VlnPlot2: Enhanced Violin Plot

In huayc09/SeuratExtend: SeuratExtend

Enhanced Violin Plot

Description

Generates advanced violin plots distinct from Seurat's VlnPlot. This improved version offers a more compact design for efficient space utilization, the ability to overlay a boxplot, and convenient inclusion of statistical annotations. The function accommodates input in the form of either a Seurat object or a data frame.

Usage

VlnPlot2(object, ...)

## S3 method for class 'Seurat'
VlnPlot2(
  seu,
  features,
  group.by = NULL,
  split.by = NULL,
  cells = NULL,
  slot = "data",
  assay = NULL,
  priority = c("expr", "none"),
  cols = "auto",
  load.cols = TRUE,
  ncol = NULL,
  lab_fill = "group",
  scales = "free_y",
  violin = T,
  box = T,
  width = 0.9,
  pt = T,
  hide.outlier = F,
  pt.style = c("jitter", "quasirandom"),
  pt.size = 0.2,
  pt.alpha = 1,
  strip.position = "top",
  stat.method = c("none", "wilcox.test", "t.test"),
  p.adjust.method = "holm",
  label = c("p.signif", "p", "p.adj", "p.format"),
  comparisons = NULL,
  hide.ns = TRUE,
  step.increase = 0.12,
  tip.length = 0.03,
  ...
)

## Default S3 method:
VlnPlot2(
  matr,
  f,
  f2 = NULL,
  features = NULL,
  t = F,
  cols = "pro_default",
  ncol = NULL,
  lab_fill = "group",
  scales = "free_y",
  violin = T,
  box = T,
  width = 0.9,
  pt = T,
  hide.outlier = F,
  pt.style = c("jitter", "quasirandom"),
  pt.size = 0.2,
  pt.alpha = 1,
  strip.position = "top",
  stat.method = c("none", "wilcox.test", "t.test"),
  p.adjust.method = "holm",
  label = c("p.signif", "p", "p.adj", "p.format"),
  comparisons = NULL,
  hide.ns = TRUE,
  step.increase = 0.12,
  tip.length = 0.03,
  ...
)

Arguments

object	An object, either a Seurat object or matrix.
...	Further arguments passed to the ggpubr::stat_pvalue_manual().
seu	A Seurat object. Only applicable for the Seurat method.
features	Features to depict, such as gene expression, metrics, PC scores, or any data obtainable via 'FetchData()'. Default: NULL (all features in matrix).
group.by	A variable from 'meta.data' for grouping or a character vector of equal length as the number of cells. Only applicable for the Seurat method.
split.by	A variable from 'meta.data' to bifurcate the violin plots. Only applicable for the Seurat method.
cells	Cell identifiers for use. Defaults to all cells. Only applicable for the Seurat method.
slot	Slot to retrieve feature data from. Only applicable for the Seurat method.
assay	Name of the assay to employ. Defaults to the active assay. Only applicable for the Seurat method.
priority	If set to "expr", extracts data from the expression matrix over 'meta.data'. Only applicable for the Seurat method.
cols	Flexible color settings for the plot, accepting a variety of inputs: - Seven color_pro styles: "default", "light", "pro_red", "pro_yellow", "pro_green", "pro_blue", "pro_purple". - Five color_iwh styles: "iwh_default", "iwh_intense", "iwh_pastel", "iwh_all", "iwh_all_hard". - Brewer color scales as specified by 'brewer.pal.info'. - Any manually specified colors.
load.cols	When TRUE, automatically loads pre-stored color information for variables from 'seu@misc[["var_colors"]]'.
ncol	Specifies the number of columns for display if multiple plots are shown. Default: NULL.
lab_fill	Label for the figure legend. Default: 'group'.
scales	Scales parameter passed to ggplot2::facet_wrap(). Default: 'free_y'.
violin	Indicates whether to generate a violin plot. Default: TRUE.
box	Indicates whether to depict a box plot. Default: TRUE.
width	Width of the box plot. Default: 0.9.
pt	Indicates if points should be plotted. Default: TRUE.
hide.outlier	Conceals outlier points from the box plot. Default: FALSE.
pt.style	Position adjustment. Default choices: "jitter", "quasirandom".
pt.size	Point size setting. Default: 0.2.
pt.alpha	Adjusts the transparency of points. Default: 1.
strip.position	Positions the strip ("top" (default), "bottom", "left", or "right"). Only used when 'f2 = NULL'.
stat.method	Determines if pairwise statistics are added to the plot. Either "wilcox.test" or "t.test". Default: "none".
p.adjust.method	Method for adjusting p-values, especially when conducting multiple pairwise tests or dealing with multiple grouping variables. Options include "holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr", and "none". Note: Adjustments are independently conducted for each variable in formulas containing multiple variables. Default: 'holm'.
label	Specifies label type. Options include "p.signif" (showing significance levels), "p.format" (formatted p value), or "p", "p.adj". Default: "p.signif".
comparisons	List of length-2 vectors, each containing either names of two x-axis values or two integers pointing to groups of interest for comparison. Default: all groups.
hide.ns	If TRUE, the 'ns' symbol is concealed when displaying significance levels. Default: TRUE.
step.increase	Numeric vector denoting the increase in fraction of total height for each additional comparison, minimizing overlap. Default: 0.12.
tip.length	Numeric vector indicating the fraction of total height the bar descends to specify the exact column. For a line display instead of a bracket, set to 0. Default: 0.03.
matr	A matrix or data frame with rows as features and columns as cells.
f	A factor or vector indicating the identity of each cell. Should match the column length of 'matr'.
f2	A factor or vector akin to 'f' for splitting the violin plots. Default: NULL.
t	If the matrix has features in columns and cells in rows, transpose the matrix first. Default: FALSE.

Details

This function provides a range of customization options to generate violin plots, with or without additional graphical elements such as boxplots and points. You can specify features to plot, control the appearance of violin plots, boxplots, and points, adjust point position, group data, split violin plots, selectively use cells, control the column layout of multiple plots, and add statistical annotations. For a detailed overview, refer to the provided examples.

Value

ggplot object

A ggplot object.

Examples

library(Seurat)
library(SeuratExtend)

# Using Seurat object as input:

# Basic violin plot with box plot and points:
genes <- c("CD3D","CD14","CD79A")
VlnPlot2(pbmc, features = genes, ncol = 1)

# Without violin plot, only box plot, and use quasirandom style for point position adjustment:
VlnPlot2(pbmc, features = genes, violin = F, pt.style = "quasirandom", ncol = 1)

# Hide points but display outlier points of the box plot:
VlnPlot2(pbmc, features = genes, pt = FALSE, ncol = 1)

# When hiding points, outliers are shown by default (recommended). However, outliers can be hidden for a cleaner appearance:
VlnPlot2(pbmc, features = genes, pt = FALSE, hide.outlier = T, ncol = 1)

# Group by cluster and split each cluster by samples:
VlnPlot2(pbmc, features = genes, group.by = "cluster", split.by = "orig.ident")

# Display only cells from certain subtypes (e.g. B cell, CD14+ Mono, and CD8 T cell), and arrange plots in 3 columns:
cells <- colnames(pbmc)[pbmc$cluster %in% c("B cell", "Mono CD14", "CD8 T cell")]
VlnPlot2(pbmc, features = genes, group.by = "cluster", cells = cells)

# Add statistical annotations using the Wilcoxon test for pairwise comparisons and hide non-significant results:
VlnPlot2(pbmc, features = genes, group.by = "cluster", cell = cells,
         stat.method = "wilcox.test", hide.ns = TRUE)

# Display statistics comparing only the specified clusters using a t-test:
VlnPlot2(pbmc, features = genes, group.by = "cluster", cell = cells,
         stat.method = "t.test", comparisons = list(c(1,2), c(1,3)), hide.ns = FALSE)

# Using a matrix as input:
# For instance, after performing Geneset Enrichment Analysis (GSEA) using the Hallmark 50 geneset to obtain the AUCell matrix:
pbmc <- GeneSetAnalysis(pbmc, genesets = hall50$human)
matr <- pbmc@misc$AUCell$genesets

# Create violin plots for the first three pathways:
VlnPlot2(matr[1:3,], f = pbmc$cluster, ncol = 1)

huayc09/SeuratExtend documentation built on July 15, 2024, 6:22 p.m.