CanvasXpress - Seurat
Isaac Neuhaus
06/04/2024
- Load data and Libraries
- Five
visualizations of marker feature expression
- Ridge plots - from ggridges. Visualize single cell expression distributions in each cluster
- Dot
plots - SplitDotPlotGG has been replaced with the
split.byparameter for DotPlot - Violin plot - Visualize single cell expression distributions in each cluster
- Violin plots can also be split on some variable. Simply add the splitting variable to object metadata and pass it to the split.by argument
- Feature plot - visualize feature expression in low-dimensional space
- DimPlot replaces TSNEPlot, PCAPlot, etc. In addition, it will plot either ‘umap’, ‘tsne’, or ‘pca’ by default, in that order
- Dot
plots - SplitDotPlotGG has been replaced with the
split.byparameter for DotPlot - Single cell heatmap of feature expression
- Senssion Info
if (!("ggplot2" %in% installed.packages())) {
install.packages('ggplot2')
}
library('ggplot2')## Warning: package 'ggplot2' was built under R version 4.4.1if (!("Seurat" %in% installed.packages())) {
install.packages('Seurat')
}
library('Seurat')## Loading required package: SeuratObject## Loading required package: sp##
## Attaching package: 'SeuratObject'## The following objects are masked from 'package:base':
##
## intersect, tif (!("devtools" %in% installed.packages())) {
install.packages('devtools')
}
if (!("canvasXpress" %in% installed.packages())) {
devtools::install_github('neuhausi/canvasXpress')
}
#devtools::install_local("~/git/canvas/R/canvasXpress.tar.gz", build_manual = TRUE, upgrade = "always")
library('canvasXpress')Load data and Libraries
https://satijalab.org/seurat/articles/visualization_vignette.html
SeuratData::InstallData("pbmc3k")## Warning: The following packages are already installed and will not be
## reinstalled: pbmc3klibrary(Seurat)
library(SeuratData)## ── Installed datasets ──────────────────────────────── SeuratData v0.2.2.9001 ──## ✔ cbmc 3.1.4 ✔ pbmc3k 3.1.4## ────────────────────────────────────── Key ─────────────────────────────────────## ✔ Dataset loaded successfully
## ❯ Dataset built with a newer version of Seurat than installed
## ❓ Unknown version of Seurat installedlibrary(ggplot2)
library(ggridges)
library(patchwork)
pbmc3k.final <- LoadData("pbmc3k", type = "pbmc3k.final")## Validating object structure## Updating object slots## Ensuring keys are in the proper structure## Updating matrix keys for DimReduc 'pca'## Updating matrix keys for DimReduc 'umap'## Warning: Assay RNA changing from Assay to Assay## Warning: Graph RNA_nn changing from Graph to Graph## Warning: Graph RNA_snn changing from Graph to Graph## Warning: DimReduc pca changing from DimReduc to DimReduc## Warning: DimReduc umap changing from DimReduc to DimReduc## Ensuring keys are in the proper structure## Ensuring feature names don't have underscores or pipes## Updating slots in RNA## Updating slots in RNA_nn## Setting default assay of RNA_nn to RNA## Updating slots in RNA_snn## Setting default assay of RNA_snn to RNA## Updating slots in pca## Updating slots in umap## Setting umap DimReduc to global## Setting assay used for NormalizeData.RNA to RNA## Setting assay used for FindVariableFeatures.RNA to RNA## Setting assay used for ScaleData.RNA to RNA## Setting assay used for RunPCA.RNA to RNA## Setting assay used for JackStraw.RNA.pca to RNA## No assay information could be found for ScoreJackStraw## Warning: Adding a command log without an assay associated with it## Setting assay used for FindNeighbors.RNA.pca to RNA## No assay information could be found for FindClusters## Warning: Adding a command log without an assay associated with it## Setting assay used for RunUMAP.RNA.pca to RNA## Validating object structure for Assay 'RNA'## Validating object structure for Graph 'RNA_nn'## Validating object structure for Graph 'RNA_snn'## Validating object structure for DimReduc 'pca'## Validating object structure for DimReduc 'umap'## Object representation is consistent with the most current Seurat version## Warning: Assay RNA changing from Assay to Assay5pbmc3k.final$groups <- sample(c("group1", "group2"), size = ncol(pbmc3k.final), replace = TRUE)
features <- c("LYZ", "CCL5", "IL32", "PTPRCAP", "FCGR3A", "PF4")
pbmc3k.final## An object of class Seurat
## 13714 features across 2638 samples within 1 assay
## Active assay: RNA (13714 features, 2000 variable features)
## 3 layers present: data, counts, scale.data
## 2 dimensional reductions calculated: pca, umapFive visualizations of marker feature expression
Ridge plots - from ggridges. Visualize single cell expression distributions in each cluster
r = RidgePlot(pbmc3k.final, features = features, ncol = 2)
r## Picking joint bandwidth of 0.318## Picking joint bandwidth of 0.177## Picking joint bandwidth of 0.161## Picking joint bandwidth of 0.15## Picking joint bandwidth of 0.0894## Picking joint bandwidth of 0.0298
canvasXpress(r)## Picking joint bandwidth of 0.318## Picking joint bandwidth of 0.177## Picking joint bandwidth of 0.161## Picking joint bandwidth of 0.15## Picking joint bandwidth of 0.0894## Picking joint bandwidth of 0.0298Dot plots - SplitDotPlotGG has been replaced with the
split.by parameter for DotPlot
d = DotPlot(pbmc3k.final, features = features, split.by = "groups") + RotatedAxis()
d
canvasXpress(d)Violin plot - Visualize single cell expression distributions in each cluster
v = VlnPlot(pbmc3k.final, features = features)
v
canvasXpress(v)Violin plots can also be split on some variable. Simply add the splitting variable to object metadata and pass it to the split.by argument
v2 = VlnPlot(pbmc3k.final, features = "percent.mt", split.by = "groups")## The default behaviour of split.by has changed.
## Separate violin plots are now plotted side-by-side.
## To restore the old behaviour of a single split violin,
## set split.plot = TRUE.
##
## This message will be shown once per session.v2
canvasXpress(v2)Feature plot - visualize feature expression in low-dimensional space
f = FeaturePlot(pbmc3k.final, features = features)
f
canvasXpress(f)DimPlot replaces TSNEPlot, PCAPlot, etc. In addition, it will plot either ‘umap’, ‘tsne’, or ‘pca’ by default, in that order
s = DimPlot(pbmc3k.final)
s
canvasXpress(s)Dot plots - SplitDotPlotGG has been replaced with the
split.by parameter for DotPlot
d = DotPlot(pbmc3k.final, features = features, split.by = "groups") + RotatedAxis()
d
canvasXpress(d)Single cell heatmap of feature expression
h = DoHeatmap(subset(pbmc3k.final, downsample = 100), features = features, size = 3)
h
canvasXpress(h)Senssion Info
sessionInfo()## R version 4.4.0 (2024-04-24)
## Platform: aarch64-apple-darwin20
## Running under: macOS 15.5
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRlapack.dylib; LAPACK version 3.12.0
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## time zone: America/New_York
## tzcode source: internal
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] patchwork_1.2.0 ggridges_0.5.6 pbmc3k.SeuratData_3.1.4
## [4] cbmc.SeuratData_3.1.4 SeuratData_0.2.2.9001 canvasXpress_1.50.5
## [7] Seurat_5.1.0 SeuratObject_5.0.2 sp_2.1-4
## [10] ggplot2_3.5.2
##
## loaded via a namespace (and not attached):
## [1] deldir_2.0-4 pbapply_1.7-2 gridExtra_2.3
## [4] rlang_1.1.6 magrittr_2.0.3 RcppAnnoy_0.0.22
## [7] spatstat.geom_3.2-9 matrixStats_1.3.0 compiler_4.4.0
## [10] png_0.1-8 vctrs_0.6.5 reshape2_1.4.4
## [13] stringr_1.5.1 crayon_1.5.3 pkgconfig_2.0.3
## [16] fastmap_1.2.0 labeling_0.4.3 promises_1.3.3
## [19] rmarkdown_2.29 purrr_1.0.4 xfun_0.52
## [22] cachem_1.1.0 jsonlite_2.0.0 goftest_1.2-3
## [25] later_1.4.2 spatstat.utils_3.0-4 irlba_2.3.5.1
## [28] parallel_4.4.0 cluster_2.1.6 R6_2.6.1
## [31] ica_1.0-3 bslib_0.9.0 stringi_1.8.7
## [34] RColorBrewer_1.1-3 spatstat.data_3.0-4 reticulate_1.36.1
## [37] parallelly_1.37.1 lmtest_0.9-40 jquerylib_0.1.4
## [40] scattermore_1.2 Rcpp_1.0.14 knitr_1.50
## [43] tensor_1.5 future.apply_1.11.2 zoo_1.8-12
## [46] sctransform_0.4.1 httpuv_1.6.15 Matrix_1.7-0
## [49] splines_4.4.0 igraph_2.0.3 tidyselect_1.2.1
## [52] abind_1.4-5 rstudioapi_0.16.0 yaml_2.3.10
## [55] spatstat.random_3.2-3 codetools_0.2-20 miniUI_0.1.1.1
## [58] spatstat.explore_3.2-7 listenv_0.9.1 lattice_0.22-6
## [61] tibble_3.3.0 plyr_1.8.9 shiny_1.8.1.1
## [64] withr_3.0.2 ROCR_1.0-11 evaluate_1.0.4
## [67] Rtsne_0.17 future_1.33.2 fastDummies_1.7.3
## [70] survival_3.6-4 polyclip_1.10-6 fitdistrplus_1.1-11
## [73] pillar_1.10.2 KernSmooth_2.23-24 plotly_4.10.4
## [76] generics_0.1.4 RcppHNSW_0.6.0 scales_1.4.0
## [79] globals_0.16.3 xtable_1.8-4 glue_1.8.0
## [82] lazyeval_0.2.2 tools_4.4.0 data.table_1.17.4
## [85] RSpectra_0.16-1 RANN_2.6.1 leiden_0.4.3.1
## [88] dotCall64_1.1-1 cowplot_1.1.3 grid_4.4.0
## [91] tidyr_1.3.1 colorspace_2.1-1 nlme_3.1-164
## [94] cli_3.6.5 rappdirs_0.3.3 spatstat.sparse_3.0-3
## [97] spam_2.10-0 viridisLite_0.4.2 dplyr_1.1.4
## [100] uwot_0.2.2 gtable_0.3.6 sass_0.4.10
## [103] digest_0.6.37 progressr_0.14.0 ggrepel_0.9.5
## [106] htmlwidgets_1.6.4 farver_2.1.2 htmltools_0.5.8.1
## [109] lifecycle_1.0.4 httr_1.4.7 mime_0.13
## [112] MASS_7.3-60.2