Evaluate Top Genes

Shows plots over bins of expression of the top n genes. This is designed to help identify if you have selected genes that vary over the pseudotime you have chosen bins to exist over. Uses the normcounts of the SCE.

Usage

evaluate_top_n_genes(blase_data, n_genes_to_plot = 16, plot_columns = 4)

Arguments

blase_data

The BlaseData to get bins and expression from.

n_genes_to_plot

The number of genes to plot.

plot_columns

The number of columns to plot the grid with. Best as a divisor of n_genes_to_plot.

Value

A plot showing the normalised expression of the top genes over pseudotime bins.

Examples

ncells <- 70
ngenes <- 100
counts_matrix <- matrix(
    c(seq_len(3500) / 10, seq_len(3500) / 5),
    ncol = ncells,
    nrow = ngenes
)
sce <- SingleCellExperiment::SingleCellExperiment(assays = list(
    normcounts = counts_matrix, logcounts = log(counts_matrix)
))
colnames(sce) <- paste0("cell", seq_len(ncells))
rownames(sce) <- paste0("gene", seq_len(ngenes))
sce$cell_type <- c(
    rep("celltype_1", ncells / 2),
    rep("celltype_2", ncells / 2)
)

sce$pseudotime <- seq_len(ncells) - 1
genelist <- rownames(sce)

# Evaluating created BlaseData
blase_data <- as.BlaseData(sce, pseudotime_slot = "pseudotime", n_bins = 10)
genes(blase_data) <- genelist[1:20]

# Check gene expression over pseudotime
evaluate_top_n_genes(blase_data)