scplotter to work with Nanostring CosMx data prepared by Seurat¶
Go back to scplotter documentation: https://pwwang.github.io/scplotter/
In [1]:
options(future.globals.maxSize = 512 * 1024^3) # 512 GB
suppressPackageStartupMessages({
library(Seurat)
})
# Load the scplotter package
# library(scplotter)
devtools::load_all()
# devtools::load_all("../../../plotthis")
# see also the vignette for scplotter to work with CosMx data prepared by Giotto
data.dir <- "data/CosMx_Quarter_Brain/Quarter_Brain"
obj <- LoadNanostring(data.dir = data.dir, fov = 'fov')
obj
ℹ Loading scplotter
An object of class Seurat 960 features across 38987 samples within 1 assay Active assay: Nanostring (960 features, 0 variable features) 1 layer present: counts 1 spatial field of view present: fov
In [13]:
options(repr.plot.width = 8, repr.plot.height = 4.5)
SpatDimPlot(obj, points_size = 0.5)
ℹ Loading scplotter
In [14]:
# add fake annotations
obj$celltype <- sample(LETTERS[1:10], size = ncol(obj), replace = TRUE)
obj$celltype <- factor(obj$celltype, levels = LETTERS[1:10])
Idents(obj) <- "celltype"
options(repr.plot.width = 8, repr.plot.height = 4.5)
SpatDimPlot(obj, image = "black", group_by = "celltype", points_size = 0.5,
theme = "theme_this")
In [15]:
options(repr.plot.width = 8, repr.plot.height = 4.5)
subobj <- subset(obj, celltype %in% c("A", "B", "C"))
SpatFeaturePlot(subobj, image = "black", group_by = "celltype", points_size = 0.5)
Warning message: “Not validating FOV objects” Warning message: “Not validating Centroids objects” Warning message: “Not validating Centroids objects” Warning message: “Not validating FOV objects” Warning message: “Not validating Centroids objects” Warning message: “Not validating FOV objects” Warning message: “Not validating FOV objects” Warning message: “Not validating FOV objects” Warning message: “Not validating Seurat objects” Warning message: “Not validating FOV objects” Warning message: “Not validating Centroids objects” Warning message: “Not validating FOV objects” Warning message: “Not validating Centroids objects” Warning message: “Not validating Centroids objects” Warning message: “Not validating FOV objects” Warning message: “Not validating FOV objects” Warning message: “Not validating FOV objects” Warning message: “Adding image with unordered cells” Warning message: “Not validating Seurat objects”
In [16]:
options(repr.plot.width = 8, repr.plot.height = 9)
p1 <- SpatFeaturePlot(obj, image = "black", features = "Chrna4", layer = "counts", points_size = 0.5)
p2 <- SpatDimPlot(obj, image = "black", features = c('Chrna4', 'Slc6a1', 'Cd109', 'Ldha', 'Aldoc'),
points_size = 0.25, nmols = 10000)
p1 / p2
In [17]:
options(repr.plot.width = 12, repr.plot.height = 5)
# Note that Crop() swapped x and y
crop <- Crop(obj[["fov"]], y = c(130000, 135000), x = c(0, 5000))
obj[["zoom1"]] <- crop
DefaultBoundary(obj[["zoom1"]]) <- "segmentation"
# devtools::load_all()
# Use cropped fov
p1 <- SpatDimPlot(obj, fov = "zoom1", points = FALSE, shapes_fill_by = "celltype",
shapes_border_size = 0.1, theme = "theme_this")
# Or just pass the desired extent
# Note that the extent is in the right x and y order
p2 <- SpatDimPlot(obj, fov = "fov", points = FALSE, shapes_fill_by = "celltype",
shapes = "segmentation", # pass the boundary name, since centroids are the default
shapes_border_size = 0.1, ext = c(130000, 135000, 0, 5000), theme = "theme_this")
p1 + p2
Warning message: “Key ‘Nanostring_’ taken, using ‘zoom1_’ instead” Warning message: “Adding image with unordered cells”
In [18]:
options(repr.plot.width = 6, repr.plot.height = 5)
SpatDimPlot(
obj, fov = "zoom1", image = "black",
features = c('Chrna4', 'Slc6a1', 'Cd109', 'Ldha', 'Aldoc'),
points_size = 0.25, nmols = 10000
)
In [19]:
x <- sessionInfo()
x <- capture.output(print(x))
# hide the BLAS/LAPACK paths
x <- x[!startsWith(x, "BLAS/LAPACK:")]
cat(paste(x, collapse = "\n"))
R version 4.3.3 (2024-02-29) Platform: x86_64-conda-linux-gnu (64-bit) Running under: Red Hat Enterprise Linux 8.10 (Ootpa) Matrix products: default locale: [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C [3] LC_TIME=en_US.UTF-8 LC_COLLATE=C [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8 [7] LC_PAPER=en_US.UTF-8 LC_NAME=C [9] LC_ADDRESS=C LC_TELEPHONE=C [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C time zone: America/Chicago tzcode source: system (glibc) attached base packages: [1] stats graphics grDevices utils datasets methods base other attached packages: [1] scplotter_0.4.0 Seurat_5.3.0 SeuratObject_5.0.2 sp_2.1-4 loaded via a namespace (and not attached): [1] fs_1.6.4 matrixStats_1.1.0 [3] spatstat.sparse_3.1-0 bitops_1.0-7 [5] sf_1.0-20 devtools_2.4.5 [7] httr_1.4.7 RColorBrewer_1.1-3 [9] repr_1.1.7 profvis_0.3.8 [11] tools_4.3.3 sctransform_0.4.1 [13] utf8_1.2.4 R6_2.5.1 [15] lazyeval_0.2.2 uwot_0.1.16 [17] urlchecker_1.0.1 withr_3.0.1 [19] gridExtra_2.3 progressr_0.14.0 [21] quantreg_5.98 cli_3.6.3 [23] Biobase_2.62.0 Cairo_1.6-2 [25] spatstat.explore_3.2-6 fastDummies_1.7.3 [27] iNEXT_3.0.1 labeling_0.4.3 [29] spatstat.data_3.1-2 proxy_0.4-27 [31] ggridges_0.5.6 pbapply_1.7-2 [33] pbdZMQ_0.3-11 stringdist_0.9.12 [35] parallelly_1.38.0 sessioninfo_1.2.2 [37] VGAM_1.1-12 rstudioapi_0.16.0 [39] generics_0.1.3 shape_1.4.6.1 [41] ica_1.0-3 spatstat.random_3.2-3 [43] dplyr_1.1.4 Matrix_1.6-5 [45] fansi_1.0.6 S4Vectors_0.40.2 [47] abind_1.4-5 terra_1.8-42 [49] lifecycle_1.0.4 SummarizedExperiment_1.32.0 [51] SparseArray_1.2.2 Rtsne_0.17 [53] grid_4.3.3 promises_1.3.0 [55] crayon_1.5.3 miniUI_0.1.1.1 [57] lattice_0.22-6 cowplot_1.1.3 [59] pillar_1.9.0 GenomicRanges_1.54.1 [61] rjson_0.2.21 future.apply_1.11.2 [63] codetools_0.2-20 glue_1.8.0 [65] data.table_1.15.4 remotes_2.5.0 [67] vctrs_0.6.5 png_0.1-8 [69] spam_2.11-0 testthat_3.2.1.1 [71] gtable_0.3.5 assertthat_0.2.1 [73] cachem_1.1.0 S4Arrays_1.2.0 [75] mime_0.12 tidygraph_1.3.0 [77] survival_3.7-0 SingleCellExperiment_1.24.0 [79] units_0.8-5 ellipsis_0.3.2 [81] fitdistrplus_1.1-11 scRepertoire_2.2.1 [83] ROCR_1.0-11 nlme_3.1-165 [85] usethis_2.2.3 RcppAnnoy_0.0.22 [87] evd_2.3-7.1 GenomeInfoDb_1.38.1 [89] rprojroot_2.0.4 irlba_2.3.5.1 [91] KernSmooth_2.23-24 plotthis_0.7.1 [93] colorspace_2.1-1 BiocGenerics_0.48.1 [95] DBI_1.2.3 tidyselect_1.2.1 [97] compiler_4.3.3 SparseM_1.84 [99] xml2_1.3.6 desc_1.4.3 [101] ggdendro_0.2.0 DelayedArray_0.28.0 [103] plotly_4.10.4 scales_1.3.0 [105] classInt_0.4-10 lmtest_0.9-40 [107] stringr_1.5.1 digest_0.6.37 [109] goftest_1.2-3 spatstat.utils_3.1-1 [111] XVector_0.42.0 htmltools_0.5.8.1 [113] pkgconfig_2.0.3 base64enc_0.1-3 [115] MatrixGenerics_1.14.0 fastmap_1.2.0 [117] rlang_1.1.4 GlobalOptions_0.1.2 [119] htmlwidgets_1.6.4 shiny_1.8.1.1 [121] farver_2.1.2 zoo_1.8-12 [123] jsonlite_1.8.8 RCurl_1.98-1.13 [125] magrittr_2.0.3 GenomeInfoDbData_1.2.11 [127] dotCall64_1.2 patchwork_1.3.0 [129] IRkernel_1.3.2 munsell_0.5.1 [131] Rcpp_1.0.13 evmix_2.12 [133] ggnewscale_0.5.0 viridis_0.6.5 [135] reticulate_1.38.0 truncdist_1.0-2 [137] stringi_1.8.7 ggalluvial_0.12.5 [139] ggraph_2.2.1 brio_1.1.5 [141] zlibbioc_1.48.0 MASS_7.3-60.0.1 [143] plyr_1.8.9 pkgbuild_1.4.4 [145] parallel_4.3.3 listenv_0.9.1 [147] ggrepel_0.9.6 forcats_1.0.0 [149] deldir_2.0-4 graphlayouts_1.1.0 [151] IRdisplay_1.1 splines_4.3.3 [153] gridtext_0.1.5 tensor_1.5 [155] circlize_0.4.16 igraph_1.5.1 [157] uuid_1.2-0 spatstat.geom_3.2-9 [159] cubature_2.1.1 RcppHNSW_0.6.0 [161] reshape2_1.4.4 stats4_4.3.3 [163] pkgload_1.3.4 evaluate_0.24.0 [165] tweenr_2.0.3 httpuv_1.6.15 [167] MatrixModels_0.5-3 RANN_2.6.1 [169] tidyr_1.3.1 purrr_1.0.2 [171] polyclip_1.10-6 future_1.34.0 [173] scattermore_1.2 ggplot2_3.5.1 [175] ggforce_0.4.2 xtable_1.8-4 [177] e1071_1.7-14 RSpectra_0.16-1 [179] later_1.3.2 viridisLite_0.4.2 [181] class_7.3-22 gsl_2.1-8 [183] tibble_3.2.1 memoise_2.0.1 [185] IRanges_2.36.0 cluster_2.1.6 [187] globals_0.16.3