plot_pseudotime_heatmap2: Plots a pseudotime-ordered, row-centered heatmap which is slightly modified in monocle2

Description

The function plot_pseudotime_heatmap takes a CellDataSet object (usually containing a only subset of significant genes) and generates smooth expression curves much like plot_genes_in_pseudotime. Then, it clusters these genes and plots them using the pheatmap package. This allows you to visualize modules of genes that co-vary across pseudotime.

Usage

plot_pseudotime_heatmap2(
  cds_subset,
  cluster_rows = TRUE,
  hclust_method = "ward.D2",
  num_clusters = 6,
  hmcols = NULL,
  add_annotation_row = NULL,
  add_annotation_col = NULL,
  show_rownames = FALSE,
  use_gene_short_name = TRUE,
  norm_method = c("log", "vstExprs"),
  scale_max = 3,
  scale_min = -3,
  trend_formula = "~sm.ns(Pseudotime, df=3)",
  return_heatmap = FALSE,
  cores = 1
)

Value

A list of heatmap_matrix (expression matrix for the branch committment), ph (pheatmap heatmap object), annotation_row (annotation data.frame for the row), annotation_col (annotation data.frame for the column).

Arguments

cds_subset: CellDataSet for the experiment (normally only the branching genes detected with branchTest)
cluster_rows: Whether to cluster the rows of the heatmap.
hclust_method: The method used by pheatmap to perform hirearchical clustering of the rows.
num_clusters: Number of clusters for the heatmap of branch genes
hmcols: The color scheme for drawing the heatmap.
add_annotation_row: Additional annotations to show for each row in the heatmap. Must be a dataframe with one row for each row in the fData table of cds_subset, with matching IDs.
add_annotation_col: Additional annotations to show for each column in the heatmap. Must be a dataframe with one row for each cell in the pData table of cds_subset, with matching IDs.
show_rownames: Whether to show the names for each row in the table.
use_gene_short_name: Whether to use the short names for each row. If FALSE, uses row IDs from the fData table.
norm_method: Determines how to transform expression values prior to rendering
scale_max: The maximum value (in standard deviations) to show in the heatmap. Values larger than this are set to the max.
scale_min: The minimum value (in standard deviations) to show in the heatmap. Values smaller than this are set to the min.
trend_formula: A formula string specifying the model used in fitting the spline curve for each gene/feature.
return_heatmap: Whether to return the pheatmap object to the user.
cores: Number of cores to use when smoothing the expression curves shown in the heatmap.