In huayc09/SeuratExtend: SeuratExtend

New Features and Enhancements in v1.1.0

New Visualization Functions

Enhanced Dot Plots with DotPlot2

A new function DotPlot2 has been introduced, combining dot size (percent of expressing cells) with color intensity (average expression) for more informative visualizations:

library(Seurat)
library(SeuratExtend)

# With grouped features
grouped_features <- list(
  "B_cell_markers" = c("MS4A1", "CD79A"),
  "T_cell_markers" = c("CD3D", "CD8A", "IL7R"),
  "Myeloid_markers" = c("CD14", "FCGR3A", "S100A8")
)
DotPlot2(pbmc, features = grouped_features)

New Volcano Plots

The new VolcanoPlot function provides statistical visualization of differential expression:

VolcanoPlot(pbmc, 
            ident.1 = "B cell",
            ident.2 = "CD8 T cell")

UMAP Arrow Annotations

Added theme_umap_arrows for simplified axis indicators on dimension reduction plots:

DimPlot2(pbmc, theme = NoAxes()) + theme_umap_arrows()

Enhanced Existing Functions

VlnPlot2 Enhancements

Added support for mean and median lines in violin plots:

VlnPlot2(pbmc, 
         features = c("CCR7", "IL7R", "TCF7"),
         cols = "light",
         show.mean = TRUE,      # Show mean and median lines
         mean_colors = c("red", "blue"),  # Colors for mean and median
         ncol = 1)

New WaterfallPlot Style: "segment"

Added a new visualization style "segment" to WaterfallPlot, providing an alternative way to display differences between conditions:

# Prepare data
pbmc <- GeneSetAnalysis(pbmc, genesets = hall50$human)
matr <- pbmc@misc$AUCell$genesets

# Create a plot using the new segment style
WaterfallPlot(
  matr,
  f = pbmc$cluster,
  ident.1 = "Mono CD14",
  ident.2 = "CD8 T cell",
  style = "segment",
  color_theme = "D"
)

Color Scheme Updates

New Default Color Schemes

Two major color scheme changes have been implemented in v1.1.0:

1. For continuous variables: Changed from viridis "A" to RColorBrewer "Blues"
2. For heatmaps: Updated from c(low = muted("blue"), mid = "white", high = muted("red")) to "BuRd"

Here are visual comparisons of the old and new defaults:

Continuous Variable Color Scheme

# Create a side-by-side comparison for continuous variables
library(cowplot)
library(ggpubr)

# Old default (viridis "A")
p1 <- DimPlot2(pbmc, 
               features = "CD3D", 
               cols = "A",  # Old default
               theme = theme_umap_arrows()) 

# New default (Blues)
p2 <- DimPlot2(pbmc, 
               features = "CD3D", 
               theme = theme_umap_arrows()) 

plot_grid(
  annotate_figure(p1, top = text_grob("Old Default (viridis 'A')", size = 14)), 
  annotate_figure(p2, top = text_grob("New Default ('Blues')", size = 14)),
  ncol = 2)

Heatmap Color Scheme

# Calculate data for heatmap
genes <- VariableFeatures(pbmc)
toplot <- CalcStats(pbmc, features = genes, method = "zscore", order = "p", n = 4)

# Create side-by-side heatmap comparison
p1 <- Heatmap(toplot, 
              color_scheme = c(low = scales::muted("blue"), 
                             mid = "white", 
                             high = scales::muted("red")), # Old default
              lab_fill = "zscore") +
      ggtitle("Old Default (blue-white-red)")

p2 <- Heatmap(toplot, 
              lab_fill = "zscore") +  # New default (BuRd) is automatically applied
      ggtitle("New Default ('BuRd')")

plot_grid(p1, p2, ncol = 2)

To revert to previous color schemes: - For continuous variables: Use cols = "A" - For heatmaps: Use color_scheme = c(low = scales::muted("blue"), mid = "white", high = scales::muted("red"))

New RColorBrewer Integration

Added support for RColorBrewer sequential and diverging palettes:

• Sequential: "Blues", "BuGn", "BuPu", "GnBu", etc.
• Diverging: "BrBG", "PiYG", "PRGn", "PuOr", etc.
• Custom diverging: "GnYlRd", "BuYlRd", "GyRd", "BuRd", "PuOr"

Feature Enhancements

• VlnPlot2: Now supports both stats.method and stat.method as parameter inputs (#10)
• ClusterDistrBar: Added reverse_order parameter to adjust the stacking order (#11)
• WaterfallPlot: Set upper limit for -log10(p) values to avoid NA issues (#14)
• DimPlot2: Improved automatic point size adjustment and fixed point display issues in raster mode (#17)
• show_col2: Function is now exported, allowing users to knit Visualization.Rmd without issues (#8)

Bug Fixes

• VlnPlot2: Now explicitly uses dplyr::select internally to avoid conflicts with other packages' select functions (#5, #10)
• ClusterDistrBar: Fixed display issues when factor levels are numeric (e.g., seurat_clusters)

Documentation Updates

Conda Environment Setup

The create_condaenv_seuratextend() function documentation has been updated with important compatibility information:

• Currently supported and tested on:
• Windows
• Intel-based macOS (not Apple Silicon/M1/M2)
• Linux (Ubuntu 20.04)

Note for Apple Silicon Users: The function is not currently compatible with Apple Silicon/M1/M2 devices (#7). Users with Apple Silicon devices who are interested in contributing to the development of M1/M2 support are welcome to reach out via GitHub Issues.

Windows-Specific File Download

When downloading loom files (which are HDF5-based binary files) on Windows, it's essential to use mode = "wb" in the download.file() function:

# Example for Windows users
download.file("https://example.com/file.loom", "file.loom", mode = "wb")

This prevents Windows from modifying line endings in the binary file, which would corrupt the HDF5 format. Mac and Linux users don't require this parameter, but including it is harmless.

sessionInfo()

huayc09/SeuratExtend documentation built on March 29, 2025, 7:28 p.m.