType
Cell type Annotation
Sung Rye Park
2024-12-03
Packages
- scType
scType
Processing
library(HGNChelper)
library(openxlsx)
source("https://raw.githubusercontent.com/IanevskiAleksandr/sc-type/master/R/gene_sets_prepare.R"); source("https://raw.githubusercontent.com/IanevskiAleksandr/sc-type/master/R/sctype_score_.R")
gs_list <- gene_sets_prepare("https://raw.githubusercontent.com/IanevskiAleksandr/sc-type/master/ScTypeDB_short.xlsx", "Brain") # e.g. Immune system, Liver, Pancreas, Kidney, Eye, Braincluster_resolution = "RNA_snn_res.0.4"
# extract scaled scRNA-seq matrix
seurat_package_v5 <- isFALSE('counts' %in% names(attributes(obj.srt[["RNA"]])));
scRNAseqData_scaled <- if (seurat_package_v5) as.matrix(obj.srt[["RNA"]]$scale.data) else as.matrix(obj.srt[["RNA"]]@scale.data)
# run ScType
es.max <- sctype_score(scRNAseqData = scRNAseqData_scaled,
scaled = TRUE,
gs = gs_list$gs_positive,
gs2 = gs_list$gs_negative)
cL_results <- do.call("rbind", lapply(unique(obj.srt@meta.data[, cluster_resolution]), function(cl) {
cl_cells <- rownames(obj.srt@meta.data[obj.srt@meta.data[, cluster_resolution] == cl, ])
# Maximum scores of each clusters
es.max.cl <- sort(rowSums(es.max[, cl_cells]), decreasing = TRUE)
# data frame
df <- data.frame(
cluster = cl,
type = names(es.max.cl),
scores = es.max.cl,
ncells = sum(obj.srt@meta.data[, cluster_resolution] == cl)
)
# top 10
return(head(df, 10))
}))
# cL_results %>% saveRDS(paste0(dir, "rds/cL_results.rds"))
sctype_scores <- cL_results %>% group_by(cluster) %>% top_n(n = 5, wt = scores)
sctype_scores <- sctype_scores %>% filter(scores> 0)scType Score
Table
sctype_scores$cluster = paste0("cluster ", sctype_scores$cluster)
sctype_scores %>% DT::datatable(extensions = "Buttons",
filter = 'bottom',
width = "800px",
options = list(autoWidth = TRUE,
fixedHeader = TRUE,
dom="Bfrtip", buttons=c("csv","excel")))Score plot
df = sctype_scores
df$cluster = factor(df$cluster, levels = c(paste0("cluster ", 0:17)))
df %>% ggplot(aes(cluster, scores, fill=type)) +
geom_col(position = "dodge2", color="grey7", linewidth =0.1) +
theme_bw() +
RotatedAxis()
Score plot2
df = sctype_scores
df$cluster = factor(df$cluster, levels = c(paste0("cluster ", 0:17)))
df %>% ggplot(aes(cluster, scores, fill=type)) +
geom_col(position = "dodge2", color="grey7", linewidth =0.1) +
theme_bw() +
facet_wrap(.~type, ncol = 3) +
RotatedAxis()
Vlnplot
es.max.t = t(es.max)
es.max.t = es.max.t[colnames(obj.srt),] %>% as.data.frame()
obj.srt = AddMetaData(object = obj.srt, metadata = es.max.t)
VlnPlot(obj.srt, group.by = "RNA_snn_res.0.4",
features = c("Cholinergic neurons",
"Dopaminergic neurons",
"Endothelial cells",
"GABAergic neurons",
"Glutamatergic neurons",
"Immature neurons",
"Mature neurons",
"Microglial cells",
"Myelinating Schwann cells",
"Neural Progenitor cells",
"Oligodendrocyte precursor cells",
"Oligodendrocytes",
"Radial glial cells",
"Schwann precursor cells",
"Serotonergic neurons"),
stack = T, flip = T, sort = T) +NoLegend()
Scoring correlation
Processing
es.max.t = t(es.max)
es.max.t = es.max.t[colnames(obj.srt),] %>% as.data.frame()
# obj.srt = AddMetaData(object = obj.srt, metadata = es.max.t)input.mtx= es.max.t
test.cor <- WGCNA::cor(input.mtx, use = 'pairwise.complete.obs')
#test.cor <- WGCNA::cor(input.mtx %>% t(), use = 'pairwise.complete.obs')
my.colors <- c(colorRampPalette(colors = c("navy","white"))(15),
colorRampPalette(colors = c("white","red"))(30))
# test.cor %>% pheatmap::pheatmap(color = my.colors)Correlation Heatmap
p =test.cor %>% pheatmap::pheatmap(color = my.colors)
Correlation table
test.cor[p$tree_row$order,p$tree_col$order] %>%
DT::datatable(extensions = "Buttons",
filter = 'bottom',
width = "800px",
options = list(autoWidth = TRUE,
fixedHeader = TRUE,
pageLength= 22,
scrollX=T,
dom="Bfrtip", buttons=c("csv","excel")))sessionInfo()## R version 4.4.1 (2024-06-14)
## Platform: aarch64-apple-darwin20
## Running under: macOS Sonoma 14.6.1
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRlapack.dylib; LAPACK version 3.12.0
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## time zone: America/Detroit
## tzcode source: internal
##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] openxlsx_4.2.7 HGNChelper_0.8.14
## [3] SeuratWrappers_0.4.0 SingleCellExperiment_1.26.0
## [5] SummarizedExperiment_1.34.0 Biobase_2.64.0
## [7] GenomicRanges_1.56.1 GenomeInfoDb_1.40.1
## [9] IRanges_2.38.1 S4Vectors_0.42.1
## [11] BiocGenerics_0.50.0 MatrixGenerics_1.16.0
## [13] matrixStats_1.4.1 scCustomize_2.1.2
## [15] Seurat_5.1.0 SeuratObject_5.0.2
## [17] sp_2.1-4 dplyr_1.1.4
## [19] ggrepel_0.9.6 ggplot2_3.5.1
##
## loaded via a namespace (and not attached):
## [1] spatstat.sparse_3.1-0 lubridate_1.9.3 httr_1.4.7
## [4] RColorBrewer_1.1-3 doParallel_1.0.17 dynamicTreeCut_1.63-1
## [7] tools_4.4.1 sctransform_0.4.1 backports_1.5.0
## [10] utf8_1.2.4 R6_2.5.1 DT_0.33
## [13] lazyeval_0.2.2 uwot_0.2.2 withr_3.0.2
## [16] gridExtra_2.3 preprocessCore_1.66.0 progressr_0.15.1
## [19] WGCNA_1.73 cli_3.6.3 spatstat.explore_3.3-3
## [22] fastDummies_1.7.4 labeling_0.4.3 sass_0.4.9
## [25] spatstat.data_3.1-4 ggridges_0.5.6 pbapply_1.7-2
## [28] foreign_0.8-86 R.utils_2.12.3 parallelly_1.39.0
## [31] rstudioapi_0.16.0 impute_1.78.0 RSQLite_2.3.8
## [34] generics_0.1.3 shape_1.4.6.1 ica_1.0-3
## [37] spatstat.random_3.3-2 crosstalk_1.2.1 zip_2.3.1
## [40] GO.db_3.19.1 Matrix_1.7-0 ggbeeswarm_0.7.2
## [43] fansi_1.0.6 abind_1.4-8 R.methodsS3_1.8.2
## [46] lifecycle_1.0.4 yaml_2.3.10 snakecase_0.11.1
## [49] SparseArray_1.4.8 Rtsne_0.17 paletteer_1.6.0
## [52] grid_4.4.1 blob_1.2.4 promises_1.3.2
## [55] crayon_1.5.3 miniUI_0.1.1.1 lattice_0.22-6
## [58] cowplot_1.1.3 KEGGREST_1.44.1 pillar_1.9.0
## [61] knitr_1.49 future.apply_1.11.3 codetools_0.2-20
## [64] leiden_0.4.3.1 glue_1.8.0 spatstat.univar_3.1-1
## [67] data.table_1.16.2 remotes_2.5.0 vctrs_0.6.5
## [70] png_0.1-8 spam_2.11-0 gtable_0.3.6
## [73] rematch2_2.1.2 cachem_1.1.0 xfun_0.49
## [76] S4Arrays_1.4.1 mime_0.12 survival_3.6-4
## [79] pheatmap_1.0.12 iterators_1.0.14 fitdistrplus_1.2-1
## [82] ROCR_1.0-11 nlme_3.1-164 bit64_4.5.2
## [85] RcppAnnoy_0.0.22 bslib_0.8.0 irlba_2.3.5.1
## [88] vipor_0.4.7 KernSmooth_2.23-24 rpart_4.1.23
## [91] splitstackshape_1.4.8 colorspace_2.1-1 DBI_1.2.3
## [94] Hmisc_5.2-0 nnet_7.3-19 ggrastr_1.0.2
## [97] tidyselect_1.2.1 bit_4.5.0 compiler_4.4.1
## [100] htmlTable_2.4.3 DelayedArray_0.30.1 plotly_4.10.4
## [103] checkmate_2.3.2 scales_1.3.0 lmtest_0.9-40
## [106] stringr_1.5.1 digest_0.6.37 goftest_1.2-3
## [109] spatstat.utils_3.1-1 rmarkdown_2.29 XVector_0.44.0
## [112] htmltools_0.5.8.1 pkgconfig_2.0.3 base64enc_0.1-3
## [115] fastmap_1.2.0 rlang_1.1.4 GlobalOptions_0.1.2
## [118] htmlwidgets_1.6.4 UCSC.utils_1.0.0 shiny_1.9.1
## [121] farver_2.1.2 jquerylib_0.1.4 zoo_1.8-12
## [124] jsonlite_1.8.9 R.oo_1.27.0 magrittr_2.0.3
## [127] Formula_1.2-5 GenomeInfoDbData_1.2.12 dotCall64_1.2
## [130] patchwork_1.3.0 munsell_0.5.1 Rcpp_1.0.13-1
## [133] reticulate_1.40.0 stringi_1.8.4 zlibbioc_1.50.0
## [136] MASS_7.3-60.2 plyr_1.8.9 parallel_4.4.1
## [139] listenv_0.9.1 forcats_1.0.0 deldir_2.0-4
## [142] Biostrings_2.72.1 splines_4.4.1 tensor_1.5
## [145] circlize_0.4.16 igraph_2.1.1 fastcluster_1.2.6
## [148] spatstat.geom_3.3-4 RcppHNSW_0.6.0 reshape2_1.4.4
## [151] evaluate_1.0.1 BiocManager_1.30.25 ggprism_1.0.5
## [154] foreach_1.5.2 httpuv_1.6.15 RANN_2.6.2
## [157] tidyr_1.3.1 purrr_1.0.2 polyclip_1.10-7
## [160] future_1.34.0 scattermore_1.2 rsvd_1.0.5
## [163] janitor_2.2.0 xtable_1.8-4 RSpectra_0.16-2
## [166] later_1.4.1 viridisLite_0.4.2 tibble_3.2.1
## [169] memoise_2.0.1 beeswarm_0.4.0 AnnotationDbi_1.66.0
## [172] cluster_2.1.6 timechange_0.3.0 globals_0.16.3