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doc: add CODEX examples for spatial plots
1 parent bf42ab8 commit bee5acb

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‎R/spatialutils.R‎

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@@ -180,7 +180,12 @@ NULL
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if (ncol(points_args$data) > 0) {
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points_args$data <- points_args$data[, colnames(points_args$data)[!is.na(colnames(points_args$data))], drop = FALSE]
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}
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points_args$data <- cbind(points_args$data, points_data)
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points_args$data <- cbind(points_data, points_args$data)
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# check if there is duplicated columns
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dup_cols <- duplicated(colnames(points_args$data))
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if (any(dup_cols)) {
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points_args$data <- points_args$data[, !dup_cols, drop = FALSE]
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}
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# x and y are already handled in points_data, we don't need to handle the swapping here
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points_args$data[[points_args$x]] <- points_args$data[[points_args$x]] * scale_factor
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points_args$data[[points_args$y]] <- points_args$data[[points_args$y]] * scale_factor
@@ -209,7 +214,6 @@ NULL
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}
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facet_by <- NULL
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if (!is.null(group_by)) {
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if (nrow(object@meta.data) == nrow(points_args$data)) {
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points_args$data[[group_by]] <- object@meta.data[[group_by]]
@@ -238,7 +242,6 @@ NULL
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facet_by <- ".facet_var"
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}
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}
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points_args$legend.position <- legend.position
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points_args$legend.direction <- legend.direction
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points_args$flip_y <- flip_y

‎_pkgdown.yml‎

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@@ -30,6 +30,8 @@ navbar:
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href: articles/Seurat_Xenium.html
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- text: "Visualizing Nanostring CosMx data prepared with Seurat"
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href: articles/Seurat_Nanostring_CosMx.html
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- text: "Visualizing Akoya_CODEX data prepared with Seurat"
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href: articles/Seurat_Akoya_CODEX.html
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- text: "Visualizing Visium data prepared with Giotto"
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href: articles/Giotto_Visium.html
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- text: "Visualizing VisiumHD data prepared with Giotto"
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href: articles/Giotto_Spatial_CITE-Seq.html
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- text: "Visualizing Nanostirng CosMx data prepared with Giotto"
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href: articles/Giotto_Nanostring_CosMx.html
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- text: "Visualizing CODEX data prepared with Giotto"
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href: articles/Giotto_CODEX.html
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reference:
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- title: scRNA-seq

‎man/SpatDimPlot.Rd‎

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‎man/SpatFeaturePlot.Rd‎

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‎notebooks/spatial/Giotto.CODEX.ipynb‎

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‎notebooks/spatial/Giotto.Nanostring_CosMx.ipynb‎

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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"execution_count": 1,
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"id": "24b4fec1",
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"metadata": {
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"vscode": {
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"languageId": "r"
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}
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},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"Loading required package: GiottoClass\n",
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"\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"Newer devel version of GiottoClass on GitHub: 0.4.8\n",
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"\n",
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"Giotto Suite 4.2.1\n",
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"\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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},
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{
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"cell_type": "code",
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"id": "cellid-missed-1",
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"execution_count": 108,
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"id": "cellid-missed-1",
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"metadata": {
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"vscode": {
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},
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{
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"cell_type": "code",
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"execution_count": 109,
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"id": "cellid-missed-2",
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"metadata": {
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},
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{
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"cell_type": "code",
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"execution_count": 129,
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"metadata": {
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},
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{
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"cell_type": "code",
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"execution_count": 130,
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"metadata": {
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "e66b989f",
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"execution_count": 39,
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"id": "sessioninfo",
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"metadata": {
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"vscode": {
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"languageId": "r"
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}
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},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"Meta data updated for assay: rna\n",
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"\n"
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]
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},
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{
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"name": "stderr",
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Meta features updated for assay: negprobes\n",
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"\n"
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"R version 4.4.3 (2025-02-28)\n",
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"Platform: x86_64-conda-linux-gnu\n",
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"Running under: Red Hat Enterprise Linux 8.10 (Ootpa)\n",
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"\n",
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"Matrix products: default\n",
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"\n",
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"locale:\n",
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" [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C \n",
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" [3] LC_TIME=en_US.UTF-8 LC_COLLATE=C \n",
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" [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8 \n",
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" [7] LC_PAPER=en_US.UTF-8 LC_NAME=C \n",
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" [9] LC_ADDRESS=C LC_TELEPHONE=C \n",
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"[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C \n",
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"\n",
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"time zone: America/Chicago\n",
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"tzcode source: system (glibc)\n",
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"\n",
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"attached base packages:\n",
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"[1] stats graphics grDevices utils datasets methods base \n",
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"\n",
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"other attached packages:\n",
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"[1] scplotter_0.4.0 Giotto_4.2.1 GiottoClass_0.4.7\n",
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"\n",
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"loaded via a namespace (and not attached):\n",
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" [1] fs_1.6.6 matrixStats_1.5.0 \n",
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" [3] spatstat.sparse_3.1-0 bitops_1.0-9 \n",
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" [5] devtools_2.4.5 httr_1.4.7 \n",
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" [7] RColorBrewer_1.1-3 repr_1.1.7 \n",
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" [9] profvis_0.4.0 tools_4.4.3 \n",
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" [11] sctransform_0.4.2 backports_1.5.0 \n",
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" [13] R6_2.6.1 uwot_0.2.3 \n",
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" [15] lazyeval_0.2.2 urlchecker_1.0.1 \n",
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" [17] withr_3.0.2 sp_2.2-0 \n",
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" [19] gridExtra_2.3 GiottoUtils_0.2.5 \n",
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" [21] progressr_0.15.1 quantreg_6.00 \n",
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" [23] cli_3.6.5 Biobase_2.62.0 \n",
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" [25] spatstat.explore_3.4-3 fastDummies_1.7.5 \n",
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" [27] iNEXT_3.0.1 Seurat_5.3.0 \n",
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" [29] spatstat.data_3.1-6 ggridges_0.5.6 \n",
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" [31] pbapply_1.7-2 pbdZMQ_0.3-14 \n",
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" [33] stringdist_0.9.15 parallelly_1.45.0 \n",
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" [35] sessioninfo_1.2.3 VGAM_1.1-13 \n",
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" [37] rstudioapi_0.17.1 generics_0.1.4 \n",
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" [39] shape_1.4.6.1 gtools_3.9.5 \n",
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" [41] ica_1.0-3 spatstat.random_3.4-1 \n",
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" [43] dplyr_1.1.4 Matrix_1.7-3 \n",
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" [45] S4Vectors_0.40.2 abind_1.4-5 \n",
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" [47] terra_1.8-42 lifecycle_1.0.4 \n",
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" [49] SummarizedExperiment_1.32.0 SparseArray_1.2.4 \n",
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" [51] Rtsne_0.17 grid_4.4.3 \n",
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" [53] promises_1.3.2 crayon_1.5.3 \n",
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" [55] miniUI_0.1.2 lattice_0.22-7 \n",
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" [57] cowplot_1.1.3 pillar_1.10.2 \n",
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" [59] GenomicRanges_1.54.1 rjson_0.2.23 \n",
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" [61] future.apply_1.20.0 codetools_0.2-20 \n",
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" [63] glue_1.8.0 spatstat.univar_3.1-3 \n",
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" [65] data.table_1.17.4 remotes_2.5.0 \n",
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" [67] vctrs_0.6.5 png_0.1-8 \n",
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" [69] spam_2.11-1 gtable_0.3.6 \n",
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" [71] assertthat_0.2.1 cachem_1.1.0 \n",
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" [73] S4Arrays_1.2.1 mime_0.13 \n",
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" [75] tidygraph_1.3.0 survival_3.8-3 \n",
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" [77] SingleCellExperiment_1.24.0 ellipsis_0.3.2 \n",
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" [79] scRepertoire_2.2.1 fitdistrplus_1.2-2 \n",
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" [81] ROCR_1.0-11 nlme_3.1-168 \n",
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" [83] usethis_3.1.0 RcppAnnoy_0.0.22 \n",
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" [85] evd_2.3-7.1 GenomeInfoDb_1.38.8 \n",
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" [87] rprojroot_2.0.4 irlba_2.3.5.1 \n",
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" [89] KernSmooth_2.23-26 plotthis_0.7.0 \n",
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" [91] colorspace_2.1-1 BiocGenerics_0.48.1 \n",
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" [93] tidyselect_1.2.1 compiler_4.4.3 \n",
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" [95] SparseM_1.84-2 xml2_1.3.8 \n",
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" [97] desc_1.4.3 ggdendro_0.2.0 \n",
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" [99] DelayedArray_0.28.0 plotly_4.10.4 \n",
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"[101] checkmate_2.3.2 scales_1.4.0 \n",
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"[103] lmtest_0.9-40 rappdirs_0.3.3 \n",
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"[105] stringr_1.5.1 digest_0.6.37 \n",
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"[107] goftest_1.2-3 spatstat.utils_3.1-4 \n",
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"[109] XVector_0.42.0 htmltools_0.5.8.1 \n",
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"[111] GiottoVisuals_0.2.12 pkgconfig_2.0.3 \n",
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"[113] base64enc_0.1-3 MatrixGenerics_1.14.0 \n",
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"[115] fastmap_1.2.0 rlang_1.1.6 \n",
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"[117] GlobalOptions_0.1.2 htmlwidgets_1.6.4 \n",
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"[119] shiny_1.10.0 farver_2.1.2 \n",
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"[121] zoo_1.8-14 jsonlite_2.0.0 \n",
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"[123] RCurl_1.98-1.17 magrittr_2.0.3 \n",
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"[125] GenomeInfoDbData_1.2.11 dotCall64_1.2 \n",
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"[127] patchwork_1.3.0 IRkernel_1.3.2 \n",
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"[129] Rcpp_1.0.14 evmix_2.12 \n",
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"[131] ggnewscale_0.5.1 viridis_0.6.5 \n",
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"[133] reticulate_1.42.0 truncdist_1.0-2 \n",
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"[135] stringi_1.8.7 ggalluvial_0.12.5 \n",
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"[137] ggraph_2.2.1 zlibbioc_1.48.2 \n",
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"[139] MASS_7.3-64 plyr_1.8.9 \n",
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"[141] pkgbuild_1.4.8 parallel_4.4.3 \n",
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"[143] listenv_0.9.1 ggrepel_0.9.6 \n",
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"[145] forcats_1.0.0 deldir_2.0-4 \n",
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"[147] graphlayouts_1.2.2 IRdisplay_1.1 \n",
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"[149] splines_4.4.3 gridtext_0.1.5 \n",
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"[151] tensor_1.5 circlize_0.4.16 \n",
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"[153] colorRamp2_0.1.0 igraph_2.0.3 \n",
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"[155] uuid_1.2-1 spatstat.geom_3.4-1 \n",
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"[157] cubature_2.1.4 RcppHNSW_0.6.0 \n",
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"[159] reshape2_1.4.4 stats4_4.4.3 \n",
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"[161] pkgload_1.4.0 evaluate_1.0.3 \n",
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"[163] SeuratObject_5.1.0 tweenr_2.0.3 \n",
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"[165] httpuv_1.6.15 MatrixModels_0.5-4 \n",
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"[167] RANN_2.6.2 tidyr_1.3.1 \n",
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"[169] purrr_1.0.4 polyclip_1.10-7 \n",
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"[171] future_1.58.0 scattermore_1.2 \n",
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"[173] ggplot2_3.5.2 ggforce_0.4.2 \n",
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"[175] xtable_1.8-4 RSpectra_0.16-2 \n",
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"[177] later_1.4.2 viridisLite_0.4.2 \n",
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"[179] gsl_2.1-8 tibble_3.2.1 \n",
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"[181] memoise_2.0.1 IRanges_2.36.0 \n",
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"[183] cluster_2.1.8.1 globals_0.18.0 "
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]
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},
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{
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"data": {
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"text/plain": [
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"An object of class Seurat \n",
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"980 features across 73222 samples within 2 assays \n",
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"Active assay: rna (960 features, 0 variable features)\n",
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" 3 layers present: counts, data, scale.data\n",
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" 1 other assay present: negprobes\n",
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" 4 dimensional reductions calculated: pca, tsne, umap, spatial\n",
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" 56 images present: composite_fov001, composite_fov002, composite_fov003, composite_fov004, composite_fov005, composite_fov006, composite_fov007, composite_fov008, composite_fov009, composite_fov010, composite_fov011, composite_fov012, composite_fov013, composite_fov014, composite_fov015, composite_fov016, composite_fov017, composite_fov018, composite_fov019, composite_fov020, composite_fov021, composite_fov022, composite_fov023, composite_fov024, composite_fov025, composite_fov026, composite_fov027, composite_fov028, overlay_fov001, overlay_fov002, overlay_fov003, overlay_fov004, overlay_fov005, overlay_fov006, overlay_fov007, overlay_fov008, overlay_fov009, overlay_fov010, overlay_fov011, overlay_fov012, overlay_fov013, overlay_fov014, overlay_fov015, overlay_fov016, overlay_fov017, overlay_fov018, overlay_fov019, overlay_fov020, overlay_fov021, overlay_fov022, overlay_fov023, overlay_fov024, overlay_fov025, overlay_fov026, overlay_fov027, overlay_fov028"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"# Convert to Seurat to test with scplotter working with Seurat v5\n",
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"# seurat_cosmx <- giottoToSeuratV5(cosmx)\n",
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"# saveRDS(seurat_cosmx, file = \"data/Nanostring_CosMx_Subcellular_Lung_Cancer/seurat.rds\")\n",
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"# seurat_cosmx"
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"x <- sessionInfo()\n",
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"x <- capture.output(print(x))\n",
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"# hide the BLAS/LAPACK paths\n",
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"x <- x[!startsWith(x, \"BLAS/LAPACK:\")]\n",
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"cat(paste(x, collapse = \"\\n\"))"
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]
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}
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],

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