AUCell_plotTSNE: Plot

In AUCell: AUCell: Analysis of 'gene set' activity in single-cell RNA-seq data (e.g. identify cells with specific gene signatures)

Description

Plots the AUC histogram and t-SNE coloured by AUC, binary activity and TF expression

Usage

AUCell_plotTSNE(
  tSNE,
  exprMat = NULL,
  cellsAUC = NULL,
  thresholds = NULL,
  reorderGeneSets = FALSE,
  cex = 1,
  alphaOn = 1,
  alphaOff = 0.2,
  borderColor = adjustcolor("lightgray", alpha.f = 0.1),
  offColor = "lightgray",
  plots = c("histogram", "binaryAUC", "AUC", "expression"),
  exprCols = c("goldenrod1", "darkorange", "brown"),
  asPNG = FALSE,
  ...
)

Arguments

tSNE	t-SNE coordinates (e.g. tSNE$Y)
exprMat	Expression matrix
cellsAUC	AUC (as returned by calcAUC)
thresholds	Thresholds returned by AUCell
reorderGeneSets	Whether to reorder the gene sets based on AUC similarity
cex	Scaling factor for the dots in the scatterplot
alphaOn	Transparency for the dots representing "active" cells
alphaOff	Transparency for the dots representing "inactive" cells
borderColor	Border color for the dots (scatterplot)
offColor	Color for the dots representing "inactive" cells
plots	Which plots to generate? Select one or multiple: plots=c("histogram", "binaryAUC", "AUC", "expression")
exprCols	Color scale for the expression
asPNG	Output each individual plot in a .png file? (can also be a directory)
...	Other arguments to pass to hist function.

Details

To avoid calculating thresholds, set thresholds to FALSE

Value

Returns invisible: cells_trhAssignment

See Also

List of vignettes included in the package: vignette(package="AUCell")

Examples

######
# Fake run of AUCell
set.seed(123)
exprMatrix <- matrix(
  data=sample(c(rep(0, 5000), sample(1:3, 5000, replace=TRUE))),
  nrow=20, 
  dimnames=list(paste("Gene", 1:20, sep=""), 
                paste("Cell", 1:500, sep="")))
geneSets <- list(geneSet1=sample(rownames(exprMatrix), 10),
                 geneSet2=sample(rownames(exprMatrix), 5))

cells_rankings <- AUCell_buildRankings(exprMatrix, plotStats = FALSE)
cells_AUC <- AUCell_calcAUC(geneSets, cells_rankings, aucMaxRank=5, nCores=1)
selectedThresholds <- rowMeans(getAUC(cells_AUC))
# cellsTsne<- Rtsne::Rtsne(t(exprMatrix),max_iter = 10)$Y
cellsTsne<- tsne::tsne(t(exprMatrix),max_iter = 10)
rownames(cellsTsne) <- colnames(exprMatrix)
######


par(mfrow=c(2,3))
thrs <- AUCell_plotTSNE(tSNE=cellsTsne, exprMat=NULL,
                        cellsAUC=cells_AUC, thresholds=selectedThresholds, 
                        plots = c("histogram", "binaryAUC", "AUC"))
 


#####
# Color based on the known phenodata:
cellInfo <- data.frame(cellType1=sample(LETTERS[1:3],ncol(exprMatrix), replace=TRUE), 
                       cellType2=sample(letters[5:7],ncol(exprMatrix), replace=TRUE), 
                       nGenes=abs(rnorm(ncol(exprMatrix))), 
                       row.names=colnames(exprMatrix))
colVars <- list(cellType2=setNames(c("skyblue","magenta", "darkorange"),letters[5:7]))
# dev.off()
plotTsne_cellProps(cellsTsne, cellInfo, colVars=colVars)

AUCell documentation built on Nov. 8, 2020, 5:51 p.m.