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)
The new VolcanoPlot function provides statistical visualization of
differential expression:
VolcanoPlot(pbmc,
ident.1 = "B cell",
ident.2 = "CD8 T cell")
Added theme_umap_arrows for simplified axis indicators on dimension
reduction plots:
DimPlot2(pbmc, theme = NoAxes()) + theme_umap_arrows()
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)
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"
)
Two major color scheme changes have been implemented in v1.1.0:
- For continuous variables: Changed from viridis “A” to RColorBrewer “Blues”
- 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:
# 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)
# 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"))
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”
- VlnPlot2: Now supports both
stats.methodandstat.methodas parameter inputs (#10) - ClusterDistrBar: Added
reverse_orderparameter 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)
- VlnPlot2: Now explicitly uses
dplyr::selectinternally to avoid conflicts with other packages’ select functions (#5, #10) - ClusterDistrBar: Fixed display issues when factor levels are numeric (e.g., seurat_clusters)
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.
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()## 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 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 LC_PAPER=de_BE.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C LC_MEASUREMENT=de_BE.UTF-8 LC_IDENTIFICATION=C
##
## time zone: Europe/Brussels
## tzcode source: system (glibc)
##
## attached base packages:
## [1] grid stats4 tools stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] ggrepel_0.9.5 tidyr_1.3.1 rlist_0.4.6.2
## [4] DelayedMatrixStats_1.26.0 DelayedArray_0.30.1 SparseArray_1.4.8
## [7] S4Arrays_1.4.1 abind_1.4-5 ggpubr_0.6.0
## [10] mosaic_1.9.1 mosaicData_0.20.4 ggformula_0.12.0
## [13] Matrix_1.7-0 lattice_0.22-6 slingshot_2.12.0
## [16] TrajectoryUtils_1.12.0 SingleCellExperiment_1.26.0 SummarizedExperiment_1.34.0
## [19] Biobase_2.64.0 GenomicRanges_1.56.1 GenomeInfoDb_1.40.1
## [22] IRanges_2.38.1 S4Vectors_0.42.1 BiocGenerics_0.50.0
## [25] MatrixGenerics_1.16.0 matrixStats_1.3.0 princurve_2.1.6
## [28] rlang_1.1.4 scales_1.3.0 dplyr_1.1.4
## [31] mgcv_1.9-1 nlme_3.1-165 reshape2_1.4.4
## [34] viridis_0.6.5 viridisLite_0.4.2 cowplot_1.1.3
## [37] RColorBrewer_1.1-3 ggplot2_3.5.1 reticulate_1.38.0
## [40] glue_1.7.0 hdf5r_1.3.11 SeuratExtend_1.0.9
## [43] SeuratExtendData_0.2.1 Seurat_5.1.0 SeuratObject_5.0.2
## [46] sp_2.1-4
##
## loaded via a namespace (and not attached):
## [1] RcppAnnoy_0.0.22 splines_4.4.0 later_1.3.2 tibble_3.2.1
## [5] polyclip_1.10-6 fastDummies_1.7.3 lifecycle_1.0.4 rstatix_0.7.2
## [9] globals_0.16.3 MASS_7.3-61 backports_1.5.0 magrittr_2.0.3
## [13] plotly_4.10.4 rmarkdown_2.27 yaml_2.3.9 httpuv_1.6.15
## [17] sctransform_0.4.1 spam_2.10-0 spatstat.sparse_3.1-0 pbapply_1.7-2
## [21] pkgload_1.4.0 zlibbioc_1.50.0 Rtsne_0.17 purrr_1.0.2
## [25] rappdirs_0.3.3 labelled_2.13.0 GenomeInfoDbData_1.2.12 irlba_2.3.5.1
## [29] listenv_0.9.1 spatstat.utils_3.0-5 goftest_1.2-3 RSpectra_0.16-1
## [33] spatstat.random_3.2-3 fitdistrplus_1.2-1 parallelly_1.37.1 pkgdown_2.1.0
## [37] leiden_0.4.3.1 codetools_0.2-20 tidyselect_1.2.1 UCSC.utils_1.0.0
## [41] farver_2.1.2 spatstat.explore_3.2-7 jsonlite_1.8.8 progressr_0.14.0
## [45] ggridges_0.5.6 survival_3.7-0 ica_1.0-3 Rcpp_1.0.13
## [49] gridExtra_2.3 xfun_0.45 usethis_2.2.3 withr_3.0.0
## [53] fastmap_1.2.0 fansi_1.0.6 digest_0.6.36 R6_2.5.1
## [57] mime_0.12 colorspace_2.1-0 scattermore_1.2 tensor_1.5
## [61] spatstat.data_3.1-2 utf8_1.2.4 generics_0.1.3 data.table_1.15.4
## [65] httr_1.4.7 htmlwidgets_1.6.4 uwot_0.2.2 pkgconfig_2.0.3
## [69] gtable_0.3.5 rsconnect_1.3.1 lmtest_0.9-40 XVector_0.44.0
## [73] htmltools_0.5.8.1 carData_3.0-5 dotCall64_1.1-1 png_0.1-8
## [77] knitr_1.48 rstudioapi_0.16.0 zoo_1.8-12 stringr_1.5.1
## [81] KernSmooth_2.23-24 parallel_4.4.0 miniUI_0.1.1.1 pillar_1.9.0
## [85] vctrs_0.6.5 RANN_2.6.1 promises_1.3.0 car_3.1-2
## [89] xtable_1.8-4 cluster_2.1.6 evaluate_0.24.0 cli_3.6.3
## [93] compiler_4.4.0 crayon_1.5.3 ggsignif_0.6.4 future.apply_1.11.2
## [97] labeling_0.4.3 forcats_1.0.0 plyr_1.8.9 fs_1.6.4
## [101] stringi_1.8.4 deldir_2.0-4 munsell_0.5.1 lazyeval_0.2.2
## [105] spatstat.geom_3.2-9 mosaicCore_0.9.4.0 RcppHNSW_0.6.0 hms_1.1.3
## [109] patchwork_1.2.0 sparseMatrixStats_1.16.0 bit64_4.0.5 future_1.33.2
## [113] shiny_1.8.1.1 haven_2.5.4 highr_0.11 ROCR_1.0-11
## [117] broom_1.0.6 igraph_2.0.3 bit_4.0.5