Create binary plots

Create binary plots that show similarity between single cells and two selected terminals in a barycentric coordinate. The two vertices are placed at the left and right of a 2D plot where x-axis measures the similarity. Y-axis is jittered for a clear view. A density (histogram) curve is added for indicating the distribution.

See plotTernary manual for more details.

Usage

plotBinary(x, ...)

# S3 method for default
plotBinary(
  x,
  clusterVar,
  vertices,
  features = NULL,
  byCluster = NULL,
  processed = FALSE,
  method = c("euclidean", "cosine", "pearson", "spearman"),
  force = FALSE,
  sigma = 0.08,
  scale = TRUE,
  dotColor = "grey60",
  returnData = FALSE,
  ...
)

# S3 method for Seurat
plotBinary(
  x,
  layer = "counts",
  assay = NULL,
  clusterVar = NULL,
  processed = FALSE,
  ...
)

# S3 method for SingleCellExperiment
plotBinary(x, assay.type = "counts", clusterVar = NULL, processed = FALSE, ...)

# S3 method for simMat
plotBinary(
  x,
  dotSize = 0.6,
  dotColor = "grey60",
  densLinewidth = 0.8,
  labelColors = c("#3B4992FF", "#EE0000FF"),
  title = NULL,
  ...
)

Arguments

x

Input data. Can be a matrix or dgCMatrix object with cells as columns, a Seurat or SingleCellExperiment object. "simMat" method takes intermediate values.

...

Arguments passed to other methods.

clusterVar

A vector/factor assigning the cluster variable to each column of the matrix object. For "Seurat" method, NULL (default) for Idents(x), or a variable name in meta.data slot. For "SingleCellExperiment" method, NULL (default) for colLabels(x), or a variable name in colData slot.

vertices

Vector of three unique cluster names that will be used for plotting. Or a named list that groups clusters as three terminal vertices. There must not be any overlap between groups.

features

Valid matrix row subsetting index to select features for similarity calculation. Default NULL uses all available features.

byCluster

Default NULL to generate one plot with all cells. Set "all" to split cells in plot by cluster and returns a list of subplots for each cluster as well as the plot including all cells. Otherwise, a vector of cluster names to generate a list of subplots for the specified clusters.

processed

Logical. Whether the input matrix is already processed. TRUE will bypass internal preprocessing and input matrix will be directly used for similarity calculation. Default FALSE and raw count input is recommended. If missing in call, using slot = "counts" in "Seurat" method or using assay.type = "counts" in "SingleCellExperiment" method will force this argument to be FALSE and others for TRUE.

method

Similarity calculation method. Default "euclidean". Choose from "euclidean", "cosine", "pearson", "spearman".

force

Whether to force calculate the similarity when more then 500 features are detected, which is generally not recommended. Default FALSE.

sigma

Gaussian kernel parameter that controls the effect of variance. Only effective when using a distance metric (i.e. method is "euclidian" or "cosine"). Larger value tighten the dot spreading on figure. Default 0.08.

scale

Whether to min-max scale the distance matrix by clusters. Default TRUE.

returnData

Logical. Whether to return similarity data instead of generating plot. Default FALSE.

layer

For "Seurat" method, which layer of the assay to be used. Default "counts".

assay

For "Seurat" method, the specific assay to get data from. Default NULL to the default assay.

assay.type

For "SingleCellExperiment" methods. Which assay to use for calculating the similarity. Default "counts".

dotSize, dotColor

Dot aesthetics passed to geom_point. Default 0.6 and "grey60".

densLinewidth

Density plot line aesthetic. Default 0.8.

labelColors

Color of the axis lines and vertex labels. Default c("#3B4992FF", "#EE0000FF") (blue and red).

title

Title text of the plot. Default NULL.

Value

For 'simMat' method, a ggplot object. For other methods, a ggplot object when splitCluster = FALSE, or a list of ggplot objects when splitCluster = TRUE.

Examples

gene <- selectTopFeatures(rnaRaw, rnaCluster, c("RE", "OS"))
#> Selected 30 features for "OS".
#> Selected 30 features for "RE".
plotBinary(rnaRaw, rnaCluster, c("RE", "OS"), gene)

# \donttest{
# Seurat example
library(Seurat)
srt <- CreateSeuratObject(rnaRaw)
#> Warning: Feature names cannot have underscores ('_'), replacing with dashes ('-')
Idents(srt) <- rnaCluster
gene <- selectTopFeatures(srt, vertices = c("OS", "RE"))
#> Selected 30 features for "OS".
#> Selected 30 features for "RE".
plotBinary(srt, features = gene, vertices = c("OS", "RE"))

# }
# \donttest{
# SingleCellExperiment example
library(SingleCellExperiment)
sce <- SingleCellExperiment(assays = list(counts = rnaRaw))
colLabels(sce) <- rnaCluster
gene <- selectTopFeatures(sce, vertices = c("OS", "RE"))
#> Selected 30 features for "OS".
#> Selected 30 features for "RE".
plotBinary(sce, features = gene, vertices = c("OS", "RE"))

# }