plot_reduced: plot reduced data

In scGPS: A complete analysis of single cell subpopulations, from identifying subpopulations to analysing their relationship (scGPS = single cell Global Predictions of Subpopulation)

Description

plot PCA, tSNE, and CIDR reduced datasets

Usage

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))
p2 <- plot_reduced(tSNE_dim, color_fac = factor(colData(mixedpop1)[,1]),
    palletes = seq_len(length(unique(colData(mixedpop1)[,1]))))
plot(p2)

scGPS documentation built on Nov. 8, 2020, 5:22 p.m.