DO Heatmap of the mean expression of genes across a groups

Wrapper around heatmap.py, which generates a heatmap of showing the average nUMI for a set of genes in different groups. Differential gene expression analysis between the different groups can be performed.

Usage

DO.Heatmap(
  sce_object,
  assay_normalized = "RNA",
  group_by = "seurat_clusters",
  features,
  z_score = NULL,
  path = NULL,
  filename = "Heatmap.svg",
  swap_axes = TRUE,
  cmap = "Reds",
  title = NULL,
  title_fontprop = NULL,
  clustering_method = "complete",
  clustering_metric = "euclidean",
  cluster_x_axis = FALSE,
  cluster_y_axis = FALSE,
  axs = NULL,
  figsize = c(5, 6),
  linewidth = 0.1,
  ticks_fontdict = NULL,
  xticks_rotation = NULL,
  yticks_rotation = NULL,
  vmin = 0,
  vcenter = NULL,
  vmax = NULL,
  legend_title = "LogMean(nUMI)\nin group",
  add_stats = TRUE,
  df_pvals = NULL,
  stats_x_size = NULL,
  square_x_size = NULL,
  test = "wilcox",
  pval_cutoff = 0.05,
  log2fc_cutoff = 0,
  only_pos = TRUE,
  square = TRUE,
  showP = TRUE,
  logcounts = TRUE
)

Arguments

sce_object: SCE object or Seurat with meta.data
assay_normalized: Assay with raw counts
group_by: meta data column name with categorical values
features: gene names or continuous value in meta data
z_score: apply z-score transformation
path: path to save the plot
filename: name of the file
swap_axes: whether to swap the axes or not
cmap: color map
title: title for the main plot
title_fontprop: font properties for the title (e.g., 'weight' and 'size')
clustering_method: clustering method to use when hierarchically clustering the x and y-axis
clustering_metric: metric to use when hierarchically clustering the x and y-axis
cluster_x_axis: hierarchically clustering the x-axis
cluster_y_axis: hierarchically clustering the y-axis
axs: matplotlib axis
figsize: figure size
linewidth: line width for the border of cells
ticks_fontdict: font properties for the x and y ticks (e.g., 'weight' and 'size')
xticks_rotation: rotation of the x-ticks
yticks_rotation: rotations of the y-ticks
vmin: minimum value
vcenter: center value
vmax: maximum value
legend_title: title for the color bar
add_stats: add statistical annotation, will add a square with an '*' in the center if the expression is significantly different in a group with respect to the others
df_pvals: dataframe with the p-values, should be gene x group or group x gene in case of swap_axes is False
stats_x_size: size of the asterisk
square_x_size: size and thickness of the square
test: test to use for test for significance
pval_cutoff: cutoff for the p-value
log2fc_cutoff: minimum cutoff for the log2FC
only_pos: if set to TRUE, only use positive genes in the condition
square: whether to make the cell square or not
showP: if set to false return a dictionary with the axis
logcounts: whether the input is logcounts or not

Value

Depending on showP, returns the plot if set to TRUE or a dictionary with the axes.

Author

Mariano Ruz Jurado & David Rodriguez Morales

Examples

#'
sce_data <- readRDS(system.file("extdata", "sce_data.rds", package = "DOtools"))

DO.Heatmap(
sce_object = sce_data,
assay_normalized = "RNA",
group_by="seurat_clusters",
features = rownames(sce_data)[1:10],
z_score = NULL,
path = NULL,
filename = "Heatmap.svg",
swap_axes = TRUE,
cmap = "Reds",
title = NULL,
title_fontprop = NULL,
clustering_method = "complete",
clustering_metric = "euclidean",
cluster_x_axis = FALSE,
cluster_y_axis = FALSE,
axs = NULL,
figsize = c(5, 6),
linewidth = 0.1,
ticks_fontdict = NULL,
xticks_rotation = 45,
yticks_rotation = NULL,
vmin = 0.0,
vcenter = NULL,
vmax = NULL,
legend_title = "LogMean(nUMI)\nin group",
add_stats = TRUE,
df_pvals = NULL,
stats_x_size = NULL,
square_x_size = NULL,
test = "wilcox",
pval_cutoff = 0.05,
log2fc_cutoff = 0,
only_pos = TRUE,
square = TRUE,
showP = FALSE,
logcounts = TRUE
)
#> Calculating cluster 1
#> Warning: No features pass logfc.threshold threshold; returning empty data.frame
#> Calculating cluster 2
#> Calculating cluster 3
#> Calculating cluster 4
#> Calculating cluster 5
#> Calculating cluster 6
#> Calculating cluster 7
#> Warning: No features pass logfc.threshold threshold; returning empty data.frame
#> Calculating cluster 8