What’s New in v1.1.0

Yichao Hua

2024-11-28

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.

## R version 4.4.0 (2024-04-24)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 20.04.6 LTS
## 
## Matrix products: default
## BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.9.0 
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.9.0
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=de_BE.UTF-8        LC_COLLATE=en_US.UTF-8    
##  [5] LC_MONETARY=de_BE.UTF-8    LC_MESSAGES=en_US.UTF-8   
##  [7] LC_PAPER=de_BE.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=de_BE.UTF-8 LC_IDENTIFICATION=C       
## 
## time zone: Europe/Brussels
## tzcode source: system (glibc)
## 
## attached base packages:
## [1] stats4    grid      stats     graphics  grDevices utils     datasets 
## [8] methods   base     
## 
## other attached packages:
##  [1] rlang_1.1.4               ggpubr_0.6.0             
##  [3] scales_1.3.0              DelayedMatrixStats_1.26.0
##  [5] DelayedArray_0.30.1       SparseArray_1.4.8        
##  [7] S4Arrays_1.4.1            abind_1.4-5              
##  [9] IRanges_2.38.1            S4Vectors_0.42.1         
## [11] MatrixGenerics_1.16.0     matrixStats_1.3.0        
## [13] BiocGenerics_0.50.0       cowplot_1.1.3            
## [15] RColorBrewer_1.1-3        ggrepel_0.9.5            
## [17] tidyr_1.3.1               viridis_0.6.5            
## [19] viridisLite_0.4.2         mosaic_1.9.1             
## [21] mosaicData_0.20.4         ggformula_0.12.0         
## [23] Matrix_1.7-0              lattice_0.22-6           
## [25] rlist_0.4.6.2             dplyr_1.1.4              
## [27] reshape2_1.4.4            ggplot2_3.5.1            
## [29] SeuratExtend_1.1.0        SeuratExtendData_0.2.1   
## [31] Seurat_5.1.0              SeuratObject_5.0.2       
## [33] sp_2.1-4                 
## 
## loaded via a namespace (and not attached):
##   [1] RcppAnnoy_0.0.22         splines_4.4.0            later_1.3.2             
##   [4] tibble_3.2.1             polyclip_1.10-6          fastDummies_1.7.3       
##   [7] lifecycle_1.0.4          rstatix_0.7.2            globals_0.16.3          
##  [10] MASS_7.3-61              backports_1.5.0          magrittr_2.0.3          
##  [13] plotly_4.10.4            sass_0.4.9               rmarkdown_2.29          
##  [16] jquerylib_0.1.4          yaml_2.3.9               httpuv_1.6.15           
##  [19] sctransform_0.4.1        spam_2.10-0              spatstat.sparse_3.1-0   
##  [22] reticulate_1.38.0        pbapply_1.7-2            zlibbioc_1.50.0         
##  [25] Rtsne_0.17               purrr_1.0.2              labelled_2.13.0         
##  [28] irlba_2.3.5.1            listenv_0.9.1            spatstat.utils_3.0-5    
##  [31] goftest_1.2-3            RSpectra_0.16-1          spatstat.random_3.2-3   
##  [34] fitdistrplus_1.2-1       parallelly_1.37.1        pkgdown_2.0.7           
##  [37] leiden_0.4.3.1           codetools_0.2-20         tidyselect_1.2.1        
##  [40] farver_2.1.2             spatstat.explore_3.2-7   jsonlite_1.8.8          
##  [43] progressr_0.14.0         ggridges_0.5.6           survival_3.7-0          
##  [46] systemfonts_1.1.0        tools_4.4.0              ragg_1.3.2              
##  [49] ica_1.0-3                Rcpp_1.0.13              glue_1.7.0              
##  [52] gridExtra_2.3            xfun_0.45                withr_3.0.0             
##  [55] fastmap_1.2.0            fansi_1.0.6              digest_0.6.36           
##  [58] R6_2.5.1                 mime_0.12                textshaping_0.4.0       
##  [61] colorspace_2.1-0         scattermore_1.2          tensor_1.5              
##  [64] spatstat.data_3.1-2      utf8_1.2.4               generics_0.1.3          
##  [67] data.table_1.15.4        httr_1.4.7               htmlwidgets_1.6.4       
##  [70] uwot_0.2.2               pkgconfig_2.0.3          gtable_0.3.5            
##  [73] lmtest_0.9-40            XVector_0.44.0           htmltools_0.5.8.1       
##  [76] carData_3.0-5            dotCall64_1.1-1          png_0.1-8               
##  [79] knitr_1.48               rstudioapi_0.16.0        nlme_3.1-165            
##  [82] cachem_1.1.0             zoo_1.8-12               stringr_1.5.1           
##  [85] KernSmooth_2.23-24       parallel_4.4.0           miniUI_0.1.1.1          
##  [88] desc_1.4.3               pillar_1.9.0             vctrs_0.6.5             
##  [91] RANN_2.6.1               promises_1.3.0           car_3.1-2               
##  [94] xtable_1.8-4             cluster_2.1.6            evaluate_0.24.0         
##  [97] cli_3.6.3                compiler_4.4.0           crayon_1.5.3            
## [100] future.apply_1.11.2      ggsignif_0.6.4           labeling_0.4.3          
## [103] plyr_1.8.9               forcats_1.0.0            fs_1.6.4                
## [106] stringi_1.8.4            deldir_2.0-4             munsell_0.5.1           
## [109] lazyeval_0.2.2           spatstat.geom_3.2-9      mosaicCore_0.9.4.0      
## [112] RcppHNSW_0.6.0           hms_1.1.3                patchwork_1.2.0         
## [115] sparseMatrixStats_1.16.0 future_1.33.2            shiny_1.8.1.1           
## [118] highr_0.11               haven_2.5.4              ROCR_1.0-11             
## [121] igraph_2.0.3             broom_1.0.6              memoise_2.0.1           
## [124] bslib_0.4.2