scCNVis: Single-Cell Copy Number Visualization

scCNVis is a visualization tool for single-cell copy number data.

Multiple tools exist for calling copy number variants using single cell data. Most of those tools include default plotting methods which work well in most cases, especially since many users want to simply use the copy number calls as a way to confirm the cancer status of individual cells. However, in situations where a deeper exploration of single-cell copy number results is desired, the basic plotting methods of those tools are insufficient. This package contains helper functions to support exploration of copy number data and gain novel biological insights.

Developed by Jace Webster while working in the lab of David Quigley at UCSF.

View the full documenation here

Installation 

devtools::install_github("jbwebster/scCNVis")

Some dependencies may fail to install because they must be installed through BiocManager rather than standard CRAN. If that is the case, you can try to install those dependencies by doing the following:

if (!require("BiocManager", quietly = TRUE) )
    install.packages("BiocManager")
BiocManager::install("ComplexHeatmap")
BiocManager::install("GenomicRanges")

Quickstart

A high-level overview of how the package works is outlined below. See full documentation for further explanations and details, including alternative plot types.

scCNVis works with scCNVis objects. This provides a common format that can be created using the outputs from any of the common CNV callers. To create a scCNVis object, you need:

  1. A matrix (rows = cells, columns = regions) with copy number values
  2. A list of cell names (should be the same length as nrows(matrix))
  3. A GenomicRanges::GRanges object describing the columns of the matrix
  4. A metadata data.frame that minimally has rownames as cell names and a column named “Sample”

You can start with the provided example data to get a sense of what these inputs look like:

library(scCNVis)
#Load the data that comes with scCNVis
input.matrix <- example.matrix
cell.names <- example.cells
granges <- example.granges
meta <- example.meta
obj <- scCNVis::createPlotObject(input.matrix, cell.names, granges, meta)

Alternatively, you can create a scCNVisObject directly from an infercnv object, by using a helper function:

library(scCNVis)
#Load the infercnv object that comes with scCNVis
inferncv.obj <- example.infercnv.obj
obj <- scCNVis::createPlotObjectFromInfercnv(infercnv.obj, "Example.Sample")

The resulting scCNVisObject output can then be passed into any of the available plotting functions. For example:

heatmap.result <- scCNVis::makeSCHeatmap(obj)

#If using RStudio, you can view the plot in the IDE:
heatmap.result$Plot

The output of the plotting functions is a list with two items. The first is the Plot and the second is the PlotData that was used to make the plot in case you wish to create your own version of the plot or inspect the data more thoroughly. If you wish to simply export the plot, it can be done using the wrapper function:

scCNVis::saveCustomPlot(heatmap.result, "~/path/to/heatmap.png")