Package 'escape'

Visualize the mean density ranking of genes across gene set

Description

This function allows to the user to examine the mean ranking within the groups across the gene set. The visualization uses the density function to display the relative position and distribution of rank.

Usage

densityEnrichment(
  input.data,
  gene.set.use = NULL,
  gene.sets = NULL,
  group.by = NULL,
  palette = "inferno"
)

Arguments

input.data	The single-cell object to use.
gene.set.use	Selected individual gene set.
gene.sets	The gene set library to use to extract the individual gene set information from.
group.by	Categorical parameter to plot along the x.axis. If input is a single-cell object the default will be cluster.
palette	Colors to use in visualization - input any hcl.pals.

Value

ggplot2 object mean rank gene density across groups

Examples

GS <- list(Bcells = c("MS4A1", "CD79B", "CD79A", "IGH1", "IGH2"),
           Tcells = c("CD3E", "CD3D", "CD3G", "CD7","CD8A"))
pbmc_small <- SeuratObject::pbmc_small
                        
densityEnrichment(pbmc_small, 
                  gene.set.use = "Tcells",
                  gene.sets = GS)

---

Built-In Gene Sets for escape

Description

A list of gene sets derived from Azizi, et al 2018 PMID: 29961579) relating to tumor immunity.

---

Calculate gene set enrichment scores

Description

This function allows users to input both the single-cell RNA-sequencing counts and output the enrichment scores as a matrix.

Usage

escape.matrix(
  input.data,
  gene.sets = NULL,
  method = "ssGSEA",
  groups = 1000,
  min.size = 5,
  normalize = FALSE,
  make.positive = FALSE,
  BPPARAM = SerialParam(),
  ...
)

Arguments

input.data	The count matrix, Seurat, or Single-Cell Experiment object.
gene.sets	Gene sets can be a list, output from getGeneSets, or the built-in gene sets in the escape package escape.gene.sets.
method	Select the method to calculate enrichment, AUCell, GSVA, ssGSEA or UCell.
groups	The number of cells to separate the enrichment calculation.
min.size	Minimum number of gene necessary to perform the enrichment calculation
normalize	Whether to divide the enrichment score by the number of genes TRUE or report unnormalized FALSE.
make.positive	During normalization shift enrichment values to a positive range TRUE for downstream analysis or not TRUE (default). Will only be applied if normalize = TRUE.
BPPARAM	A BiocParallel::bpparam() object that for parallelization.
...	pass arguments to AUCell GSVA, ssGSEA, or UCell call

Value

matrix of enrichment scores

Author(s)

Nick Borcherding, Jared Andrews

See Also

getGeneSets to collect gene sets.

Examples

GS <- list(Bcells = c("MS4A1", "CD79B", "CD79A", "IGH1", "IGH2"),
           Tcells = c("CD3E", "CD3D", "CD3G", "CD7","CD8A"))
pbmc_small <- SeuratObject::pbmc_small
ES <- escape.matrix(pbmc_small, 
                    gene.sets = GS, 
                    min.size = NULL)

---

Get a collection of gene sets to perform enrichment on

Description

This function allows users to select libraries and specific gene.sets to form a GeneSetCollection that is a list of gene sets.

Usage

getGeneSets(
  species = "Homo sapiens",
  library = NULL,
  subcategory = NULL,
  gene.sets = NULL
)

Arguments

species	The scientific name of the species of interest in order to get correct gene nomenclature
library	Individual collection(s) of gene sets, e.g. c("H", "C5"). See msigdbrfor all MSigDB collections.
subcategory	MSigDB sub-collection abbreviation, such as CGP or BP.
gene.sets	Select gene sets or pathways, using specific names, example: pathways = c("HALLMARK_TNFA_SIGNALING_VIA_NFKB"). Will only be honored if library is set, too.

Value

A list of gene sets from msigdbr.

Author(s)

Nick Borcherding, Jared Andrews

Examples

GS <- getGeneSets(library = "H")

---

Generate a ridge plot to examine enrichment distributions

Description

This function allows to the user to examine the distribution of enrichment across groups by generating a ridge plot.

Usage

geyserEnrichment(
  input.data,
  assay = NULL,
  group.by = NULL,
  gene.set = NULL,
  color.by = "group",
  order.by = NULL,
  scale = FALSE,
  facet.by = NULL,
  palette = "inferno"
)

Arguments

input.data	Enrichment output from escape.matrix or runEscape.
assay	Name of the assay to plot if data is a single-cell object.
group.by	Categorical parameter to plot along the x.axis. If input is a single-cell object the default will be cluster.
gene.set	Gene set to plot (on y-axis).
color.by	How the color palette applies to the graph - can be "group" for a categorical color palette based on the group.by parameter or use the gene.set name if wanting to apply a gradient palette.
order.by	Method to organize the x-axis: "mean" will arrange the x-axis by the mean of the gene.set, while "group" will arrange the x-axis by in alphanumerical order. Using NULL will not reorder the x-axis.
scale	Visualize raw values FALSE or Z-transform enrichment values TRUE.
facet.by	Variable to facet the plot into n distinct graphs.
palette	Colors to use in visualization - input any hcl.pals.

Value

ggplot2 object with geyser-based distributions of selected gene.set

Examples

GS <- list(Bcells = c("MS4A1", "CD79B", "CD79A", "IGH1", "IGH2"),
           Tcells = c("CD3E", "CD3D", "CD3G", "CD7","CD8A"))
pbmc_small <- SeuratObject::pbmc_small
pbmc_small <- runEscape(pbmc_small, 
                        gene.sets = GS, 
                        min.size = NULL)
                        
geyserEnrichment(pbmc_small, 
                 assay = "escape",
                 gene.set = "Tcells")

---

Generate a heatmap to visualize enrichment values

Description

This function allows to the user to examine the heatmap with the mean enrichment values by group. The heatmap will have the gene sets as rows and columns will be the grouping variable.

Usage

heatmapEnrichment(
  input.data,
  assay = NULL,
  group.by = NULL,
  gene.set.use = "all",
  cluster.rows = FALSE,
  cluster.columns = FALSE,
  scale = FALSE,
  facet.by = NULL,
  palette = "inferno"
)

Arguments

input.data	Enrichment output from escape.matrix or runEscape.
assay	Name of the assay to plot if data is a single-cell object.
group.by	Categorical parameter to plot along the x.axis. If input is a single-cell object the default will be cluster.
gene.set.use	Selected gene sets to visualize. If "all", the heatmap will be generated across all gene sets.
cluster.rows	Use Euclidean distance to order the row values.
cluster.columns	Use Euclidean distance to order the column values.
scale	Visualize raw values FALSE or Z-transform enrichment values TRUE.
facet.by	Variable to facet the plot into n distinct graphs.
palette	Colors to use in visualization - input any hcl.pals.

Value

ggplot2 object with heatmap of mean enrichment values

Examples

GS <- list(Bcells = c("MS4A1", "CD79B", "CD79A", "IGH1", "IGH2"),
           Tcells = c("CD3E", "CD3D", "CD3G", "CD7","CD8A"))
pbmc_small <- SeuratObject::pbmc_small
pbmc_small <- runEscape(pbmc_small, 
                        gene.sets = GS, 
                        min.size = NULL)
                        
heatmapEnrichment(pbmc_small, 
                  assay = "escape")

---

Visualize the PCA of enrichment values

Description

This function allows to the user to examine the distribution of principal components run on the enrichment values.

Usage

pcaEnrichment(
  input.data,
  dimRed = NULL,
  x.axis = "PC1",
  y.axis = "PC2",
  facet.by = NULL,
  style = "point",
  add.percent.contribution = TRUE,
  display.factors = FALSE,
  number.of.factors = 10,
  palette = "inferno"
)

Arguments

input.data	PCA from performPCA.
dimRed	Name of the dimensional reduction to plot if data is a single-cell object.
x.axis	Component to plot on the x.axis.
y.axis	Component set to plot on the y.axis.
facet.by	Variable to facet the plot into n distinct graphs.
style	Return a "hex" bin plot or a "point"-based plot.
add.percent.contribution	Add the relative percent of contribution of the selected components to the axis labels.
display.factors	Add an arrow overlay to show the direction and magnitude of individual gene sets on the PCA dimensions.
number.of.factors	The number of gene.sets to display on the overlay.
palette	Colors to use in visualization - input any hcl.pals.

Value

ggplot2 object with PCA distribution

Examples

GS <- list(Bcells = c("MS4A1", "CD79B", "CD79A", "IGH1", "IGH2"),
           Tcells = c("CD3E", "CD3D", "CD3G", "CD7","CD8A"))
pbmc_small <- SeuratObject::pbmc_small
pbmc_small <- runEscape(pbmc_small, 
                        gene.sets = GS, 
                        min.size = NULL)
                        
pbmc_small <- performPCA(pbmc_small, 
                         assay = "escape")
                         
pcaEnrichment(pbmc_small,
              x.axis = "PC1",
              y.axis = "PC2",
              dimRed = "escape.PCA")

---

Perform Normalization on Enrichment Data

Description

This function allows users to normalize the enrichment calculations by accounting for single-cell dropout and producing positive values for downstream differential enrichment analyses. A positive range values is useful for several downstream analyses, like differential evaluation for log2-fold change, but will alter the original enrichment values.

Usage

performNormalization(
  input.data,
  assay = NULL,
  gene.sets = NULL,
  make.positive = FALSE,
  scale.factor = NULL
)

Arguments

input.data	Enrichment output from escape.matrix or runEscape.
assay	Name of the assay to plot if data is a single-cell object.
gene.sets	The gene set library to use to extract the individual gene set information from.
make.positive	Shift enrichment values to a positive range TRUE for downstream analysis or not TRUE (default).
scale.factor	A vector to use for normalizing enrichment scores per cell.

Value

Single-cell object or matrix of normalized enrichment scores

Examples

GS <- list(Bcells = c("MS4A1", "CD79B", "CD79A", "IGH1", "IGH2"),
           Tcells = c("CD3E", "CD3D", "CD3G", "CD7","CD8A"))
pbmc_small <- SeuratObject::pbmc_small
pbmc_small <- runEscape(pbmc_small, 
                        gene.sets = GS, 
                        min.size = NULL)
                        
pbmc_small <- performNormalization(pbmc_small, 
                                   assay = "escape", 
                                   gene.sets = GS)

---

Perform Principal Component Analysis on Enrichment Data

Description

This function allows users to calculate the principal components for the gene set enrichment values. For single-cell data, the PCA will be stored with the dimensional reductions. If a matrix is used as input, the output is a list for further plotting. Alternatively, users can use functions for PCA calculations based on their desired workflow in lieu of using performPCA, but will not be compatible with downstream pcaEnrichment visualization.

Usage

performPCA(
  input.data,
  assay = NULL,
  scale = TRUE,
  n.dim = 1:10,
  reduction.name = "escape.PCA",
  reduction.key = "PCA"
)

Arguments

input.data	Enrichment output from escape.matrix or runEscape.
assay	Name of the assay to plot if data is a single-cell object.
scale	Standardize the enrichment value (TRUE) or not (FALSE)
n.dim	The number of components to calculate.
reduction.name	Name of the reduced dimensions object to add if data is a single-cell object.
reduction.key	Name of the key to use with the components.

Value

single-cell object or list with PCA components to plot.

Examples

GS <- list(Bcells = c("MS4A1", "CD79B", "CD79A", "IGH1", "IGH2"),
           Tcells = c("CD3E", "CD3D", "CD3G", "CD7","CD8A"))
pbmc_small <- SeuratObject::pbmc_small
pbmc_small <- runEscape(pbmc_small, 
                        gene.sets = GS, 
                        min.size = NULL)
                        
pbmc_small <- performPCA(pbmc_small, 
                         assay = "escape")

---

Visualize enrichment results with a ridge plot

Description

This function allows to the user to examine the distribution of enrichment across groups by generating a ridge plot.

Usage

ridgeEnrichment(
  input.data,
  assay = NULL,
  group.by = NULL,
  gene.set = NULL,
  color.by = "group",
  order.by = NULL,
  scale = FALSE,
  facet.by = NULL,
  add.rug = FALSE,
  palette = "inferno"
)

Arguments

input.data	Enrichment output from escape.matrix or runEscape.
assay	Name of the assay to plot if data is a single-cell object.
group.by	Categorical parameter to plot along the x.axis. If input is a single-cell object the default will be cluster.
gene.set	Gene set to plot (on y-axis).
color.by	How the color palette applies to the graph - can be "group" for a categorical color palette based on the group.by parameter or use the gene.set name if wanting to apply a gradient palette.
order.by	Method to organize the x-axis: "mean" will arrange the x-axis by the mean of the gene.set, while "group" will arrange the x-axis by in alphanumerical order. Using NULL will not reorder the x-axis.
scale	Visualize raw values FALSE or Z-transform enrichment values TRUE.
facet.by	Variable to facet the plot into n distinct graphs.
add.rug	Add visualization of the discrete cells along the ridge plot (TRUE).
palette	Colors to use in visualization - input any hcl.pals.

Value

ggplot2 object with ridge-based distributions of selected gene.set

Examples

GS <- list(Bcells = c("MS4A1", "CD79B", "CD79A", "IGH1", "IGH2"),
           Tcells = c("CD3E", "CD3D", "CD3G", "CD7","CD8A"))
pbmc_small <- SeuratObject::pbmc_small
pbmc_small <- runEscape(pbmc_small, 
                        gene.sets = GS, 
                        min.size = NULL)
                        
ridgeEnrichment(pbmc_small, 
                assay = "escape",
                gene.set = "Tcells")
                
ridgeEnrichment(pbmc_small, 
                assay = "escape",
                gene.set = "Tcells", 
                color.by = "Tcells")

---

Enrichment calculation for single-cell workflows

Description

Run the escape-based gene-set enrichment calculation with Seurat or SingleCellExperiment pipelines

Usage

runEscape(
  input.data,
  gene.sets = NULL,
  method = "ssGSEA",
  groups = 1000,
  min.size = 5,
  normalize = FALSE,
  make.positive = FALSE,
  new.assay.name = "escape",
  BPPARAM = SerialParam(),
  ...
)

Arguments

input.data	The count matrix, Seurat, or Single-Cell Experiment object.
gene.sets	Gene sets can be a list, output from getGeneSets, or the built-in gene sets in the escape package escape.gene.sets.
method	Select the method to calculate enrichment, AUCell, GSVA, ssGSEA or UCell.
groups	The number of cells to separate the enrichment calculation.
min.size	Minimum number of gene necessary to perform the enrichment calculation
normalize	Whether to divide the enrichment score by the number of genes TRUE or report unnormalized FALSE.
make.positive	During normalization shift enrichment values to a positive range TRUE for downstream analysis or not TRUE (default). Will only be applied if normalize = TRUE.
new.assay.name	The new name of the assay to append to the single-cell object containing the enrichment scores.
BPPARAM	A BiocParallel::bpparam() object that for parallelization.
...	pass arguments to AUCell GSVA, ssGSEA or UCell call

Value

Seurat or Single-Cell Experiment object with escape enrichment scores in the assay slot.

Examples

GS <- list(Bcells = c("MS4A1", "CD79B", "CD79A", "IGH1", "IGH2"),
           Tcells = c("CD3E", "CD3D", "CD3G", "CD7","CD8A"))
pbmc_small <- SeuratObject::pbmc_small
pbmc_small <- runEscape(pbmc_small, 
                        gene.sets = GS, 
                        min.size = NULL)

---

Generate a density-based scatter plot

Description

This function allows to the user to examine the distribution of 2 gene sets along the x.axis and y.axis. The color gradient is generated using the a density estimate. See ggpointdensity) for more information.

Usage

scatterEnrichment(
  input.data,
  assay = NULL,
  x.axis = NULL,
  y.axis = NULL,
  scale = FALSE,
  facet.by = NULL,
  style = "point",
  palette = "inferno"
)

Arguments

input.data	Enrichment output from escape.matrix or runEscape.
assay	Name of the assay to plot if data is a single-cell object.
x.axis	Gene set to plot on the x.axis.
y.axis	Gene set to plot on the y.axis. group.by parameter or use the gene.set name if wanting to apply a gradient palette.
scale	Visualize raw values FALSE or Z-transform enrichment values TRUE.
facet.by	Variable to facet the plot into n distinct graphs.
style	Return a "hex" bin plot or a "point"-based plot.
palette	Colors to use in visualization - input any hcl.pals.

Value

ggplot2 object with a scatter plot of selected gene.sets

Examples

GS <- list(Bcells = c("MS4A1", "CD79B", "CD79A", "IGH1", "IGH2"),
           Tcells = c("CD3E", "CD3D", "CD3G", "CD7","CD8A"))
pbmc_small <- SeuratObject::pbmc_small
pbmc_small <- runEscape(pbmc_small, 
                        gene.sets = GS, 
                        min.size = NULL)
                        
scatterEnrichment(pbmc_small, 
                  assay = "escape",
                  x.axis = "Tcells",
                  y.axis = "Bcells")

---

Visualize enrichment results with a split violin plot

Description

This function allows to the user to examine the distribution of enrichment across groups by generating a split violin plot.

Usage

splitEnrichment(
  input.data,
  assay = NULL,
  split.by = NULL,
  group.by = NULL,
  gene.set = NULL,
  order.by = NULL,
  facet.by = NULL,
  scale = TRUE,
  palette = "inferno"
)

Arguments

input.data	Enrichment output from escape.matrix or runEscape.
assay	Name of the assay to plot if data is a single-cell object.
split.by	Variable to form the split violin, must have 2 levels.
group.by	Categorical parameter to plot along the x.axis. If input is a single-cell object the default will be cluster.
gene.set	Gene set to plot (on y-axis).
order.by	Method to organize the x-axis - "mean" will arrange the x-axis by the mean of the gene.set, while "group" will arrange the x-axis by in alphanumerical order. Using NULL will not reorder the x-axis.
facet.by	Variable to facet the plot into n distinct graphs.
scale	Visualize raw values FALSE or Z-transform enrichment values TRUE.
palette	Colors to use in visualization - input any hcl.pals.

Value

ggplot2 object violin-based distributions of selected gene.set

Examples

GS <- list(Bcells = c("MS4A1", "CD79B", "CD79A", "IGH1", "IGH2"),
           Tcells = c("CD3E", "CD3D", "CD3G", "CD7","CD8A"))
pbmc_small <- SeuratObject::pbmc_small
pbmc_small <- runEscape(pbmc_small, 
                        gene.sets = GS, 
                        min.size = NULL)
                        
splitEnrichment(pbmc_small, 
                assay = "escape",
                split.by = "groups",
                gene.set = "Tcells")

---