scplotter to work with CODEX data prepared by Giotto¶
See: https://drieslab.github.io/Giotto_website/articles/codex_mouse_spleen.html
Go back to scplotter documentation: https://pwwang.github.io/scplotter/
In [1]:
# GiottoData::getSpatialDataset(
# dataset = "codex_spleen",
# directory = "data/codex_spleen",
# verbose = TRUE,
# dryrun = FALSE
# )
In [2]:
library(Giotto)
# Ensure Giotto can access a python env
genv_exists <- suppressMessages(checkGiottoEnvironment())
print(genv_exists)
python_path <- file.path(Sys.getenv("HOME"), "miniconda3", "envs", "giotto_env", "bin", "python")
Sys.setenv(RETICULATE_PYTHON = python_path)
invisible(capture.output(suppressMessages(set_giotto_python_path(python_path = python_path))))
# library(scplotter)
devtools::load_all()
Loading required package: GiottoClass
Newer devel version of GiottoClass on GitHub: 0.4.8 Giotto Suite 4.2.1
[1] TRUE
ℹ Loading scplotter
In [3]:
library(Giotto)
## Set instructions
results_folder <- "data/codex_spleen.results"
instructions <- createGiottoInstructions(
save_dir = results_folder,
save_plot = FALSE,
show_plot = TRUE,
return_plot = TRUE,
python_path = python_path
)
data_path <- "data/codex_spleen/"
# 2. create giotto object from provided paths ####
expr_path <- paste0(data_path, "codex_BALBc_3_expression.txt.gz")
loc_path <- paste0(data_path, "codex_BALBc_3_coord.txt")
meta_path <- paste0(data_path, "codex_BALBc_3_annotation.txt")
# read in data information
# expression info
codex_expression <- readExprMatrix(expr_path, transpose = FALSE)
# cell coordinate info
codex_locations <- data.table::fread(loc_path)
# metadata
codex_metadata <- data.table::fread(meta_path)
## stitch x.y tile coordinates to global coordinates
xtilespan <- 1344
ytilespan <- 1008
# TODO: expand the documentation and input format of stitchTileCoordinates. Probably not enough information for new users.
stitch_file <- stitchTileCoordinates(location_file = codex_metadata,
Xtilespan = xtilespan,
Ytilespan = ytilespan)
codex_locations <- stitch_file[,.(Xcoord, Ycoord)]
# create Giotto object
codex_test <- createGiottoObject(expression = codex_expression,
spatial_locs = codex_locations,
instructions = instructions)
codex_metadata$cell_ID <- as.character(codex_metadata$cellID)
codex_test <- addCellMetadata(codex_test, new_metadata = codex_metadata,
by_column = TRUE,
column_cell_ID = "cell_ID")
# subset Giotto object
cell_metadata <- pDataDT(codex_test)
cell_IDs_to_keep <- cell_metadata[Imaging_phenotype_cell_type != "dirt" & Imaging_phenotype_cell_type != "noid" & Imaging_phenotype_cell_type != "capsule",]$cell_ID
codex_test <- subsetGiotto(codex_test,
cell_ids = cell_IDs_to_keep)
## filter
codex_test <- filterGiotto(gobject = codex_test,
expression_threshold = 1,
feat_det_in_min_cells = 10,
min_det_feats_per_cell = 2,
expression_values = "raw",
verbose = TRUE)
codex_test <- normalizeGiotto(gobject = codex_test,
scalefactor = 6000,
verbose = TRUE,
log_norm = FALSE,
library_size_norm = FALSE,
scale_feats = FALSE,
scale_cells = TRUE)
## add gene & cell statistics
codex_test <- addStatistics(gobject = codex_test,
expression_values = "normalized")
## adjust expression matrix for technical or known variables
codex_test <- adjustGiottoMatrix(gobject = codex_test,
expression_values = "normalized",
batch_columns = "sample_Xtile_Ytile",
covariate_columns = NULL,
return_gobject = TRUE,
update_slot = "custom")
python already initialized in this session active environment : 'giotto_env' python version : 3.10 completed 1: preparation completed 2: subset expression data completed 3: subset spatial locations completed 4: subset cell metadata completed 5: subset feature metadata completed 6: subset spatial network(s) completed 7: subsetted dimension reductions completed 8: subsetted nearest network(s) completed 9: subsetted spatial enrichment results
Feature type: rna Number of cells removed: 2 out of 80019 Number of feats removed: 0 out of 30
Setting expression [cell][rna] normalized Setting expression [cell][rna] scaled calculating statistics for "normalized" expression Warning message: “The `update_slot` argument of `adjustGiottoMatrix()` is deprecated as of <NA> 4.1.7. ℹ Please use the `name` argument instead. ℹ The deprecated feature was likely used in the base package. Please report the issue to the authors.” Setting expression [cell][rna] custom
In [4]:
options(repr.plot.width = 7, repr.plot.height = 7)
SpatDimPlot(codex_test, color_by = "lightblue", size = 0.25)
In [5]:
options(repr.plot.width = 14, repr.plot.height = 7)
SpatDimPlot(
codex_test,
group_by = "sample_Xtile_Ytile",
size = 0.2
)
In [6]:
# PCA
codex_test <- runPCA(gobject = codex_test,
expression_values = "normalized",
scale_unit = TRUE,
method = "factominer")
# UMAP
codex_test <- runUMAP(codex_test,
dimensions_to_use = 1:14,
n_components = 2,
n_threads = 12)
## sNN network (default)
codex_test <- createNearestNetwork(gobject = codex_test,
dimensions_to_use = 1:14,
k = 20)
## 0.1 resolution
codex_test <- doLeidenCluster(gobject = codex_test,
resolution = 0.5,
n_iterations = 100,
name = "leiden")
clusters_cell_types <- c("naive B cells", "B cells", "B cells", "naive B cells",
"B cells", "macrophages", "erythroblasts",
"erythroblasts", "erythroblasts", "CD8 + T cells",
"Naive T cells", "CD4+ T cells", "Naive T cells",
"CD4+ T cells", "Dendritic cells", "NK cells",
"Dendritic cells", "Plasma cells", "endothelial cells",
"monocytes")
names(clusters_cell_types) <- c(2, 15, 13, 5, 8, 9, 19, 1, 10, 3, 12, 14, 4, 6,
7, 16, 17, 18, 11, 20)
codex_test <- annotateGiotto(gobject = codex_test,
annotation_vector = clusters_cell_types,
cluster_column = "leiden",
name = "cell_types")
cell_metadatadata <- pDataDT(codex_test)
subset_cell_ids <- cell_metadatadata[sample_Xtile_Ytile=="BALBc-3_X04_Y08"]$cell_ID
codex_test_zone1 <- subsetGiotto(codex_test,
cell_ids = subset_cell_ids)
"hvf" was not found in the gene metadata information. all genes will be used. Warning message in .run_pca_factominer(x = t_flex(expr_values), scale = scale_unit, : “ncp > ncol(x), will be set to ncol(x)”
Setting dimension reduction [cell][rna] pca Setting dimension reduction [cell][rna] umap
In [48]:
options(repr.plot.width = 14, repr.plot.height = 6)
p1 <- CellDimPlot(codex_test, reduction = "pca", group_by = "cell_types")
p2 <- CellDimPlot(codex_test, reduction = "umap", group_by = "cell_types")
p1 + p2
In [49]:
options(repr.plot.width = 7, repr.plot.height = 6)
CellDimPlot(codex_test, reduction = "umap", group_by = "leiden")
In [7]:
options(repr.plot.width = 7, repr.plot.height = 7)
codex_test@cell_metadata$cell$rna$leiden <- factor(
as.character(codex_test@cell_metadata$cell$rna$leiden),
levels = unique(as.character(sort(codex_test@cell_metadata$cell$rna$leiden))))
SpatDimPlot(
codex_test,
group_by = "leiden",
size = 0.2
)
In [8]:
options(repr.plot.width = 12, repr.plot.height = 7)
SpatDimPlot(
codex_test,
group_by = "Imaging_phenotype_cell_type",
size = 0.2
)
In [9]:
options(repr.plot.width = 8, repr.plot.height = 5)
SpatDimPlot(
codex_test_zone1,
group_by = "Imaging_phenotype_cell_type",
size = 1
)
In [10]:
options(repr.plot.width = 10, repr.plot.height = 5)
SpatFeaturePlot(
codex_test_zone1,
features = c("CD8a","CD19"),
layer = "scaled",
size = 1
)
In [11]:
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 4.4.3 (2025-02-28) Platform: x86_64-conda-linux-gnu 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 Giotto_4.2.1 GiottoClass_0.4.7 loaded via a namespace (and not attached): [1] fs_1.6.6 matrixStats_1.5.0 [3] spatstat.sparse_3.1-0 bitops_1.0-9 [5] sf_1.0-20 devtools_2.4.5 [7] httr_1.4.7 RColorBrewer_1.1-3 [9] repr_1.1.7 profvis_0.4.0 [11] tools_4.4.3 sctransform_0.4.2 [13] backports_1.5.0 DT_0.33 [15] R6_2.6.1 uwot_0.2.3 [17] lazyeval_0.2.2 urlchecker_1.0.1 [19] withr_3.0.2 sp_2.2-0 [21] gridExtra_2.3 GiottoUtils_0.2.5 [23] progressr_0.15.1 quantreg_6.00 [25] cli_3.6.5 Biobase_2.62.0 [27] spatstat.explore_3.4-3 fastDummies_1.7.5 [29] flashClust_1.01-2 iNEXT_3.0.1 [31] sandwich_3.1-1 labeling_0.4.3 [33] Seurat_5.3.0 mvtnorm_1.3-3 [35] spatstat.data_3.1-6 proxy_0.4-27 [37] ggridges_0.5.6 pbapply_1.7-2 [39] pbdZMQ_0.3-14 dbscan_1.2.2 [41] R.utils_2.13.0 stringdist_0.9.15 [43] parallelly_1.45.0 sessioninfo_1.2.3 [45] limma_3.62.2 VGAM_1.1-13 [47] rstudioapi_0.17.1 generics_0.1.4 [49] shape_1.4.6.1 gtools_3.9.5 [51] ica_1.0-3 spatstat.random_3.4-1 [53] dplyr_1.1.4 leaps_3.2 [55] Matrix_1.7-3 S4Vectors_0.40.2 [57] abind_1.4-5 R.methodsS3_1.8.2 [59] terra_1.8-42 lifecycle_1.0.4 [61] scatterplot3d_0.3-44 multcomp_1.4-28 [63] SummarizedExperiment_1.32.0 SparseArray_1.2.4 [65] Rtsne_0.17 grid_4.4.3 [67] promises_1.3.2 crayon_1.5.3 [69] miniUI_0.1.2 lattice_0.22-7 [71] cowplot_1.1.3 pillar_1.10.2 [73] GenomicRanges_1.54.1 rjson_0.2.23 [75] estimability_1.5.1 future.apply_1.20.0 [77] codetools_0.2-20 glue_1.8.0 [79] spatstat.univar_3.1-3 data.table_1.17.4 [81] remotes_2.5.0 vctrs_0.6.5 [83] png_0.1-8 spam_2.11-1 [85] gtable_0.3.6 assertthat_0.2.1 [87] cachem_1.1.0 S4Arrays_1.2.1 [89] mime_0.13 tidygraph_1.3.0 [91] survival_3.8-3 SingleCellExperiment_1.24.0 [93] units_0.8-5 statmod_1.5.0 [95] TH.data_1.1-3 ellipsis_0.3.2 [97] scRepertoire_2.2.1 fitdistrplus_1.2-2 [99] ROCR_1.0-11 nlme_3.1-168 [101] usethis_3.1.0 RcppAnnoy_0.0.22 [103] evd_2.3-7.1 GenomeInfoDb_1.38.8 [105] rprojroot_2.0.4 irlba_2.3.5.1 [107] KernSmooth_2.23-26 DBI_1.2.3 [109] plotthis_0.7.1 colorspace_2.1-1 [111] BiocGenerics_0.48.1 tidyselect_1.2.1 [113] emmeans_1.11.1 compiler_4.4.3 [115] SparseM_1.84-2 xml2_1.3.8 [117] desc_1.4.3 ggdendro_0.2.0 [119] DelayedArray_0.28.0 plotly_4.10.4 [121] checkmate_2.3.2 scales_1.4.0 [123] classInt_0.4-11 lmtest_0.9-40 [125] multcompView_0.1-10 rappdirs_0.3.3 [127] stringr_1.5.1 digest_0.6.37 [129] goftest_1.2-3 spatstat.utils_3.1-4 [131] XVector_0.42.0 htmltools_0.5.8.1 [133] GiottoVisuals_0.2.12 pkgconfig_2.0.3 [135] base64enc_0.1-3 MatrixGenerics_1.14.0 [137] FactoMineR_2.11 fastmap_1.2.0 [139] rlang_1.1.6 GlobalOptions_0.1.2 [141] htmlwidgets_1.6.4 shiny_1.10.0 [143] farver_2.1.2 zoo_1.8-14 [145] jsonlite_2.0.0 R.oo_1.27.1 [147] RCurl_1.98-1.17 magrittr_2.0.3 [149] GenomeInfoDbData_1.2.11 dotCall64_1.2 [151] patchwork_1.3.0 IRkernel_1.3.2 [153] Rcpp_1.0.14 evmix_2.12 [155] ggnewscale_0.5.1 viridis_0.6.5 [157] reticulate_1.42.0 truncdist_1.0-2 [159] stringi_1.8.7 ggalluvial_0.12.5 [161] ggraph_2.2.1 zlibbioc_1.48.2 [163] MASS_7.3-64 plyr_1.8.9 [165] pkgbuild_1.4.8 parallel_4.4.3 [167] listenv_0.9.1 ggrepel_0.9.6 [169] forcats_1.0.0 deldir_2.0-4 [171] graphlayouts_1.2.2 IRdisplay_1.1 [173] splines_4.4.3 gridtext_0.1.5 [175] tensor_1.5 circlize_0.4.16 [177] colorRamp2_0.1.0 igraph_2.0.3 [179] uuid_1.2-1 spatstat.geom_3.4-1 [181] cubature_2.1.4 RcppHNSW_0.6.0 [183] reshape2_1.4.4 stats4_4.4.3 [185] pkgload_1.4.0 evaluate_1.0.3 [187] SeuratObject_5.1.0 tweenr_2.0.3 [189] httpuv_1.6.15 MatrixModels_0.5-4 [191] RANN_2.6.2 tidyr_1.3.1 [193] purrr_1.0.4 polyclip_1.10-7 [195] future_1.58.0 scattermore_1.2 [197] ggplot2_3.5.2 ggforce_0.4.2 [199] xtable_1.8-4 e1071_1.7-16 [201] RSpectra_0.16-2 later_1.4.2 [203] class_7.3-23 viridisLite_0.4.2 [205] gsl_2.1-8 tibble_3.2.1 [207] memoise_2.0.1 IRanges_2.36.0 [209] cluster_2.1.8.1 globals_0.18.0