plot reduced data

Source: R/Utilities.R

plot PCA, tSNE, and CIDR reduced datasets

plot_reduced(
  reduced_dat,
  color_fac = NULL,
  dims = c(1, 2),
  dimNames = c("Dim1", "Dim2"),
  palletes = NULL,
  legend_title = "Cluster"
)

Arguments

reduced_dat

is a matrix with genes in rows and cells in columns
color_fac

is a vector of colors corresponding to clusters to determine colors of scattered plots
dims

an integer of the number of dimestions
dimNames

a vector of the names of the dimensions
palletes

can be a customised color pallete that determine colors for density plots, if NULL it will use RColorBrewer colorRampPalette(RColorBrewer::brewer.pal(sample_num, 'Set1'))(sample_num)
legend_title

title of the plot's legend

Value

a matrix with the top 20 CIDR dimensions

Examples

day2 <- day_2_cardio_cell_sample
mixedpop1 <-new_scGPS_object(ExpressionMatrix = day2$dat2_counts, 
    GeneMetadata = day2$dat2geneInfo, CellMetadata = day2$dat2_clusters)
#CIDR_dim <-CIDR(expression.matrix=assay(mixedpop1))
#p <- plot_reduced(CIDR_dim, color_fac = factor(colData(mixedpop1)[,1]),
#     palletes = seq_len(length(unique(colData(mixedpop1)[,1]))))
#plot(p)
tSNE_dim <-tSNE(expression.mat=assay(mixedpop1))

#> Preparing PCA inputs using the top 1500 genes ...
#> Computing PCA values...
#> Running tSNE ...
p2 <- plot_reduced(tSNE_dim, color_fac = factor(colData(mixedpop1)[,1]),
    palletes = seq_len(length(unique(colData(mixedpop1)[,1]))))

#> Warning: Use of `reduced_dat_toPlot$Dim1` is discouraged. Use `Dim1` instead.
#> Warning: Use of `reduced_dat_toPlot$Dim2` is discouraged. Use `Dim2` instead.
plot(p2)