## ----setup,echo=TRUE---------------------------------------------------------- suppressPackageStartupMessages({ library(knitr) library(ontoProc) go = getGeneOnto() cl = getCellOnto() pr = getPROnto() library(ontologyPlot) library(BiocStyle) }) ## ----lksco-------------------------------------------------------------------- kable(packDesc2019) ## ----lklk--------------------------------------------------------------------- kable(stab <- seur3kTab()) ## ----lklklk------------------------------------------------------------------- library(ontoProc) cl = getCellOnto() onto_plot2(cl, stab$tag) ## ----lkfa--------------------------------------------------------------------- kable(CLfeats(cl, "CL:0002531")) ## ----lksy--------------------------------------------------------------------- kable(sdf <- as.data.frame(sym2CellOnto("ITGAM", cl, pr))) table(sdf$cond) kable(as.data.frame(sym2CellOnto("FOXP3", cl, pr))) ## ----lksig-------------------------------------------------------------------- sigels = c("CL:X01"="GRIK3", "CL:X02"="NTNG1", "CL:X03"="BAGE2", "CL:X04"="MC4R", "CL:X05"="PAX6", "CL:X06"="TSPAN12", "CL:X07"="hSHISA8", "CL:X08"="SNCG", "CL:X09"="ARHGEF28", "CL:X10"="EGF") ## ----lkdfff------------------------------------------------------------------- cs = cyclicSigset(sigels) dim(cs) cs[c(1:5,9:13),] table(cs$cond) ## ----lklk1-------------------------------------------------------------------- makeIntnProlog = function(id, ...) { # make type-specific prologs as key-value pairs c( sprintf("id: %s", id), sprintf("name: %s-expressing cortical layer 1 interneuron, human", ...), sprintf("def: '%s-expressing cortical layer 1 interneuron, human described via RNA-seq observations' [PMID 29322913]", ...), "is_a: CL:0000099 ! interneuron", "intersection_of: CL:0000099 ! interneuron") } ## ----doterm------------------------------------------------------------------- pmap = c("hasExp"="has_expression_of", lacksExp="lacks_expression_of") head(unlist(tms <- ldfToTerms(cs, pmap, sigels, makeIntnProlog)), 20) ## ----lkmap-------------------------------------------------------------------- hpca_map = read.csv(system.file("extdata/hpca.csv", package="ontoProc"), strings=FALSE) head(hpca_map) ## ----doren-------------------------------------------------------------------- names(hpca_map) = c("informal", "formal") # obligatory for now ## ----gethpca, eval=TRUE------------------------------------------------------- library(SingleCellExperiment) library(SingleR) hpca_sce = HumanPrimaryCellAtlasData() ## ----dobind, eval=TRUE-------------------------------------------------------- hpca_sce = bind_formal_tags(hpca_sce, "label.fine", hpca_map) length(unique(hpca_sce$label.ont)) ## ----justna, eval=TRUE-------------------------------------------------------- length(xx <- which(is.na(hpca_sce$label.ont))) if (length(xx)>0) print(colData(hpca_sce)[xx,]) sum(hpca_sce$label.ont == "", na.rm=TRUE) # iPS and BM ## ----dosub, eval=TRUE--------------------------------------------------------- cell_onto = ontoProc::getCellOnto() hpca_mono = subset_descendants( hpca_sce, cell_onto, "^monocyte$" ) table(hpca_mono$label.fine) table(hpca_mono$label.ont) # not much diversity hpca_tcell = subset_descendants( hpca_sce, cell_onto, "^T cell$" ) table(hpca_tcell$label.fine) table(hpca_tcell$label.ont) # uu = unique(hpca_tcell$label.ont) onto_plot2(cell_onto, uu)