vignettes/ASURATDB.md
In keita-iida/ASURATDB: ASURAT database builder
ASURATDB
Keita Iida
2023-02-11
- 1 Installations
- 2 Collect
Disease Ontology database for human cells
- 3
Collect Cell Ontology database for human and mouse cells
- 4
Collect Gene Ontology database for human and mouse cells
- 5 Collect
KEGG database for human and mouse cells
- 6 Collect MSigDB for human
cells
- 7 Collect CellMarker for
human cells
- 8 Create a custom-built
database
- 9
Session information
1 Installations
Attach necessary libraries:
library(ASURATDB)
2 Collect Disease Ontology database for human cells
library(DOSE) # For using `data(DO2EG)`
ASURATDB function format_DO() reformats a Disease Ontology database.
data(DO2EG)
dict_DO <- enrichDO(unlist(DO2EG), ont = "DO", pvalueCutoff = 1,
pAdjustMethod = "BH", minGSSize = 0, maxGSSize = 1e+10,
qvalueCutoff = 1, readable = FALSE)
human_DO <- format_DO(dict = dict_DO@result, all_geneIDs = dict_DO@gene,
orgdb = org.Hs.eg.db::org.Hs.eg.db)
# Save data.
# save(human_DO, file = "genes2bioterm/20201213_human_DO.rda")
The data were stored in the following repositories:
3 Collect Cell Ontology database for human and mouse cells
ASURATDB functions collect_CO() and format_CO() load a Cell Ontology
database using ontoProc package and reformat the database, respectively.
Tips: As of December 2020, Cell Ontology database might not be
complete enough for some biological contexts. For example, well-known
marker genes for pancreatic beta cell, Ins1 and Ins2, were not
registered for “type B pancreatic cell” with ID “CL:0000169”.
# Human
dict_CO <- collect_CO(orgdb = org.Hs.eg.db::org.Hs.eg.db)
human_CO <- format_CO(dict = dict_CO, orgdb = org.Hs.eg.db::org.Hs.eg.db)
# Save data.
# save(human_CO, file = "genes2bioterm/20201213_human_CO.rda")
# Mouse
dict_CO <- collect_CO(orgdb = org.Mm.eg.db::org.Mm.eg.db)
mouse_CO <- format_CO(dict = dict_CO, orgdb = org.Mm.eg.db::org.Mm.eg.db)
# Save data.
# save(mouse_CO, file = "genes2bioterm/20201211_mouse_CO.rda")
The data were stored in the following repositories:
4 Collect Gene Ontology database for human and mouse cells
ASURATDB functions collect_GO() and format_GO() load a Gene Ontology
database using clusterProfiler package and reformat the database,
respectively. Currently, only human and mouse data are acceptable.
# Human
dict_GO <- collect_GO(orgdb = org.Hs.eg.db::org.Hs.eg.db)
human_GO <- format_GO(dict = dict_GO, orgdb = org.Hs.eg.db::org.Hs.eg.db)
# Human reduced
human_GO_red <- human_GO
onts <- c("MF", "BP", "CC")
for(i in seq_along(onts)){
ids <- human_GO[[onts[i]]][which(human_GO[[onts[i]]]$Count >= 2), ]$ID
mat <- human_GO$similarity_matrix[[onts[i]]][ids, ids]
human_GO_red$similarity_matrix[[onts[i]]] <- mat
}
# Save data.
# save(human_GO_red, file = "genes2bioterm/20201213_human_GO_red.rda")
# Mouse
dict_GO <- collect_GO(orgdb = org.Mm.eg.db::org.Mm.eg.db)
mouse_GO <- format_GO(dict = dict_GO, orgdb = org.Mm.eg.db::org.Mm.eg.db)
# Mouse reduced
mouse_GO_red <- mouse_GO
onts <- c("MF", "BP", "CC")
for(i in seq_along(onts)){
ids <- mouse_GO[[onts[i]]][which(mouse_GO[[onts[i]]]$Count >= 2), ]$ID
mat <- mouse_GO$similarity_matrix[[onts[i]]][ids, ids]
mouse_GO_red$similarity_matrix[[onts[i]]] <- mat
}
# Save data.
# save(mouse_GO_red, file = "genes2bioterm/20201211_mouse_GO_red.rda")
The data were stored in the following repositories:
5 Collect KEGG database for human and mouse cells
ASURATDB functions collect_KEGG() and format_KEGG() load a KEGG
database using KEGGREST package via the internet and reformat the
database, respectively.
The arguments of collect_KEGG() are organism and categories. Here,
organism must obey the naming rule of
KEGG (see KEGGREST function
listDatabases()) and categories must be one of "pathway",
"module", and "drug" (only for human) in the current version.
# Human
dict_KEGG <- collect_KEGG(organism = "hsa", categories = c("pathway"))
human_KEGG <- format_KEGG(dict = list(pathway = dict_KEGG[["pathway"]][["success"]]),
orgdb = org.Hs.eg.db::org.Hs.eg.db)
# Save data.
# save(human_KEGG, file = "genes2bioterm/20201213_human_KEGG.rda")
# Mouse
dict_KEGG <- collect_KEGG(organism = "mmu", categories = c("pathway"))
mouse_KEGG <- format_KEGG(dict = list(pathway = dict_KEGG[["pathway"]][["success"]]),
orgdb = org.Mm.eg.db::org.Mm.eg.db)
# Save data.
# save(mouse_KEGG, file = "genes2bioterm/20201211_mouse_KEGG.rda")
# Human (drug)
dict_KEGG_drug <- collect_KEGG(organism = "hsa", categories = c("drug"))
human_KEGG_drug <- format_KEGG(dict = list(drug = dict_KEGG_drug[["drug"]][["success"]]),
orgdb = org.Hs.eg.db::org.Hs.eg.db)
# Save data.
# save(human_KEGG_drug, file = "genes2bioterm/20221102_human_KEGG_drug.rda")
Note collect_KEGG() uses KEGGREST function keggGet(), which may
produce both successful and unsuccessful results. The data were stored
in the following repositories:
6 Collect MSigDB for human cells
6.1 H: hallmark gene sets
Load databases, where category is “H” (hallmark gene sets) and species
is human (cf. msigdbr::msigdbr_species()).
dbtable <- msigdbr::msigdbr(species = "Homo sapiens", category = "H")
Reformat the database.
dbtable_gsetID <- dbtable[, which(colnames(dbtable) %in% c("gs_name", "gs_id"))]
dbtable_gsetID <- unique(dbtable_gsetID)
dbtable_geneID <- split(x = dbtable$human_entrez_gene, f = dbtable$gs_name)
dbtable_symbol <- split(x = dbtable$gene_symbol, f = dbtable$gs_name)
stopifnot(identical(length(dbtable_geneID), length(dbtable_symbol)))
res <- c("ID", "Description", "Count", "Gene", "GeneID", "IC")
res <- data.frame(matrix(ncol = 6, nrow = 0, dimnames = list(NULL, res)))
for(i in 1:length(dbtable_geneID)){
res <- rbind(res, data.frame(
ID = dbtable_gsetID$gs_id[i],
Description = dbtable_gsetID$gs_name[i],
IC = NA,
Count = length(dbtable_geneID[[i]]),
Gene = paste(dbtable_symbol[[i]], collapse = "/"),
GeneID = paste(dbtable_geneID[[i]], collapse = "/")))
}
human_MSigDB_Hallmark <- list(hallmark = res)
# Save data.
# save(human_MSigDB_Hallmark, file = "genes2bioterm/20230127_human_MSigDB_Hallmark.rda")
The data were stored in the following repositories:
6.2 C2: curated gene sets (BioCarta subset of CP)
Load databases, where category is “C3” (regulatory target gene sets) and
species is human (cf. msigdbr::msigdbr_species()).
dbtable <- msigdbr::msigdbr(species = "Homo sapiens", category = "C2")
dbtable <- dbtable[which(dbtable$gs_subcat == "CP:BIOCARTA"), ]
Reformat the database.
dbtable_gsetID <- dbtable[, which(colnames(dbtable) %in% c("gs_name", "gs_id"))]
dbtable_gsetID <- unique(dbtable_gsetID)
dbtable_geneID <- split(x = dbtable$human_entrez_gene, f = dbtable$gs_name)
dbtable_symbol <- split(x = dbtable$gene_symbol, f = dbtable$gs_name)
stopifnot(identical(length(dbtable_geneID), length(dbtable_symbol)))
res <- c("ID", "Description", "Count", "Gene", "GeneID", "IC")
res <- data.frame(matrix(ncol = 6, nrow = 0, dimnames = list(NULL, res)))
for(i in 1:length(dbtable_geneID)){
res <- rbind(res, data.frame(
ID = dbtable_gsetID$gs_id[i],
Description = dbtable_gsetID$gs_name[i],
IC = NA,
Count = length(dbtable_geneID[[i]]),
Gene = paste(dbtable_symbol[[i]], collapse = "/"),
GeneID = paste(dbtable_geneID[[i]], collapse = "/")))
}
human_MSigDB_BIOCARTA <- list(BIOCARTA = res)
# Save data.
# save(human_MSigDB_BIOCARTA, file = "genes2bioterm/20230211_human_MSigDB_BIOCARTA.rda")
The data were stored in the following repositories:
6.3 C3: regulatory target gene sets (TFT:GTRD)
Load databases, where category is “C3” (regulatory target gene sets) and
species is human (cf. msigdbr::msigdbr_species()).
dbtable <- msigdbr::msigdbr(species = "Homo sapiens", category = "C3")
dbtable <- dbtable[which(dbtable$gs_subcat == "TFT:GTRD"), ]
Reformat the database.
dbtable_gsetID <- dbtable[, which(colnames(dbtable) %in% c("gs_name", "gs_id"))]
dbtable_gsetID <- unique(dbtable_gsetID)
dbtable_geneID <- split(x = dbtable$human_entrez_gene, f = dbtable$gs_name)
dbtable_symbol <- split(x = dbtable$gene_symbol, f = dbtable$gs_name)
stopifnot(identical(length(dbtable_geneID), length(dbtable_symbol)))
res <- c("ID", "Description", "Count", "Gene", "GeneID", "IC")
res <- data.frame(matrix(ncol = 6, nrow = 0, dimnames = list(NULL, res)))
for(i in 1:length(dbtable_geneID)){
res <- rbind(res, data.frame(
ID = dbtable_gsetID$gs_id[i],
Description = dbtable_gsetID$gs_name[i],
IC = NA,
Count = length(dbtable_geneID[[i]]),
Gene = paste(dbtable_symbol[[i]], collapse = "/"),
GeneID = paste(dbtable_geneID[[i]], collapse = "/")))
}
human_MSigDB_GTRD <- list(GTRD = res)
# Save data.
# save(human_MSigDB_GTRD, file = "genes2bioterm/20230211_human_MSigDB_GTRD.rda")
The data were stored in the following repositories:
6.4 Cell types in MSigDB
Load databases.
dbtable <- clustermole::clustermole_markers()
sort(unique(dbtable$db))
[1] "ARCHS4" "CellMarker" "MSigDB" "PanglaoDB" "SaVanT" "TISSUES"
[7] "xCell"
Select species and databases.
dbtable <- dbtable[which(dbtable$species == "Human"), ]
dbtable <- dbtable[which(dbtable$db == "MSigDB"),]
dbtable$geneID <- NA
Change gene symbols into entrez IDs.
dictionary <- AnnotationDbi::select(org.Hs.eg.db::org.Hs.eg.db,
key = dbtable$gene_original,
columns = c("SYMBOL", "ENTREZID"),
keytype = "SYMBOL")
dictionary <- dictionary[!duplicated(dictionary$SYMBOL), ]
dictionary <- dictionary[which(!is.na(dictionary$SYMBOL)),]
for(i in 1:nrow(dbtable)){
gene <- dbtable$gene_original[i]
inds <- which(dictionary$SYMBOL == gene)
dbtable$geneID[i] <- dictionary[inds,]$ENTREZID
}
Reformat the database. Here, the identifier of each biological term are
named “MSigDBID.”
dbtable_geneID <- split(x = dbtable$geneID, f = dbtable$celltype)
dbtable_symbol <- split(x = dbtable$gene_original, f = dbtable$celltype)
stopifnot(identical(length(dbtable_geneID), length(dbtable_symbol)))
res <- c("ID", "Description", "Count", "Gene", "GeneID", "IC")
res <- data.frame(matrix(ncol = 6, nrow = 0, dimnames = list(NULL, res)))
for(i in 1:length(dbtable_geneID)){
res <- rbind(res, data.frame(
ID = paste("MSigDBID:", i, sep = ""),
Description = names(dbtable_geneID)[i],
IC = NA,
Count = length(dbtable_geneID[[i]]),
Gene = paste(dbtable_symbol[[i]], collapse = "/"),
GeneID = paste(dbtable_geneID[[i]], collapse = "/")))
}
human_MSigDB <- list(cell = res)
# Save data.
# save(human_MSigDB, file = "genes2bioterm/20220308_human_MSigDB.rda")
The data were stored in the following repositories:
7 Collect CellMarker for human cells
Load databases.
dbtable <- clustermole::clustermole_markers()
sort(unique(dbtable$db))
[1] "ARCHS4" "CellMarker" "MSigDB" "PanglaoDB" "SaVanT" "TISSUES"
[7] "xCell"
Select species and databases.
dbtable <- dbtable[which(dbtable$species == "Human"), ]
dbtable <- dbtable[which(dbtable$db == "CellMarker"),]
dbtable$geneID <- NA
Change gene symbols into entrez IDs.
dictionary <- AnnotationDbi::select(org.Hs.eg.db::org.Hs.eg.db,
key = dbtable$gene_original,
columns = c("SYMBOL", "ENTREZID"),
keytype = "SYMBOL")
dictionary <- dictionary[!duplicated(dictionary$SYMBOL), ]
dictionary <- dictionary[which(!is.na(dictionary$SYMBOL)),]
for(i in 1:nrow(dbtable)){
gene <- dbtable$gene_original[i]
inds <- which(dictionary$SYMBOL == gene)
dbtable$geneID[i] <- dictionary[inds,]$ENTREZID
}
Reformat the database. Here, the identifier of each biological term are
named “CellMarkerID.”
dbtable_geneID <- split(x = dbtable$geneID, f = dbtable$celltype)
dbtable_symbol <- split(x = dbtable$gene_original, f = dbtable$celltype)
stopifnot(identical(length(dbtable_geneID), length(dbtable_symbol)))
res <- c("ID", "Description", "Count", "Gene", "GeneID", "IC")
res <- data.frame(matrix(ncol = 6, nrow = 0, dimnames = list(NULL, res)))
for(i in 1:length(dbtable_geneID)){
res <- rbind(res, data.frame(
ID = paste("CellMarkerID:", i, sep = ""),
Description = names(dbtable_geneID)[i],
IC = NA,
Count = length(dbtable_geneID[[i]]),
Gene = paste(dbtable_symbol[[i]], collapse = "/"),
GeneID = paste(dbtable_geneID[[i]], collapse = "/")))
}
human_CellMarker <- list(cell = res)
# Save data.
# save(human_CellMarker, file = "genes2bioterm/20220308_human_CellMarker.rda")
The data were stored in the following repositories:
8 Create a custom-built database
8.1 Combine CO and MSigDB for human cells
Create a cell type-related database by combining Cell ontology and
MSigDB databases for analyzing human single-cell transcriptome data.
urlpath <- "https://github.com/keita-iida/ASURATDB/blob/main/genes2bioterm/"
load(url(paste0(urlpath, "20201213_human_CO.rda?raw=true")))
load(url(paste0(urlpath, "20220308_human_MSigDB.rda?raw=true")))
res <- rbind(human_CO[["cell"]], human_MSigDB[["cell"]])
human_CB <- list(cell = res)
8.2 Combine CO, MSigDB, and CellMarker for human cells
Create a cell type-related database by combining Cell ontology, MSigDB,
and CellMarker databases for analyzing human single-cell transcriptome
data.
urlpath <- "https://github.com/keita-iida/ASURATDB/blob/main/genes2bioterm/"
load(url(paste0(urlpath, "20201213_human_CO.rda?raw=true")))
load(url(paste0(urlpath, "20220308_human_MSigDB.rda?raw=true")))
load(url(paste0(urlpath, "20220304_human_CellMarker.rda?raw=true")))
res <- do.call("rbind", list(human_CO[["cell"]], human_MSigDB[["cell"]],
human_CellMarker[["cell"]]))
human_CB <- list(cell = res)
8.3 Combine DO, CO, and MSigDB for human cells
Create a cell type-related database by combining Disease Ontology, Cell
ontology and MSigDB databases for analyzing complex human single-cell
transcriptome data.
urlpath <- "https://github.com/keita-iida/ASURATDB/blob/main/genes2bioterm/"
load(url(paste0(urlpath, "20201213_human_DO.rda?raw=true")))
load(url(paste0(urlpath, "20201213_human_CO.rda?raw=true")))
load(url(paste0(urlpath, "20220308_human_MSigDB.rda?raw=true")))
res <- do.call("rbind", list(human_DO[["disease"]], human_CO[["cell"]],
human_MSigDB[["cell"]]))
human_CB <- list(cell = res)
8.4 Combine DO, CO, MSigDB, and CellMarker for human cells
Create a cell type-related database by combining Disease Ontology, Cell
ontology, MSigDB, and CellMarker databases for analyzing complex human
single-cell transcriptome data.
urlpath <- "https://github.com/keita-iida/ASURATDB/blob/main/genes2bioterm/"
load(url(paste0(urlpath, "20201213_human_DO.rda?raw=true")))
load(url(paste0(urlpath, "20201213_human_CO.rda?raw=true")))
load(url(paste0(urlpath, "20220308_human_MSigDB.rda?raw=true")))
load(url(paste0(urlpath, "20220304_human_CellMarker.rda?raw=true")))
res <- do.call("rbind", list(human_DO[["disease"]], human_CO[["cell"]],
human_MSigDB[["cell"]], human_CellMarker[["cell"]]))
human_CB <- list(cell = res)
9 Session information
sessionInfo()
#> R version 4.2.1 (2022-06-23)
#> Platform: x86_64-apple-darwin17.0 (64-bit)
#> Running under: macOS Big Sur ... 10.16
#>
#> Matrix products: default
#> BLAS: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRblas.0.dylib
#> LAPACK: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRlapack.dylib
#>
#> locale:
#> [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
#>
#> attached base packages:
#> [1] stats graphics grDevices utils datasets methods base
#>
#> loaded via a namespace (and not attached):
#> [1] compiler_4.2.1 fastmap_1.1.0 cli_3.6.0 tools_4.2.1
#> [5] htmltools_0.5.4 rstudioapi_0.14 yaml_2.3.7 rmarkdown_2.20
#> [9] knitr_1.42 xfun_0.37 digest_0.6.31 rlang_1.0.6
#> [13] evaluate_0.20
keita-iida/ASURATDB documentation built on Feb. 21, 2023, 6:05 p.m.
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ASURATDB
Keita Iida 2023-02-11
- 1 Installations
- 2 Collect Disease Ontology database for human cells
- 3 Collect Cell Ontology database for human and mouse cells
- 4 Collect Gene Ontology database for human and mouse cells
- 5 Collect KEGG database for human and mouse cells
- 6 Collect MSigDB for human cells
- 7 Collect CellMarker for human cells
- 8 Create a custom-built database
- 9 Session information
1 Installations
Attach necessary libraries:
library(ASURATDB)
2 Collect Disease Ontology database for human cells
library(DOSE) # For using `data(DO2EG)`
ASURATDB function format_DO() reformats a Disease Ontology database.
data(DO2EG)
dict_DO <- enrichDO(unlist(DO2EG), ont = "DO", pvalueCutoff = 1,
pAdjustMethod = "BH", minGSSize = 0, maxGSSize = 1e+10,
qvalueCutoff = 1, readable = FALSE)
human_DO <- format_DO(dict = dict_DO@result, all_geneIDs = dict_DO@gene,
orgdb = org.Hs.eg.db::org.Hs.eg.db)
# Save data.
# save(human_DO, file = "genes2bioterm/20201213_human_DO.rda")
The data were stored in the following repositories:
3 Collect Cell Ontology database for human and mouse cells
ASURATDB functions collect_CO() and format_CO() load a Cell Ontology
database using ontoProc package and reformat the database, respectively.
Tips: As of December 2020, Cell Ontology database might not be complete enough for some biological contexts. For example, well-known marker genes for pancreatic beta cell, Ins1 and Ins2, were not registered for “type B pancreatic cell” with ID “CL:0000169”.
# Human
dict_CO <- collect_CO(orgdb = org.Hs.eg.db::org.Hs.eg.db)
human_CO <- format_CO(dict = dict_CO, orgdb = org.Hs.eg.db::org.Hs.eg.db)
# Save data.
# save(human_CO, file = "genes2bioterm/20201213_human_CO.rda")
# Mouse
dict_CO <- collect_CO(orgdb = org.Mm.eg.db::org.Mm.eg.db)
mouse_CO <- format_CO(dict = dict_CO, orgdb = org.Mm.eg.db::org.Mm.eg.db)
# Save data.
# save(mouse_CO, file = "genes2bioterm/20201211_mouse_CO.rda")
The data were stored in the following repositories:
4 Collect Gene Ontology database for human and mouse cells
ASURATDB functions collect_GO() and format_GO() load a Gene Ontology
database using clusterProfiler package and reformat the database,
respectively. Currently, only human and mouse data are acceptable.
# Human
dict_GO <- collect_GO(orgdb = org.Hs.eg.db::org.Hs.eg.db)
human_GO <- format_GO(dict = dict_GO, orgdb = org.Hs.eg.db::org.Hs.eg.db)
# Human reduced
human_GO_red <- human_GO
onts <- c("MF", "BP", "CC")
for(i in seq_along(onts)){
ids <- human_GO[[onts[i]]][which(human_GO[[onts[i]]]$Count >= 2), ]$ID
mat <- human_GO$similarity_matrix[[onts[i]]][ids, ids]
human_GO_red$similarity_matrix[[onts[i]]] <- mat
}
# Save data.
# save(human_GO_red, file = "genes2bioterm/20201213_human_GO_red.rda")
# Mouse
dict_GO <- collect_GO(orgdb = org.Mm.eg.db::org.Mm.eg.db)
mouse_GO <- format_GO(dict = dict_GO, orgdb = org.Mm.eg.db::org.Mm.eg.db)
# Mouse reduced
mouse_GO_red <- mouse_GO
onts <- c("MF", "BP", "CC")
for(i in seq_along(onts)){
ids <- mouse_GO[[onts[i]]][which(mouse_GO[[onts[i]]]$Count >= 2), ]$ID
mat <- mouse_GO$similarity_matrix[[onts[i]]][ids, ids]
mouse_GO_red$similarity_matrix[[onts[i]]] <- mat
}
# Save data.
# save(mouse_GO_red, file = "genes2bioterm/20201211_mouse_GO_red.rda")
The data were stored in the following repositories:
5 Collect KEGG database for human and mouse cells
ASURATDB functions collect_KEGG() and format_KEGG() load a KEGG
database using KEGGREST package via the internet and reformat the
database, respectively.
The arguments of collect_KEGG() are organism and categories. Here,
organism must obey the naming rule of
KEGG (see KEGGREST function
listDatabases()) and categories must be one of "pathway",
"module", and "drug" (only for human) in the current version.
# Human
dict_KEGG <- collect_KEGG(organism = "hsa", categories = c("pathway"))
human_KEGG <- format_KEGG(dict = list(pathway = dict_KEGG[["pathway"]][["success"]]),
orgdb = org.Hs.eg.db::org.Hs.eg.db)
# Save data.
# save(human_KEGG, file = "genes2bioterm/20201213_human_KEGG.rda")
# Mouse
dict_KEGG <- collect_KEGG(organism = "mmu", categories = c("pathway"))
mouse_KEGG <- format_KEGG(dict = list(pathway = dict_KEGG[["pathway"]][["success"]]),
orgdb = org.Mm.eg.db::org.Mm.eg.db)
# Save data.
# save(mouse_KEGG, file = "genes2bioterm/20201211_mouse_KEGG.rda")
# Human (drug)
dict_KEGG_drug <- collect_KEGG(organism = "hsa", categories = c("drug"))
human_KEGG_drug <- format_KEGG(dict = list(drug = dict_KEGG_drug[["drug"]][["success"]]),
orgdb = org.Hs.eg.db::org.Hs.eg.db)
# Save data.
# save(human_KEGG_drug, file = "genes2bioterm/20221102_human_KEGG_drug.rda")
Note collect_KEGG() uses KEGGREST function keggGet(), which may
produce both successful and unsuccessful results. The data were stored
in the following repositories:
6 Collect MSigDB for human cells
6.1 H: hallmark gene sets
Load databases, where category is “H” (hallmark gene sets) and species
is human (cf. msigdbr::msigdbr_species()).
dbtable <- msigdbr::msigdbr(species = "Homo sapiens", category = "H")
Reformat the database.
dbtable_gsetID <- dbtable[, which(colnames(dbtable) %in% c("gs_name", "gs_id"))]
dbtable_gsetID <- unique(dbtable_gsetID)
dbtable_geneID <- split(x = dbtable$human_entrez_gene, f = dbtable$gs_name)
dbtable_symbol <- split(x = dbtable$gene_symbol, f = dbtable$gs_name)
stopifnot(identical(length(dbtable_geneID), length(dbtable_symbol)))
res <- c("ID", "Description", "Count", "Gene", "GeneID", "IC")
res <- data.frame(matrix(ncol = 6, nrow = 0, dimnames = list(NULL, res)))
for(i in 1:length(dbtable_geneID)){
res <- rbind(res, data.frame(
ID = dbtable_gsetID$gs_id[i],
Description = dbtable_gsetID$gs_name[i],
IC = NA,
Count = length(dbtable_geneID[[i]]),
Gene = paste(dbtable_symbol[[i]], collapse = "/"),
GeneID = paste(dbtable_geneID[[i]], collapse = "/")))
}
human_MSigDB_Hallmark <- list(hallmark = res)
# Save data.
# save(human_MSigDB_Hallmark, file = "genes2bioterm/20230127_human_MSigDB_Hallmark.rda")
The data were stored in the following repositories:
6.2 C2: curated gene sets (BioCarta subset of CP)
Load databases, where category is “C3” (regulatory target gene sets) and
species is human (cf. msigdbr::msigdbr_species()).
dbtable <- msigdbr::msigdbr(species = "Homo sapiens", category = "C2")
dbtable <- dbtable[which(dbtable$gs_subcat == "CP:BIOCARTA"), ]
Reformat the database.
dbtable_gsetID <- dbtable[, which(colnames(dbtable) %in% c("gs_name", "gs_id"))]
dbtable_gsetID <- unique(dbtable_gsetID)
dbtable_geneID <- split(x = dbtable$human_entrez_gene, f = dbtable$gs_name)
dbtable_symbol <- split(x = dbtable$gene_symbol, f = dbtable$gs_name)
stopifnot(identical(length(dbtable_geneID), length(dbtable_symbol)))
res <- c("ID", "Description", "Count", "Gene", "GeneID", "IC")
res <- data.frame(matrix(ncol = 6, nrow = 0, dimnames = list(NULL, res)))
for(i in 1:length(dbtable_geneID)){
res <- rbind(res, data.frame(
ID = dbtable_gsetID$gs_id[i],
Description = dbtable_gsetID$gs_name[i],
IC = NA,
Count = length(dbtable_geneID[[i]]),
Gene = paste(dbtable_symbol[[i]], collapse = "/"),
GeneID = paste(dbtable_geneID[[i]], collapse = "/")))
}
human_MSigDB_BIOCARTA <- list(BIOCARTA = res)
# Save data.
# save(human_MSigDB_BIOCARTA, file = "genes2bioterm/20230211_human_MSigDB_BIOCARTA.rda")
The data were stored in the following repositories:
6.3 C3: regulatory target gene sets (TFT:GTRD)
Load databases, where category is “C3” (regulatory target gene sets) and
species is human (cf. msigdbr::msigdbr_species()).
dbtable <- msigdbr::msigdbr(species = "Homo sapiens", category = "C3")
dbtable <- dbtable[which(dbtable$gs_subcat == "TFT:GTRD"), ]
Reformat the database.
dbtable_gsetID <- dbtable[, which(colnames(dbtable) %in% c("gs_name", "gs_id"))]
dbtable_gsetID <- unique(dbtable_gsetID)
dbtable_geneID <- split(x = dbtable$human_entrez_gene, f = dbtable$gs_name)
dbtable_symbol <- split(x = dbtable$gene_symbol, f = dbtable$gs_name)
stopifnot(identical(length(dbtable_geneID), length(dbtable_symbol)))
res <- c("ID", "Description", "Count", "Gene", "GeneID", "IC")
res <- data.frame(matrix(ncol = 6, nrow = 0, dimnames = list(NULL, res)))
for(i in 1:length(dbtable_geneID)){
res <- rbind(res, data.frame(
ID = dbtable_gsetID$gs_id[i],
Description = dbtable_gsetID$gs_name[i],
IC = NA,
Count = length(dbtable_geneID[[i]]),
Gene = paste(dbtable_symbol[[i]], collapse = "/"),
GeneID = paste(dbtable_geneID[[i]], collapse = "/")))
}
human_MSigDB_GTRD <- list(GTRD = res)
# Save data.
# save(human_MSigDB_GTRD, file = "genes2bioterm/20230211_human_MSigDB_GTRD.rda")
The data were stored in the following repositories:
6.4 Cell types in MSigDB
Load databases.
dbtable <- clustermole::clustermole_markers()
sort(unique(dbtable$db))
[1] "ARCHS4" "CellMarker" "MSigDB" "PanglaoDB" "SaVanT" "TISSUES"
[7] "xCell"
Select species and databases.
dbtable <- dbtable[which(dbtable$species == "Human"), ]
dbtable <- dbtable[which(dbtable$db == "MSigDB"),]
dbtable$geneID <- NA
Change gene symbols into entrez IDs.
dictionary <- AnnotationDbi::select(org.Hs.eg.db::org.Hs.eg.db,
key = dbtable$gene_original,
columns = c("SYMBOL", "ENTREZID"),
keytype = "SYMBOL")
dictionary <- dictionary[!duplicated(dictionary$SYMBOL), ]
dictionary <- dictionary[which(!is.na(dictionary$SYMBOL)),]
for(i in 1:nrow(dbtable)){
gene <- dbtable$gene_original[i]
inds <- which(dictionary$SYMBOL == gene)
dbtable$geneID[i] <- dictionary[inds,]$ENTREZID
}
Reformat the database. Here, the identifier of each biological term are named “MSigDBID.”
dbtable_geneID <- split(x = dbtable$geneID, f = dbtable$celltype)
dbtable_symbol <- split(x = dbtable$gene_original, f = dbtable$celltype)
stopifnot(identical(length(dbtable_geneID), length(dbtable_symbol)))
res <- c("ID", "Description", "Count", "Gene", "GeneID", "IC")
res <- data.frame(matrix(ncol = 6, nrow = 0, dimnames = list(NULL, res)))
for(i in 1:length(dbtable_geneID)){
res <- rbind(res, data.frame(
ID = paste("MSigDBID:", i, sep = ""),
Description = names(dbtable_geneID)[i],
IC = NA,
Count = length(dbtable_geneID[[i]]),
Gene = paste(dbtable_symbol[[i]], collapse = "/"),
GeneID = paste(dbtable_geneID[[i]], collapse = "/")))
}
human_MSigDB <- list(cell = res)
# Save data.
# save(human_MSigDB, file = "genes2bioterm/20220308_human_MSigDB.rda")
The data were stored in the following repositories:
7 Collect CellMarker for human cells
Load databases.
dbtable <- clustermole::clustermole_markers()
sort(unique(dbtable$db))
[1] "ARCHS4" "CellMarker" "MSigDB" "PanglaoDB" "SaVanT" "TISSUES"
[7] "xCell"
Select species and databases.
dbtable <- dbtable[which(dbtable$species == "Human"), ]
dbtable <- dbtable[which(dbtable$db == "CellMarker"),]
dbtable$geneID <- NA
Change gene symbols into entrez IDs.
dictionary <- AnnotationDbi::select(org.Hs.eg.db::org.Hs.eg.db,
key = dbtable$gene_original,
columns = c("SYMBOL", "ENTREZID"),
keytype = "SYMBOL")
dictionary <- dictionary[!duplicated(dictionary$SYMBOL), ]
dictionary <- dictionary[which(!is.na(dictionary$SYMBOL)),]
for(i in 1:nrow(dbtable)){
gene <- dbtable$gene_original[i]
inds <- which(dictionary$SYMBOL == gene)
dbtable$geneID[i] <- dictionary[inds,]$ENTREZID
}
Reformat the database. Here, the identifier of each biological term are named “CellMarkerID.”
dbtable_geneID <- split(x = dbtable$geneID, f = dbtable$celltype)
dbtable_symbol <- split(x = dbtable$gene_original, f = dbtable$celltype)
stopifnot(identical(length(dbtable_geneID), length(dbtable_symbol)))
res <- c("ID", "Description", "Count", "Gene", "GeneID", "IC")
res <- data.frame(matrix(ncol = 6, nrow = 0, dimnames = list(NULL, res)))
for(i in 1:length(dbtable_geneID)){
res <- rbind(res, data.frame(
ID = paste("CellMarkerID:", i, sep = ""),
Description = names(dbtable_geneID)[i],
IC = NA,
Count = length(dbtable_geneID[[i]]),
Gene = paste(dbtable_symbol[[i]], collapse = "/"),
GeneID = paste(dbtable_geneID[[i]], collapse = "/")))
}
human_CellMarker <- list(cell = res)
# Save data.
# save(human_CellMarker, file = "genes2bioterm/20220308_human_CellMarker.rda")
The data were stored in the following repositories:
8 Create a custom-built database
8.1 Combine CO and MSigDB for human cells
Create a cell type-related database by combining Cell ontology and MSigDB databases for analyzing human single-cell transcriptome data.
urlpath <- "https://github.com/keita-iida/ASURATDB/blob/main/genes2bioterm/"
load(url(paste0(urlpath, "20201213_human_CO.rda?raw=true")))
load(url(paste0(urlpath, "20220308_human_MSigDB.rda?raw=true")))
res <- rbind(human_CO[["cell"]], human_MSigDB[["cell"]])
human_CB <- list(cell = res)
8.2 Combine CO, MSigDB, and CellMarker for human cells
Create a cell type-related database by combining Cell ontology, MSigDB, and CellMarker databases for analyzing human single-cell transcriptome data.
urlpath <- "https://github.com/keita-iida/ASURATDB/blob/main/genes2bioterm/"
load(url(paste0(urlpath, "20201213_human_CO.rda?raw=true")))
load(url(paste0(urlpath, "20220308_human_MSigDB.rda?raw=true")))
load(url(paste0(urlpath, "20220304_human_CellMarker.rda?raw=true")))
res <- do.call("rbind", list(human_CO[["cell"]], human_MSigDB[["cell"]],
human_CellMarker[["cell"]]))
human_CB <- list(cell = res)
8.3 Combine DO, CO, and MSigDB for human cells
Create a cell type-related database by combining Disease Ontology, Cell ontology and MSigDB databases for analyzing complex human single-cell transcriptome data.
urlpath <- "https://github.com/keita-iida/ASURATDB/blob/main/genes2bioterm/"
load(url(paste0(urlpath, "20201213_human_DO.rda?raw=true")))
load(url(paste0(urlpath, "20201213_human_CO.rda?raw=true")))
load(url(paste0(urlpath, "20220308_human_MSigDB.rda?raw=true")))
res <- do.call("rbind", list(human_DO[["disease"]], human_CO[["cell"]],
human_MSigDB[["cell"]]))
human_CB <- list(cell = res)
8.4 Combine DO, CO, MSigDB, and CellMarker for human cells
Create a cell type-related database by combining Disease Ontology, Cell ontology, MSigDB, and CellMarker databases for analyzing complex human single-cell transcriptome data.
urlpath <- "https://github.com/keita-iida/ASURATDB/blob/main/genes2bioterm/"
load(url(paste0(urlpath, "20201213_human_DO.rda?raw=true")))
load(url(paste0(urlpath, "20201213_human_CO.rda?raw=true")))
load(url(paste0(urlpath, "20220308_human_MSigDB.rda?raw=true")))
load(url(paste0(urlpath, "20220304_human_CellMarker.rda?raw=true")))
res <- do.call("rbind", list(human_DO[["disease"]], human_CO[["cell"]],
human_MSigDB[["cell"]], human_CellMarker[["cell"]]))
human_CB <- list(cell = res)
9 Session information
sessionInfo()
#> R version 4.2.1 (2022-06-23)
#> Platform: x86_64-apple-darwin17.0 (64-bit)
#> Running under: macOS Big Sur ... 10.16
#>
#> Matrix products: default
#> BLAS: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRblas.0.dylib
#> LAPACK: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRlapack.dylib
#>
#> locale:
#> [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
#>
#> attached base packages:
#> [1] stats graphics grDevices utils datasets methods base
#>
#> loaded via a namespace (and not attached):
#> [1] compiler_4.2.1 fastmap_1.1.0 cli_3.6.0 tools_4.2.1
#> [5] htmltools_0.5.4 rstudioapi_0.14 yaml_2.3.7 rmarkdown_2.20
#> [9] knitr_1.42 xfun_0.37 digest_0.6.31 rlang_1.0.6
#> [13] evaluate_0.20
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