Example apps to demonstrate the use of a reduced dimension on the x-axis of colDataPlot and featAssayPlot

iSEE-xaxis-reddim.R

library(scRNAseq)
data(allen)
class(allen)

# Example data ----

library(scater)
sce <- as(allen, "SingleCellExperiment")
counts(sce) <- assay(sce, "tophat_counts")
sce <- normalize(sce)

sce <- runPCA(sce, ncomponents=4)
sce <- runTSNE(sce)
rowData(sce)$ave_count <- rowMeans(counts(sce))
rowData(sce)$n_cells <- rowSums(counts(sce)>0)
sce

# App of colDataPlot, featAssay and redDimPlot side by side ----

initialPanels <- DataFrame(
    Name = c(
        "Column data plot 1",
        "Reduced dimension plot 1",
        "Feature assay plot 1",
        "Reduced dimension plot 2"
    ),
    Width = rep(6L, 4)
)

colDataArgs <- colDataPlotDefaults(sce, 1)
colDataArgs$DataBoxOpen <- TRUE
colDataArgs$YAxis <- "driver_1_s"
colDataArgs$XAxis <- "Reduced dimension"
colDataArgs$ColorBy <- "Column data"
colDataArgs$ColorByColData <- "driver_1_s"

# Find a gene highly correlated with PCA dimension 1
# pca1 <- reducedDim(sce)[, 1]
# logcountsMatrix <- assay(sce, "logcounts")
# cor1 <- apply(logcountsMatrix, 1, cor, y = pca1)
# max(cor1, na.rm = TRUE)
# which.max(cor1)

featAssayArgs <- featAssayPlotDefaults(sce, 1)
featAssayArgs$DataBoxOpen <- TRUE
featAssayArgs$YAxisFeatName <- "Lmo3"
featAssayArgs$Assay <- 6L
featAssayArgs$XAxis <- "Reduced dimension"
featAssayArgs$ColorBy <- "Feature name"
featAssayArgs$ColorByFeatName <- "Lmo3"
featAssayArgs$ColorByFeatNameAssay <- 6L

redDimArgs <- redDimPlotDefaults(sce, 2)
redDimArgs$DataBoxOpen <- TRUE
redDimArgs$ColorBy <- c("Column data", "Feature name")
redDimArgs$ColorByColData <- "driver_1_s"
redDimArgs$ColorByFeatName <- "Lmo3"
redDimArgs$ColorByFeatNameAssay <- 6L

app <- iSEE(
    sce,  initialPanels = initialPanels,
    colDataArgs = colDataArgs, featAssayArgs = featAssayArgs, redDimArgs = redDimArgs)
if (interactive()) {
  shiny::runApp(app, port=1234)
}