plotHeatmap: plotHeatmap

In navinlabcode/copykit: CopyKit

plotHeatmap

Description

Plots a heatmap of the copy number data. Each row is a cell and colums represent genomic positions.

Usage

plotHeatmap(
  scCNA,
  assay = "segment_ratios",
  order_cells = NULL,
  label = NULL,
  label_colors = NULL,
  group = NULL,
  consensus = FALSE,
  rounding_error = FALSE,
  genes = NULL,
  col = NULL,
  row_split = NULL,
  use_raster = TRUE,
  raster_quality = 2,
  n_threads = 1
)

Arguments

scCNA	The CopyKit object.
assay	String with the assay to pull data from to plot heatmap.
order_cells	A string with the desired method to order the cells within
label	A vector with the string names of the columns from colData for heatmap annotation.
label_colors	A named list with colors for the label annotation. Must match label length and have the same names as label
group	with the names of the columns from colData to add a barplot with frequency of the groups to a consensus heatmap.
consensus	A boolean indicating if the consensus heatmap should be plotted.
rounding_error	A boolean indicating if the rounding error matrix should be plotted.
genes	A character vector with the HUGO symbol for genes to annotate on the heatmap.
col	A colorRamp2 vector that controls the color scale of the heatmap. See Heatmap or ComplexHeatmap online docs for help.
row_split	A string with the names of the columns from colData to split the heatmap.
use_raster	Whether render the heatmap body as a raster image. It helps to reduce file size when the matrix is huge. If number of rows or columns is more than 2000, it is by default turned on. Note if cell_fun is set, use_raster is enforced to be FALSE. see Heatmap.
raster_quality	A value larger than 1. Larger values increase the file size.
n_threads	Number of threads passed on to runDistMat.

Details

• order_cells: If order_cells argument is set to 'consensus_tree' plotHeatmap checks for the existence of a consensus matrix. From the consensus matrix, a minimum evolution tree is built and cells are ordered following the order of their respective groups from the tree. If order_cells is 'hclust' cells are ordered according to hierarchical clustering. 'hclust' calculation can be sped up by changing the parameter 'n_threads' if you have more threads available to use. If order_cells is NULL the order of cells will be the same as the current order inside the CopyKit object (colnames(CopyKit)).

• label: A vector with the string names of the columns from colData for heatmap annotation. The 'label' argument can take as many columns as desired as argument as long as they are elements from colData.

• label_colors: A named list, list element names must match column names for colData and list elements must match the number of items present in the columns provided in argument 'label'. For example: to set colors for column 'outlier' containing elements 'TRUE' or 'FALSE' a valid input would be: 'list(outlier = c('FALSE' = 'green', 'TRUE' = 'red))'. Default colors are provided for 'superclones', 'subclones', 'is_aneuploid', and 'outlier' that can be override with 'label_colors'.

• rounding_error: Must be used with assay = 'integer'. plotHeatmap will access the ploidies into colData(scCNA)$ploidy that are generated from calcInteger and scale rounded integer values to the segment means. Later this scaled matrix will be subtracted from the 'integer' assay from calcInteger and the resulting matrix from this subtraction will be plotted. Useful to visualize regions of high rounding error. Such regions can indicate issues with the ploidy scaling in use.

• consensus: If set to TRUE, plotHeatmap will search for the consensus matrix in the slot consensus and plot the resulting matrix. Labels annotations can be added with the argument 'label'.

Value

A ComplexHeatmap object with a heatmap of copy number data where the columns are the genomic positions and each row is a cell.

Author(s)

Darlan Conterno Minussi

References

Zuguang Gu, Roland Eils, Matthias Schlesner, Complex heatmaps reveal patterns and correlations in multidimensional genomic data, Bioinformatics, Volume 32, Issue 18, 15 September 2016, Pages 2847–2849, https://doi.org/10.1093/bioinformatics/btw313

See Also

calcInteger.

Examples

copykit_obj <- copykit_example_filtered()
set.seed(1000)
copykit_obj <- copykit_obj[, sample(200)]
copykit_obj <- findClusters(copykit_obj)
copykit_obj <- calcConsensus(copykit_obj)
copykit_obj <- runConsensusPhylo(copykit_obj)
colData(copykit_obj)$section <- stringr::str_extract(
    colData(copykit_obj)$sample,
    "(L[0-9]+L[0-9]+|L[0-9]+)"
)
plotHeatmap(copykit_obj, label = c("section", "subclones"))

navinlabcode/copykit documentation built on Oct. 16, 2024, 2:55 p.m.