FLAMES isoform reduced dimensions plots — plot_isoform_reduced

Plot expression of top n isoforms of a gene in reduced dimensions

Usage

plot_isoform_reduced_dim(
  sce,
  gene_id,
  transcript_ids,
  n = 4,
  reduced_dim_name = "UMAP",
  use_gene_dimred = FALSE,
  expr_func = function(x) {
     SingleCellExperiment::logcounts(x)
 },
  col_low = "#313695",
  col_mid = "#FFFFBF",
  col_high = "#A50026",
  color_quantile = 1,
  format = "plot_grid",
  ...
)

Arguments

sce: The SingleCellExperiment object containing transcript counts, rowRanges and rowData with gene_id and transcript_id columns.
gene_id: The gene symbol of interest, ignored if transcript_ids is provided.
transcript_ids: The transcript ids to plot.
n: The number of top isoforms to plot from the gene. Ignored if transcript_ids is provided.
reduced_dim_name: The name of the reduced dimension to use for plotting cells.
use_gene_dimred: Whether to use gene-level reduced dimensions for plotting. Set to TRUE if the SingleCellExperiment has gene counts in main assay and transcript counts in altExp.
expr_func: The function to extract expression values from the SingleCellExperiment object. Default is logcounts. Alternatively, counts can be used for raw counts.
col_low: Color for cells with low expression levels in UMAPs.
col_mid: Color for cells with intermediate expression levels in UMAPs.
col_high: Color for cells with high expression levels in UMAPs.
color_quantile: The lower and upper expression quantile to be displayed bewteen col_low and col_high, e.g. with color_quantile = 0.95, cells with expressions higher than 95% of other cells will all be shown in col_high, and cells with expression lower than 95% of other cells will all be shown in col_low.
format: The format of the output, either "plot_grid" or "list".
...: Additional arguments to pass to plot_grid.

Value

a ggplot object of the UMAP(s)

Details

Takes SingleCellExperiment object and plots an expression on reduced dimensions with the isoform visualizations along genomic coordinates.

Examples

scmixology_lib10 <- 
  scmixology_lib10[, colSums(SingleCellExperiment::counts(scmixology_lib10)) > 0]
sce_lr <- scmixology_lib10[, colnames(scmixology_lib10) %in% colnames(scmixology_lib10_transcripts)]
SingleCellExperiment::altExp(sce_lr, "transcript") <-
  scmixology_lib10_transcripts[, colnames(sce_lr)]
combined_sce <- combine_sce(sce_lr, scmixology_lib90)
combined_sce <- combined_sce |>
  scuttle::logNormCounts() |>
  scater::runPCA() |>
  scater::runUMAP()
combined_imputed_sce <- sc_impute_transcript(combined_sce)
#> Imputing transcript counts ...
plot_isoform_reduced_dim(combined_sce, 'ENSG00000108107')
#> Constructing graphics...
#> Constructing graphics...
#> Constructing graphics...
#> Constructing graphics...

plot_isoform_reduced_dim(combined_imputed_sce, 'ENSG00000108107')
#> Constructing graphics...
#> Constructing graphics...
#> Constructing graphics...
#> Constructing graphics...