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Giotto Suite 空间转录组学分析工具包 - 100%覆盖718个文件（37个教程+285个核心API+4个数据文档+344个扩展函数+49个工具函数）

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SKILL.md

name giottosuite-local
description Giotto Suite 空间转录组学分析工具包 - 100%覆盖718个文件（37个教程+285个核心API+4个数据文档+344个扩展函数+49个工具函数）

Giottosuite-Local Skill

Comprehensive assistance with Giotto Suite spatial transcriptomics analysis, generated from official documentation.

When to Use This Skill

This skill should be triggered when:

  • Working with spatial transcriptomics data from any platform (10x Visium, Xenium, seqFISH+, MERFISH, etc.)
  • Setting up Giotto objects from different data formats and sources
  • Performing spatial data analysis including clustering, dimension reduction, and visualization
  • Processing spatial multi-omics data (RNA, protein, ATAC, imaging)
  • Creating spatial visualizations and plots
  • Implementing spatial domain detection using HMRF or other methods
  • Doing spatial statistics and spatially variable gene detection
  • Integrating multiple spatial datasets or single-cell with spatial data
  • Troubleshooting Giotto workflows and understanding error messages
  • Learning spatial analysis best practices and workflows

Quick Reference

1. Create Giotto Object from 10x Visium Data

# Basic Visium object creation
visium_gobj <- createGiottoVisiumObject(
  visium_dir = "path/to/visium_directory",
  expr_data = "filtered",  # or "raw"
  gene_column_index = 2,   # gene symbols vs ensemble IDs
  instructions = instrs
)

2. Load Xenium Spatial Data with Transcript Points

# Load Xenium data with full transcript information
xenium_gobj <- createGiottoXeniumObject(
  xenium_dir = file.path(data_path, "outs"),
  qv_threshold = 20,
  feat_type = c("rna", "UnassignedCodeword", "NegControlCodeword", "NegControlProbe"),
  split_keyword = list(c("BLANK"), c("NegControlCodeword"), c("NegControlProbe", "antisense")),
  instructions = instrs
)

3. Filter and Normalize Data

# Standard data processing pipeline
gobj <- gobj |>
  filterGiotto(
    expression_threshold = 1,
    feat_det_in_min_cells = 3,
    min_det_feats_per_cell = 5
  ) |>
  normalizeGiotto() |>
  addStatistics() |>
  runPCA()

4. Create Spatial Networks and Clusters

# Build spatial relationships and cluster
gobj <- gobj |>
  createNearestNetwork(k = 10, dimensions_to_use = 1:10) |>
  doLeidenCluster(resolution = 0.25, n_iterations = 100)

# Visualize clusters spatially
spatPlot2D(gobj, cell_color = 'leiden_clus', point_size = 0.1)

5. Run HMRF Spatial Domain Analysis

# Spatial domain detection with HMRF
HMRF_spatial_genes <- doHMRF(
  gobject = gobj,
  expression_values = "scaled",
  spatial_genes = spatial_genes,
  k = 20,
  spatial_network_name = "spatial_network",
  betas = c(0, 10, 20)
)

# Add results to object
gobj <- addHMRF(gobj, HMRFoutput = HMRF_spatial_genes, k = 20,
                betas_to_add = c(0, 10, 20), hmrf_name = "HMRF")

6. Cross-Section Analysis for 3D Data

# Create cross-section from 3D data
gobj <- createCrossSection(
  gobj,
  method = "equation",
  equation = c(0, 1, 0, 600),  # Ax + By + Cz = D
  extend_ratio = 0.6,
  name = "new_cs"
)

# Plot cross-section
crossSectionPlot(gobj, name = "new_cs")

7. Multi-omics Integration with WNN

# Integrate RNA and protein data
gobj <- runIntegratedUMAP(
  gobject = gobj,
  spat_unit = "cell",
  feat_types = c("rna", "protein"),
  integration_method = "WNN",
  k = 20
)

8. Filter Distributions Visualization

# Visualize expression distributions
filterDistributions(
  gobj,
  expression_threshold = 3,
  method = "sum",
  plot_type = "violin",
  scale_axis = "log2"
)

Pattern 1: Xenium Human Breast Cancer Pre-Release Source: vignettes/xenium_breast_cancer.rmd xenium_breast_cancer.rmd This is a legacy dataset. For a more recent example, see Xenium FFPE Human Lung Cancer with Multimodal Cell Segmentation 1 Install Extra Packages 1.1 arrow installation Xenium datasets requires arrow with ZTSD support to be installed to work with parquet files. This is optional if you want to use a different format for the tabular data. has_arrow <- requireNamespace("arrow", quietly = TRUE) zstd <- TRUE if (has_arrow) { # check arrow_info() to see that zstd support should be TRUE # See https://arrow.apache.org/docs/r/articles/install.html for details zstd <- arrow::arrow_info()$capabilities[["zstd"]] } if (!has_arrow || !zstd) { # install with compression library needed for 10x parquet files # this may take a while Sys.setenv(ARROW_WITH_ZSTD = "ON") install.packages("arrow", repos = c("https://apache.r-universe.dev"), type = "source") } 1.2 tifffile and imagecodecs installation tifffile is a python package for working with tif images. imagecodecs provides the needed JPEG2000 compression codec. 10x provides their images as ome.tif but Giotto usually either cannot open or incurs a large speed penalty when accessing these files. Instead we use tifffile to convert these images into normal tif images. Check that Giotto can access a python env and install one if it can’t. Then activate that python env. library(Giotto) # Ensure Giotto can access a python env genv_exists <- checkGiottoEnvironment() if(!genv_exists){ # The following command need only be run once to # install a default Giotto environment installGiottoEnvironment() } # set specific python path to use or python environment name # leaving NULL will use default (see ?set_giotto_python_path()) python_path <- NULL set_giotto_python_path(python_path = python_path) Check for presence of tifffile and imagecodecs in selected python env then install if missing. # install packages if not already installed in environment need_py_inst <- try(GiottoUtils::package_check( pkg_name = c("tifffile", "imagecodecs"), repository = c("pip:tifffile", "pip:imagecodecs") ), silent = TRUE) if (!isTRUE(need_py_inst)) { active_env <- GiottoUtils::py_active_env() reticulate::conda_install( envname = active_env, packages = c("tifffile", "imagecodecs"), pip = TRUE ) # may need a session restart after installation } 2 Dataset Explanation This vignette covers Giotto object creation and simple exploratory analysis with 10x Genomics’ subcellular Xenium In Situ platform data using their Human Breast Cancer Dataset provided with their bioRxiv pre-print. This is a legacy pre-release dataset, and some aspects such as file and QC probe naming and image alignment have changed since. 2.1 Download Links The data from the first tissue replicate will be worked with. The files to download are: curl links from 10x genomics # Input Files curl -O https://cf.10xgenomics.com/samples/xenium/1.0.1/Xenium_FFPE_Human_Breast_Cancer_Rep1/Xenium_FFPE_Human_Breast_Cancer_Rep1_panel.tsv curl -O https://cf.10xgenomics.com/samples/xenium/1.0.1/Xenium_FFPE_Human_Breast_Cancer_Rep1/Xenium_FFPE_Human_Breast_Cancer_Rep1_gene_groups.csv curl -O https://cf.10xgenomics.com/samples/xenium/1.0.1/Xenium_FFPE_Human_Breast_Cancer_Rep1/Xenium_FFPE_Human_Breast_Cancer_Rep1_he_image.ome.tif curl -O https://cf.10xgenomics.com/samples/xenium/1.0.1/Xenium_FFPE_Human_Breast_Cancer_Rep1/Xenium_FFPE_Human_Breast_Cancer_Rep1_he_imagealignment.csv curl -O https://cf.10xgenomics.com/samples/xenium/1.0.1/Xenium_FFPE_Human_Breast_Cancer_Rep1/Xenium_FFPE_Human_Breast_Cancer_Rep1_if_image.ome.tif curl -O https://cf.10xgenomics.com/samples/xenium/1.0.1/Xenium_FFPE_Human_Breast_Cancer_Rep1/Xenium_FFPE_Human_Breast_Cancer_Rep1_if_imagealignment.csv # Output Files curl -O https://cf.10xgenomics.com/samples/xenium/1.0.1/Xenium_FFPE_Human_Breast_Cancer_Rep1/Xenium_FFPE_Human_Breast_Cancer_Rep1_outs.zip Do not download the provided .tif images. They are cropped and scaled differently from the ome.tif images, which presents difficulties when using the provided alignment information. 2.2 Expected Directory Structure When unzipped, you should have the following directory structure: expand /path/to/data/ ├── Xenium_FFPE_Human_Breast_Cancer_Rep1_gene_groups.csv ├── Xenium_FFPE_Human_Breast_Cancer_Rep1_he_image.ome.tif ├── Xenium_FFPE_Human_Breast_Cancer_Rep1_he_imagealignment.csv ├── Xenium_FFPE_Human_Breast_Cancer_Rep1_if_image.ome.tif ├── Xenium_FFPE_Human_Breast_Cancer_Rep1_if_imagealignment.csv ├── Xenium_FFPE_Human_Breast_Cancer_Rep1_panel.tsv └── outs ├── analysis │ ├── clustering │ │ ├── ... │ ├── diffexp │ │ ├── ... │ ├── pca │ │ └── ... │ ├── tsne │ │ └── ... │ └── umap │ └── ... ├── analysis.zarr.zip ├── analysis_summary.html ├── cell_boundaries.csv.gz ├── cell_boundaries.parquet ├── cell_feature_matrix │ ├── barcodes.tsv.gz │ ├── features.tsv.gz │ └── matrix.mtx.gz ├── cell_feature_matrix.h5 ├── cell_feature_matrix.zarr.zip ├── cells.csv.gz ├── cells.parquet ├── cells.zarr.zip ├── experiment.xenium ├── gene_panel.json ├── metrics_summary.csv ├── morphology.ome.tif ├── morphology_focus.ome.tif ├── morphology_mip.ome.tif ├── nucleus_boundaries.csv.gz ├── nucleus_boundaries.parquet ├── transcripts.csv.gz ├── transcripts.parquet └── transcripts.zarr.zip The actual Xenium output directory is under outs in this layout. The outs folder will be used with the convenience functions to load into a giotto analysis object. Input files are one directory level up. Of note are the .ome.tif image and alignment .csv files. These are images of stainings (H&E and IF) generated after the Xenium run has finished. Since these were imaged on external systems, they have been aligned to the rest of the data using Xenium Explorer. The alignment .csv file is an affine transformation matrix used to align the image to the Xenium dataset. more about xenium pre-release image types This dataset provides several images images to load Xenium_FFPE_Human_Breast_Cancer_Rep1_he_image.ome.tif post-Xenium H&E. Used with alignment matrix. Xenium_FFPE_Human_Breast_Cancer_Rep1_if_image.ome.tif post-Xenium IF. Used with alignment matrix. morphology_focus.ome.tif DAPI image combined from the most in-focus regions from multiple z stacks images not loaded morphology.ome.tif z-stacked image. Many planes may be less focused morphology_mip.ome.tif maximum intensity projection image created from the morphology.ome.tif. Has more even brightness compared against morphology_focus.ome.tif, but is also slightly blurrier. This image type was only provided with the pre-release and does not show up in later Xenium pipeline versions. Also note that 10x provides several formats for many of the outputs. This will be touched on later when loading the data in. 3 Load Xenium Data The Xenium data can be loaded using createGiottoXeniumObject() The default behavior is to load: transcripts information cell and nucleus boundaries morphology focus images (DAPI and IF cell boundary stains if any) feature metadata (gene_panel.json) Alternative data to load We skip loading of: expression cell metadata They can be loaded if load_expression and load_cellmeta respectively are set to TRUE. We normally skip them since Giotto’s aggregation results may produce slightly different results than those from 10X. The molecule transcript detections can also be skipped if directly using the 10X expression information alongside the polygons. You can do this by setting load_transcripts to FALSE. It will not be possible to plot the individual transcript detections if they are not loaded in, but memory usage is greatly reduced. As an additional note, the provided expression values are generated from a QV threshold of 20 (described below) with the cell polygons. If a different QV threshold or usage of the nuclear segmentations is desired, then de novo aggregation from polygons and points will be required. Expected peak RAM usage: with transcripts: ~70GB without transcripts: ~5GB For the full dataset (HPC): time: 5-6min | memory: 50GB # 1. ** SET PATH TO FOLDER CONTAINING XENIUM DATA ** data_path <- "path/to/data" # 2. ** SET WORKING DIRECTORY WHERE PROJECT OUTPUTS WILL SAVE TO ** results_folder = '/path/to/results/' # 3. Create Giotto instructions # Directly saving plots to the working directory without rendering them in the viewer saves time. instrs = createGiottoInstructions( save_dir = results_folder, save_plot = TRUE, show_plot = FALSE, return_plot = FALSE, python_path = python_path ) # These feat_type and split_keyword settings are specific to # pre-release and early versions of the Xenium pipeline feat_types <- c( "rna", "UnassignedCodeword", "NegControlCodeword", "NegControlProbe" ) split_keywords = list( c("BLANK"), c("NegControlCodeword"), c("NegControlProbe", "antisense") ) # 4. Create the object xenium_gobj <- createGiottoXeniumObject( xenium_dir = file.path(data_path, "outs"), qv_threshold = 20, # qv of 20 is the default and also what 10x uses feat_type = feat_types, split_keyword = split_keywords, # * if aligned images already converted to .tif, they could be added as named list # * instead, see next section # load_aligned_images = list( # post_he = c( # "path/to/...he_image.tif", # "path/to/...he_imagealignment.csv" # ), # CD20 = ..., # HER2 = ..., # DAPI = ... # ), instructions = instrs ) force(xenium_gobj) An object of class giotto >Active spat_unit: cell >Active feat_type: rna dimensions : 313, 167780 (features, cells) [SUBCELLULAR INFO] polygons : cell nucleus features : rna blank_codeword neg_control_codeword neg_control_probe [AGGREGATE INFO] spatial locations ---------------- [cell] raw [nucleus] raw attached images ------------------ images : dapi Use objHistory() to see steps and params used There are several parameters for additional or alternative items you can load. See dropdowns. Loading from non-standard directories or other provided file formats The convenience function auto-detects filepaths based on the Xenium directory path and the preferred file formats .parquet for tabular (vs .csv) .h5 for matrix over other formats when available (vs .mtx) .zarr is currently not supported. When you need to use a different file format or something is not in the standard Xenium output directory structure or naming scheme shown above, you can supply a specific filepath to createGiottoXeniumObject() using these parameters: expression_path = , cell_metadata_path = , transcript_path = , bounds_path = , gene_panel_json_path = , Note that if loading in the .mtx file, expression_path param should be passed the filepath to the cell_feature_matrix subdirectory instead of the .mtx file. qv_threshold setting qv_threshold = 20 # default The Quality Value is a Phred-based 0-40 value that 10X provides for every detection in their transcripts output. Higher values mean higher confidence in the decoded transcript identity. By default 10X uses a cutoff of QV = 20 for transcripts to use downstream. Setting a value other than 20 will make the loaded dataset different from the 10X-provided expression matrix and cell metadata. QV Calculation Raw Q-score based on how likely it is that an observed code is to be the codeword that it gets mapped to vs less likely codeword. Adjustment of raw Q-score by binning the transcripts by Q-value then adjusting the exact Q per bin based on proportion of Negative Control Codewords detected within. further info from 10x documentation feat_types and split_keywords These parameters govern how transcript types are split into different groups when loading. feat_types <- c( "rna", "UnassignedCodeword", "NegControlCodeword", "NegControlProbe" ) split_keywords = list( c("BLANK"), c("NegControlCodeword"), c("NegControlProbe", "antisense") ) There are 4 types of transcript detections that 10X reports with this dataset. Gene expression (313) - These are the 'rna' gene detections. rna <- xenium_gobj[["feat_info", "rna"]][[1]] plot(rna, dens = TRUE) plot of Gene expression (rna) density # [1] "ABCC11" "ACTA2" "ACTG2" "ADAM9" "ADGRE5" "ADH1B" # [7] "ADIPOQ" "AGR3" "AHSP" "AIF1" "AKR1C1" "AKR1C3" # [13] "ALDH1A3" "ANGPT2" "ANKRD28" "ANKRD29" "ANKRD30A" "APOBEC3A" # [19] "APOBEC3B" "APOC1" "AQP1" "AQP3" "AR" "AVPR1A" # [25] "BACE2" "BANK1" "BASP1" "BTNL9" "C15orf48" "C1QA" # [31] "C1QC" "C2orf42" "C5orf46" "C6orf132" "CAV1" "CAVIN2" # [37] "CCDC6" "CCDC80" "CCL20" "CCL5" "CCL8" "CCND1" # [43] "CCPG1" "CCR7" "CD14" "CD163" "CD19" "CD1C" # [49] "CD247" "CD27" "CD274" "CD3D" "CD3E" "CD3G" # [55] "CD4" "CD68" "CD69" "CD79A" "CD79B" "CD80" # [61] "CD83" "CD86" "CD8A" "CD8B" "CD9" "CD93" # [67] "CDC42EP1" "CDH1" "CEACAM6" "CEACAM8" "CENPF" "CLCA2" # [73] "CLDN4" "CLDN5" "CLEC14A" "CLEC9A" "CLECL1" "CLIC6" # [79] "CPA3" "CRHBP" "CRISPLD2" "CSF3" "CTH" "CTLA4" # [85] "CTSG" "CTTN" "CX3CR1" "CXCL12" "CXCL16" "CXCL5" # [91] "CXCR4" "CYP1A1" "CYTIP" "DAPK3" "DERL3" "DMKN" # [97] "DNAAF1" "DNTTIP1" "DPT" "DSC2" "DSP" "DST" # [103] "DUSP2" "DUSP5" "EDN1" "EDNRB" "EGFL7" "EGFR" # [109] "EIF4EBP1" "ELF3" "ELF5" "ENAH" "EPCAM" "ERBB2" # [115] "ERN1" "ESM1" "ESR1" "FAM107B" "FAM49A" "FASN" # [121] "FBLIM1" "FBLN1" "FCER1A" "FCER1G" "FCGR3A" "FGL2" # [127] "FLNB" "FOXA1" "FOXC2" "FOXP3" "FSTL3" "GATA3" # [133] "GJB2" "GLIPR1" "GNLY" "GPR183" "GZMA" "GZMB" # [139] "GZMK" "HAVCR2" "HDC" "HMGA1" "HOOK2" "HOXD8" # [145] "HOXD9" "HPX" "IGF1" "IGSF6" "IL2RA" "IL2RG" # [151] "IL3RA" "IL7R" "ITGAM" "ITGAX" "ITM2C" "JUP" # [157] "KARS" "KDR" "KIT" "KLF5" "KLRB1" "KLRC1" # [163] "KLRD1" "KLRF1" "KRT14" "KRT15" "KRT16" "KRT23" # [169] "KRT5" "KRT6B" "KRT7" "KRT8" "LAG3" "LARS" # [175] "LDHB" "LEP" "LGALSL" "LIF" "LILRA4" "LPL" # [181] "LPXN" "LRRC15" "LTB" "LUM" "LY86" "LYPD3" # [187] "LYZ" "MAP3K8" "MDM2" "MEDAG" "MKI67" "MLPH" # [193] "MMP1" "MMP12" "MMP2" "MMRN2" "MNDA" "MPO" # [199] "MRC1" "MS4A1" "MUC6" "MYBPC1" "MYH11" "MYLK" # [205] "MYO5B" "MZB1" "NARS" "NCAM1" "NDUFA4L2" "NKG7" # [211] "NOSTRIN" "NPM3" "OCIAD2" "OPRPN" "OXTR" "PCLAF" # [217] "PCOLCE" "PDCD1" "PDCD1LG2" "PDE4A" "PDGFRA" "PDGFRB" # [223] "PDK4" "PECAM1" "PELI1" "PGR" "PIGR" "PIM1" # [229] "PLD4" "POLR2J3" "POSTN" "PPARG" "PRDM1" "PRF1" # [235] "PTGDS" "PTN" "PTPRC" "PTRHD1" "QARS" "RAB30" # [241] "RAMP2" "RAPGEF3" "REXO4" "RHOH" "RORC" "RTKN2" # [247] "RUNX1" "S100A14" "S100A4" "S100A8" "SCD" "SCGB2A1" # [253] "SDC4" "SEC11C" "SEC24A" "SELL" "SERHL2" "SERPINA3" # [259] "SERPINB9" "SFRP1" "SFRP4" "SH3YL1" "SLAMF1" "SLAMF7" # [265] "SLC25A37" "SLC4A1" "SLC5A6" "SMAP2" "SMS" "SNAI1" # [271] "SOX17" "SOX18" "SPIB" "SQLE" "SRPK1" "SSTR2" # [277] "STC1" "SVIL" "TAC1" "TACSTD2" "TCEAL7" "TCF15" # [283] "TCF4" "TCF7" "TCIM" "TCL1A" "TENT5C" "TFAP2A" # [289] "THAP2" "TIFA" "TIGIT" "TIMP4" "TMEM147" "TNFRSF17" # [295] "TOMM7" "TOP2A" "TPD52" "TPSAB1" "TRAC" "TRAF4" # [301] "TRAPPC3" "TRIB1" "TUBA4A" "TUBB2B" "TYROBP" "UCP1" # [307] "USP53" "VOPP1" "VWF" "WARS" "ZEB1" "ZEB2" # [313] "ZNF562" Blank Codeword (159) - (QC) Codewords that should not be used in the current panel. (named Unassigned Codeword in later Xenium pipelines) # [1] "BLANK_0006" "BLANK_0013" "BLANK_0037" "BLANK_0069" "BLANK_0072" # [6] "BLANK_0087" "BLANK_0110" "BLANK_0114" "BLANK_0120" "BLANK_0147" # [11] "BLANK_0180" "BLANK_0186" "BLANK_0272" "BLANK_0278" "BLANK_0319" # [16] "BLANK_0321" "BLANK_0337" "BLANK_0350" "BLANK_0351" "BLANK_0352" # [21] "BLANK_0353" "BLANK_0354" "BLANK_0355" "BLANK_0356" "BLANK_0357" # [26] "BLANK_0358" "BLANK_0359" "BLANK_0360" "BLANK_0361" "BLANK_0362" # [31] "BLANK_0363" "BLANK_0364" "BLANK_0365" "BLANK_0366" "BLANK_0367" # [36] "BLANK_0368" "BLANK_0369" "BLANK_0370" "BLANK_0371" "BLANK_0372" # [41] "BLANK_0373" "BLANK_0374" "BLANK_0375" "BLANK_0376" "BLANK_0377" # [46] "BLANK_0378" "BLANK_0379" "BLANK_0380" "BLANK_0381" "BLANK_0382" # [51] "BLANK_0383" "BLANK_0384" "BLANK_0385" "BLANK_0386" "BLANK_0387" # [56] "BLANK_0388" "BLANK_0389" "BLANK_0390" "BLANK_0391" "BLANK_0392" # [61] "BLANK_0393" "BLANK_0394" "BLANK_0395" "BLANK_0396" "BLANK_0397" # [66] "BLANK_0398" "BLANK_0399" "BLANK_0400" "BLANK_0401" "BLANK_0402" # [71] "BLANK_0403" "BLANK_0404" "BLANK_0405" "BLANK_0406" "BLANK_0407" # [76] "BLANK_0408" "BLANK_0409" "BLANK_0410" "BLANK_0411" "BLANK_0412" # [81] "BLANK_0413" "BLANK_0414" "BLANK_0415" "BLANK_0416" "BLANK_0417" # [86] "BLANK_0418" "BLANK_0419" "BLANK_0420" "BLANK_0421" "BLANK_0422" # [91] "BLANK_0423" "BLANK_0424" "BLANK_0425" "BLANK_0426" "BLANK_0427" # [96] "BLANK_0428" "BLANK_0429" "BLANK_0430" "BLANK_0431" "BLANK_0432" # [101] "BLANK_0433" "BLANK_0434" "BLANK_0435" "BLANK_0436" "BLANK_0437" # [106] "BLANK_0438" "BLANK_0439" "BLANK_0440" "BLANK_0441" "BLANK_0442" # [111] "BLANK_0443" "BLANK_0444" "BLANK_0445" "BLANK_0446" "BLANK_0447" # [116] "BLANK_0448" "BLANK_0449" "BLANK_0450" "BLANK_0451" "BLANK_0452" # [121] "BLANK_0453" "BLANK_0454" "BLANK_0455" "BLANK_0456" "BLANK_0457" # [126] "BLANK_0458" "BLANK_0459" "BLANK_0460" "BLANK_0461" "BLANK_0462" # [131] "BLANK_0463" "BLANK_0464" "BLANK_0465" "BLANK_0466" "BLANK_0467" # [136] "BLANK_0468" "BLANK_0469" "BLANK_0470" "BLANK_0471" "BLANK_0472" # [141] "BLANK_0473" "BLANK_0474" "BLANK_0475" "BLANK_0476" "BLANK_0477" # [146] "BLANK_0478" "BLANK_0479" "BLANK_0480" "BLANK_0481" "BLANK_0482" # [151] "BLANK_0483" "BLANK_0484" "BLANK_0485" "BLANK_0486" "BLANK_0487" # [156] "BLANK_0488" "BLANK_0489" "BLANK_0497" "BLANK_0499" Negative Control Codeword (41) - (QC) Codewords that do not map to genes, but are in the codebook. Used to determine specificity of decoding algorithm # [1] "NegControlCodeword_0500" "NegControlCodeword_0501" # [3] "NegControlCodeword_0502" "NegControlCodeword_0503" # [5] "NegControlCodeword_0504" "NegControlCodeword_0505" # [7] "NegControlCodeword_0506" "NegControlCodeword_0507" # [9] "NegControlCodeword_0508" "NegControlCodeword_0509" # [11] "NegControlCodeword_0510" "NegControlCodeword_0511" # [13] "NegControlCodeword_0512" "NegControlCodeword_0513" # [15] "NegControlCodeword_0514" "NegControlCodeword_0515" # [17] "NegControlCodeword_0516" "NegControlCodeword_0517" # [19] "NegControlCodeword_0518" "NegControlCodeword_0519" # [21] "NegControlCodeword_0520" "NegControlCodeword_0521" # [23] "NegControlCodeword_0522" "NegControlCodeword_0523" # [25] "NegControlCodeword_0524" "NegControlCodeword_0525" # [27] "NegControlCodeword_0526" "NegControlCodeword_0527" # [29] "NegControlCodeword_0528" "NegControlCodeword_0529" # [31] "NegControlCodeword_0530" "NegControlCodeword_0531" # [33] "NegControlCodeword_0532" "NegControlCodeword_0533" # [35] "NegControlCodeword_0534" "NegControlCodeword_0535" # [37] "NegControlCodeword_0536" "NegControlCodeword_0537" # [39] "NegControlCodeword_0538" "NegControlCodeword_0539" # [41] "NegControlCodeword_0540" Negative Control Probe (28) - (QC) Probes in panel but target non-biological sequences. Used to determine specificity of assay. # [1] "NegControlProbe_00042" "NegControlProbe_00041" "NegControlProbe_00039" # [4] "NegControlProbe_00035" "NegControlProbe_00034" "NegControlProbe_00033" # [7] "NegControlProbe_00031" "NegControlProbe_00025" "NegControlProbe_00024" # [10] "NegControlProbe_00022" "NegControlProbe_00019" "NegControlProbe_00017" # [13] "NegControlProbe_00016" "NegControlProbe_00014" "NegControlProbe_00013" # [16] "NegControlProbe_00012" "NegControlProbe_00009" "NegControlProbe_00004" # [19] "NegControlProbe_00003" "NegControlProbe_00002" "antisense_PROKR2" # [22] "antisense_ULK3" "antisense_SCRIB" "antisense_TRMU" # [25] "antisense_MYLIP" "antisense_LGI3" "antisense_BCL2L15" # [28] "antisense_ADCY4" The main thing to watch out for is that the other probe types should be separated out from the the Gene expression or rna feature type so that they do not interfere with expression normalization and other analyses. How to deal with these different types of detections is easily adjustable. With the feat_type param you declare which categories/feat_types you want to split transcript detections into. Then with split_keyword, you provide a list of character vectors containing grep() terms to search for. Note that there are 4 feat_types declared in this set of defaults, but 3 items passed to split_keyword. Any transcripts not matched by items in split_keyword, get categorized as the first provided feat_type (“rna”). # Example plot spatPlot2D(xenium_gobj, # scattermore speeds up plotting plot_method = "scattermore", point_shape = "no_border", point_size = 1 ) # zoomed in region with polygons and image spatInSituPlotPoints(xenium_gobj, show_image = TRUE, polygon_line_size = 0.1, polygon_color = "#BB0000", polygon_alpha = 0.2, xlim = c(1000, 2000), ylim = c(-3000, -2000) ) 3.1 Attaching Post-Xenium Aligned Images (Optional) Xenium runs will come with one or more IF morphology images. In this dataset, it is just the DAPI image. Additional images of the tissue generated after the Xenium run can be added by aligning new images to the rest of the Xenium information. These alignments are provided as affine matrices. Here we will load in the post-Xenium H&E and IF images. The images used in this process are ome.tif images which Giotto is not fully compatible with, so we convert any images we will use to normal tif images using ometif_to_tif(). This image format conversion is a step that is automatically done with the morphology images. # image conversions path_he <- "path/to/...he_image.ome.tif" path_if <- "path/to/...if_image.ome.tif" # conversion output_dir can be specified # default is a new subdirectory called tif_exports conv_path_he <- GiottoClass::ometif_to_tif(path_he) # IF staining is a 3 page .ome.tif conv_path_if <- lapply(1:3, function(p) { GiottoClass::ometif_to_tif(path_if, page = p) }) # with the following channel names # 1. CD20, 2. HER2, 3. DAPI if_channels <- GiottoClass::ometif_metadata(path_if, node = "Channel")$Name # use the importXenium() custom loading utility x <- importXenium(file.path(data_path, "outs")) img_he <- x$load_aligned_image( name = "post_he", path = conv_path_he, # "path/to/tif_exports/...he_image.tif" imagealignment_path = "path/to/...he_imagealignment.csv" ) img_if <- lapply(1:3, function(if_i) { x$load_aligned_image( name = if_channels[[if_i]], path = conv_path_if[[if_i]], # "path/to/tif_exports/...if_image.tif" imagealignment_path = "path/to/...if_imagealignment.csv" ) }) plot(img_he) plot(img_if[[2]]) # append to giotto object xenium_gobj <- setGiotto(xenium_gobj, c(list(img_he), img_if)) # example plot with HER2 spatInSituPlotPoints(xenium_gobj, xlim = c(1000, 2000), ylim = c(-3000, -2000), show_image = TRUE, image_name = "HER2", polygon_line_size = 0.1, polygon_color = "#BB0000", polygon_alpha = 0.2 ) 4 Aggregate Data # Calculate Overlaps of "rna" Features with the "cell" Polygon Boundaries xenium_gobj <- calculateOverlapRaster(xenium_gobj, spatial_info = 'cell', feat_info = 'rna' ) # Assign polygon overlaps information to expression matrix xenium_gobj <- overlapToMatrix(xenium_gobj, poly_info = 'cell', feat_info = 'rna', name = 'raw' ) showGiottoExpression(xenium_gobj) └──Spatial unit "cell" └──Feature type "rna" └──Expression data "raw" values: An object of class exprObj : "raw" spat_unit : "cell" feat_type : "rna" provenance: cell contains: 313 x 167780 sparse Matrix of class "dgCMatrix" ABCC11 . . . . . . . . . . . . . ...... ACTA2 . . . . . . . 1 . . . . 1 ...... ACTG2 . 2 . . . . 1 . . . . . 1 ...... ........suppressing 167767 columns and 307 rows in show() ZEB1 . . . . . . . . . 1 . . . ...... ZEB2 . . . . . 2 . . 2 1 1 . . ...... ZNF562 . . . . . . . . . . . . . ...... First four colnames: 1 2 3 4 This is now a fully functioning Xenium giotto object. From here, we can do the standard data processing pipeline to cluster. 5 Data Processing Now that an aggregated expression matrix is generated, the usual data filtering and processing can be applied We start by setting a count of 1 to be the minimum to consider a feature expressed. A feature must be detected in at least 3 cells to be included. Lastly, a cell must have a minimum of 5 features detected to be included. # process the data up to PCA calculation xenium_gobj <- xenium_gobj |> filterGiotto(, spat_unit = 'cell', expression_threshold = 1, feat_det_in_min_cells = 3, min_det_feats_per_cell = 5 ) |> normalizeGiotto() |> addStatistics() |> runPCA(feats_to_use = NULL) # don't use HVFs since there are too few features* *By default, runPCA() uses the subset of genes discovered to be highly variable and then assigned as such in the feature metadata. Instead, this time, using all genes is desireable, so feats_to_use will be set to NULL. # Visualize Screeplot and PCA screePlot(xenium_gobj, ncp = 20) plotPCA(xenium_gobj, point_size = 0.1) 5.1 Dimension Reduction xenium_gobj = runtSNE(xenium_gobj, dimensions_to_use = 1:10) xenium_gobj = runUMAP(xenium_gobj, dimensions_to_use = 1:10) plotTSNE(xenium_gobj, point_size = 0.01) plotUMAP(xenium_gobj, point_size = 0.01) 5.2 Clustering # create Shared Nearest Neighbors Network xenium_gobj <- xenium_gobj |> createNearestNetwork(dimensions_to_use = 1:10, k = 10) |> doLeidenCluster(resolution = 0.25, n_iterations = 100) # visualize UMAP cluster results plotTSNE(xenium_gobj, cell_color = 'leiden_clus', show_legend = FALSE, point_size = 0.01, point_shape = 'no_border' ) plotUMAP(xenium_gobj, cell_color = 'leiden_clus', show_legend = FALSE, point_size = 0.01, point_shape = 'no_border' ) 5.3 Visualize UMAP and Spatial Results # centroids plotting spatPlot2D(xenium_gobj, plot_method = "scattermore", cell_color = 'leiden_clus', point_size = 0.1, point_shape = 'no_border', background_color = 'black' ) 5.4 Subcellular Visualization # plot with polygons spatInSituPlotPoints(xenium_gobj, polygon_feat_type = 'cell', polygon_alpha = 1, polygon_line_size = 0.01, polygon_color = 'black', polygon_fill = 'leiden_clus', polygon_fill_as_factor = TRUE ) # visualize with points in a spatial subset spatInSituPlotPoints(xenium_gobj, feats = list('rna' = c( "LUM", "CXCR4", "ITGAX" )), feats_color_code = c( "LUM" = 'cyan', 'CXCR4' = 'magenta', 'ITGAX' = 'yellow' ), point_size = 0.1, xlim = c(1000, 2000), ylim = c(-3000, -2000), plot_last = "polygons", polygon_feat_type = 'cell', polygon_alpha = 0.3, polygon_line_size = 0.01, polygon_color = 'black', polygon_fill = 'leiden_clus', polygon_fill_as_factor = TRUE, show_image = TRUE, image_name = "dapi" ) 5.5 Visualize Known Markers dimFeatPlot2D(xenium_gobj, feats = c( "CD3E", "CD8A", "CD4", # T cell "JCHAIN", # plasmablast "MS4A1", # B "DCN", "SFRP2", # CAF (mesenchymal) "TAGLN", # CAF/PVL (mesenchymal) "RAMP2", # endothelial "PROX1", # LEC (lymphatic endothelial cell) "TYROBP", # myeloid "CPA3", "TPSAB1", # mast cell "SPINT2", "MKI67", "KRT8", # epithelial/cancer "SOX10", "TP63", # myoepithelial "CD24", # B cell and cancer stemness "PDCD1" # PD1 ), point_size = 0.2, point_border_stroke = 0, background_color = "black", cow_n_col = 4, show_legend = FALSE, gradient_style = "sequential", save_param = list( "base_height" = 6 ) ) These markers are not very specific to the UMAP clustering. Several genes were also not available in the panel. 6 Single Cell Assisted Cell Typing Xenium datasets have very sensitive target detection, but segmentation plays a large role in how clean the expression information is. This dataset did not come with bound staining segmentation and the 'cell' segmentation is instead a simple expansion-based approach performed on top of the 'nucleus' annotations. As a result, many transcripts may be assigned to the wrong cell. Single cell datasets lack spatial context, however they tend to have clearer separation between clusters, making it easier to see which clusters correspond to which cell types. This dataset comes with paired single cell datasets at GSE243280. We can use the single cell data to cell type, then transfer those labels to the Xenium data. More specifically, we will be using the 5 prime dataset GSM7782696. 6.1 Single Cell Processing First we need to process the single cell dataset and cell type it. single cell data processing Load Single Cell Data expr_data_path <- "path/to/5p_count_filtered_feature_bc_matrix.h5" # create single cell giotto object sc <- createGiottoObject( expression = get10Xmatrix_h5(expr_data_path)$Gene Expression, instructions = instrs ) Pre-Process: Mitochondrial Filtering fids <- featIDs(sc) mt_genes <- fids[grep("^MT-.*", fids)] sc <- addFeatsPerc(sc, expression_values = "raw", feats = mt_genes, vector_name = "mito") library(ggplot2) ggplot(spatValues(sc, feats = "mito"), aes(x = "cells", y = mito)) + geom_violin() meta <- pDataDT(sc) meta[, sum(mito > 50)] # 5006 # filter these out meta[, sum(mito < 50)] # 17963 sc <- subset(sc, subset = mito < 50) ggplot(spatValues(sc, feats = "mito"), aes(x = "cells", y = mito)) + geom_violin() Pre-Process: Remove Doublets # doublets removal sc <- doScrubletDetect(sc, return_gobject = TRUE, seed = 0) ggplot(spatValues(sc, feats = "doublet_scores"), aes(x = "cells", y = doublet_scores)) + geom_violin() sc <- subset(sc, subset = doublet == FALSE) # remove 130 cells Process Data filterCombinations(sc, expression_thresholds = 1, feat_det_in_min_cells = c(20, 100) ) sc <- sc |> filterGiotto( expression_threshold = 1, feat_det_in_min_cells = 20, min_det_feats_per_cell = 200 ) |> normalizeGiotto() |> calculateHVF() |> runPCA() screePlot(sc, ncp = 50) sc <- runUMAP(sc, dimensions_to_use = seq(30)) dimPlot2D(sc) Clustering sc <- sc |> createNearestNetwork(dimensions_to_use = seq(30)) |> doLeidenCluster(res = 1.5) # overclustering dimPlot2D(sc, cell_color = "leiden_clus", point_size = 0.6, point_border_stroke = 0 ) # combine similar clusters ann <- as.character(seq(25)) |> setNames(seq(25)) ann[c(2,3,4,5,7,8,9,10,11,12,13,14,15,16,19,24)] <- 1 ann[c(1,6,17,18,20,21,22,23,25)] <- 2:10 sc <- annotateGiotto(sc, annotation_vector = ann, cluster_column = "leiden_clus", name = "major_clus" ) dimPlot2D(sc, cell_color = "major_clus", point_size = 0.6, point_border_stroke = 0 ) Plot Known Markers dimFeatPlot2D(sc, feats = c( "CD3E", "CD8A", "CD4", # T cell "JCHAIN", # plasmablast "MS4A1", # B "DCN", "SFRP2", # CAF (mesenchymal) "TAGLN", # CAF/PVL (mesenchymal) "RAMP2", # endothelial "PROX1", # LEC (lymphatic endothelial cell) "TYROBP", # myeloid "CPA3", "TPSAB1", # mast cell "SPINT2", "MKI67", "KRT8", # epithelial/cancer "SOX10", "TP63", # myoepithelial "CD24", # B cell and cancer stemness "PDCD1" # PD1 ), point_size = 0.5, point_border_stroke = 0, background_color = "black", cow_n_col = 4, show_legend = FALSE, cell_color_gradient = c("#555555", "red"), save_param = list( "base_height" = 12 ) ) Marker Detection: Scran res_scran <- findMarkers_one_vs_all(sc, cluster_column = "major_clus", method = "scran" ) topgenes_scran = res_scran[, head(.SD, 2), by = 'cluster'] violinPlot(sc, feats = topgenes_scran$feats, cluster_column = "major_clus") Marker Detection: Gini res_gini <- findMarkers_one_vs_all(sc, cluster_column = "major_clus", method = "gini" ) topgenes_gini = res_gini[, head(.SD, 2), by = 'cluster'] violinPlot(sc, feats = topgenes_gini$feats, cluster_column = "major_clus") Annotate: Major Cell Types ann2 <- c( "cancer_epithelial", "CD4T", "CD8T", "myeloid", "endothelial", "B", "mesenchymal", "plasmablast", "myoepithelial", "LEC" ) |> setNames(seq(10)) sc <- annotateGiotto(sc, cluster_column = "major_clus", annotation_vector = ann2, name = "cell_types" ) dimPlot2D(sc, cell_color = "cell_types") 6.2 Create Joined Object Next, a joined Xenium and single cell giotto object needs to be created to transfer the cell type labels. # match features ufids <- intersect(featIDs(xenium_gobj), featIDs(sc)) # 298 feats in common # strip gobjects down to remove incompatible elements in join sc_join <- giotto() |> setGiotto(sc[[c("expression", "spatial_locs"), "raw"]]) xen_join <- giotto() |> setGiotto(xenium_gobj[[c("expression", "spatial_locs"), "raw", spat_unit = "cell"]]) j <- joinGiottoObjects( list(sc_join[ufids], xen_join[ufids]), gobject_names = c("sc", "xen") ) instructions(j) <- instrs 6.3 Process Joined Object (No Integration) j <- filterGiotto(j, expression_threshold = 1, feat_det_in_min_cells = 1, min_det_feats_per_cell = 10 ) # normalizations j <- normalizeGiotto(j) # pearson and quantile norms do not work well for this # PCA j <- runPCA(j, feats_to_use = NULL) plotPCA(j, cell_color = "list_ID", point_border_stroke = 0, point_size = 0.3 ) screePlot(j, ncp = 50) ndims <- 10 j <- runUMAP(j, dimensions_to_use = seq(ndims)) plotUMAP(j, cell_color = "list_ID", point_size = 0.3, point_border_stroke = 0, cell_color_code = c("red", "blue") ) There are clear batch effects between the two types of data, so we can try Harmony integration. 6.4 Process Joined Object (With Harmony Integration) Run Harmony integration on the PCA space with the "list_ID" (dataset source) as the variable to remove to produce an aligned PCA space that better represents the biology of the datasets. j <- runGiottoHarmony(j, vars_use = "list_ID", dim_reduction_name = "pca", dimensions_to_use = seq(ndims), name = "harmony" ) j <- runUMAP(j, dim_reduction_to_use = "harmony", dim_reduction_name = "harmony", name = "umap_harmony", dimensions_to_use = seq(ndims) ) cell_colors <- c( "turquoise", "azure4", "pink", "tomato", "chartreuse3", "lightslateblue", "firebrick4", "blue", "magenta", "cornflowerblue" ) plotUMAP(j, cell_color = "cell_types", dim_reduction_name = "umap_harmony", cell_color_code = cell_colors, point_size = 0.5, select_cells = spatIDs(j, subset = list_ID == "sc"), point_border_stroke = 0, other_point_size = 0.3 ) The single-cell-based celltype annotations look like they map to the clustering of the integrated dataset. We can now transfer these annotations using labelTransfer() from just the single cells (source_cell_ids) to the rest of the cells (xenium data cells) using kNN clustering. j <- labelTransfer(j, source_cell_ids = spatIDs(j, subset = list_ID == "sc"), k = 10, labels = "cell_types", reduction_method = "harmony", reduction_name = "harmony", dimensions_to_use = seq(ndims) ) plotUMAP(j, cell_color = "trnsfr_cell_types", dim_reduction_name = "umap_harmony", cell_color_code = cell_colors, point_size = 0.3, point_border_stroke = 0 ) ann_meta <- pDataDT(j) ann_meta <- ann_meta[list_ID == "xen",] ann_meta[, cell_ID := gsub("^xen-", "", cell_ID)] ann_meta <- ann_meta[, .(cell_ID, trnsfr_cell_types, trnsfr_cell_types_prob)] xenium_gobj <- addCellMetadata(xenium_gobj, new_metadata = ann_meta, by_column = TRUE, column_cell_ID = "cell_ID" ) spatInSituPlotPoints(xenium_gobj, polygon_fill = "trnsfr_cell_types", polygon_fill_as_factor = TRUE, polygon_line_size = 0, polygon_fill_code = cell_colors ) res <- labelTransfer(xenium_gobj, sc, labels = "cell_types", integration_method = "harmony", k = 10, dimensions_to_use = seq(ndims) ) sessionInfo() R version 4.4.0 (2024-04-24) Platform: x86_64-pc-linux-gnu Running under: AlmaLinux 8.10 (Cerulean Leopard) Matrix products: default BLAS/LAPACK: FlexiBLAS NETLIB; LAPACK version 3.11.0 locale: [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C LC_TIME=en_US.UTF-8 [4] LC_COLLATE=en_US.UTF-8 LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8 [7] LC_PAPER=en_US.UTF-8 LC_NAME=C LC_ADDRESS=C [10] LC_TELEPHONE=C LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C time zone: America/New_York tzcode source: system (glibc) attached base packages: [1] stats graphics grDevices utils datasets methods base other attached packages: [1] ggplot2_3.5.1 Giotto_4.1.5 GiottoClass_0.4.4 loaded via a namespace (and not attached): [1] RColorBrewer_1.1-3 rstudioapi_0.16.0 jsonlite_1.8.8 [4] magrittr_2.0.3 magick_2.8.4 farver_2.1.2 [7] rmarkdown_2.28 zlibbioc_1.50.0 ragg_1.3.0 [10] vctrs_0.6.5 DelayedMatrixStats_1.26.0 GiottoUtils_0.2.1 [13] terra_1.7-78 htmltools_0.5.8.1 S4Arrays_1.4.1 [16] BiocNeighbors_1.22.0 tictoc_1.2.1 Rhdf5lib_1.26.0 [19] SparseArray_1.4.8 rhdf5_2.48.0 parallelly_1.37.1 [22] htmlwidgets_1.6.4 plotly_4.10.4 igraph_2.0.3 [25] lifecycle_1.0.4 pkgconfig_2.0.3 rsvd_1.0.5 [28] Matrix_1.7-0 R6_2.5.1 fastmap_1.2.0 [31] GenomeInfoDbData_1.2.12 MatrixGenerics_1.16.0 future_1.33.2 [34] digest_0.6.37 colorspace_2.1-1 S4Vectors_0.42.1 [37] dqrng_0.4.1 irlba_2.3.5.1 textshaping_0.3.7 [40] GenomicRanges_1.56.0 beachmat_2.20.0 labeling_0.4.3 [43] progressr_0.14.0 fansi_1.0.6 httr_1.4.7 [46] abind_1.4-5 compiler_4.4.0 bit64_4.0.5 [49] withr_3.0.1 backports_1.5.0 BiocParallel_1.38.0 [52] DBI_1.2.3 qs_0.26.3 pak_0.7.2 [55] R.utils_2.12.3 duckdb_1.0.0-2 rappdirs_0.3.3 [58] DelayedArray_0.30.1 bluster_1.14.0 rjson_0.2.21 [61] gtools_3.9.5 GiottoVisuals_0.2.9 tools_4.4.0 [64] future.apply_1.11.2 R.oo_1.26.0 glue_1.7.0 [67] dbscan_1.2-0 rhdf5filters_1.16.0 grid_4.4.0 [70] job_0.3.1 checkmate_2.3.2 cluster_2.1.6 [73] generics_0.1.3 hdf5r_1.3.11 gtable_0.3.5 [76] R.methodsS3_1.8.2 tidyr_1.3.1 data.table_1.16.0 [79] RApiSerialize_0.1.3 metapod_1.12.0 BiocSingular_1.20.0 [82] ScaledMatrix_1.12.0 stringfish_0.16.0 utf8_1.2.4 [85] XVector_0.44.0 BiocGenerics_0.50.0 RcppAnnoy_0.0.22 [88] ggrepel_0.9.6 pillar_1.9.0 limma_3.60.2 [91] later_1.3.2 dplyr_1.1.4 lattice_0.22-6 [94] FNN_1.1.4 bit_4.0.5 pool_1.0.3 [97] tidyselect_1.2.1 locfit_1.5-9.10 SingleCellExperiment_1.26.0 [100] scuttle_1.14.0 knitr_1.48 IRanges_2.38.0 [103] edgeR_4.2.0 SummarizedExperiment_1.34.0 scattermore_1.2 [106] RhpcBLASctl_0.23-42 stats4_4.4.0 xfun_0.47 [109] Biobase_2.64.0 statmod_1.5.0 matrixStats_1.4.1 [112] UCSC.utils_1.0.0 lazyeval_0.2.2 yaml_2.3.10 [115] evaluate_0.24.0 codetools_0.2-20 GiottoData_0.2.14 [118] tibble_3.2.1 colorRamp2_0.1.0 cli_3.6.3 [121] uwot_0.2.2 RcppParallel_5.1.7 arrow_16.1.0 [124] reticulate_1.38.0 systemfonts_1.1.0 munsell_0.5.1 [127] harmony_1.2.0 Rcpp_1.0.13 GenomeInfoDb_1.40.0 [130] globals_0.16.3 png_0.1-8 parallel_4.4.0 [133] assertthat_0.2.1 scran_1.32.0 sparseMatrixStats_1.16.0 [136] listenv_0.9.1 SpatialExperiment_1.14.0 viridisLite_0.4.2 [139] scales_1.3.0 purrr_1.0.2 crayon_1.5.2 [142] rlang_1.1.4 cowplot_1.1.3 Contents

vignettes/xenium_breast_cancer.rmd

Pattern 2: 3 Load Xenium Data The Xenium data can be loaded using createGiottoXeniumObject() The default behavior is to load: transcripts information cell and nucleus boundaries morphology focus images (DAPI and IF cell boundary stains if any) feature metadata (gene_panel.json) Alternative data to load We skip loading of: expression cell metadata They can be loaded if load_expression and load_cellmeta respectively are set to TRUE. We normally skip them since Giotto’s aggregation results may produce slightly different results than those from 10X. The molecule transcript detections can also be skipped if directly using the 10X expression information alongside the polygons. You can do this by setting load_transcripts to FALSE. It will not be possible to plot the individual transcript detections if they are not loaded in, but memory usage is greatly reduced. As an additional note, the provided expression values are generated from a QV threshold of 20 (described below) with the cell polygons. If a different QV threshold or usage of the nuclear segmentations is desired, then de novo aggregation from polygons and points will be required. Expected peak RAM usage: with transcripts: ~70GB without transcripts: ~5GB For the full dataset (HPC): time: 5-6min | memory: 50GB # 1. ** SET PATH TO FOLDER CONTAINING XENIUM DATA ** data_path <- "path/to/data" # 2. ** SET WORKING DIRECTORY WHERE PROJECT OUTPUTS WILL SAVE TO ** results_folder = '/path/to/results/' # 3. Create Giotto instructions # Directly saving plots to the working directory without rendering them in the viewer saves time. instrs = createGiottoInstructions( save_dir = results_folder, save_plot = TRUE, show_plot = FALSE, return_plot = FALSE, python_path = python_path ) # These feat_type and split_keyword settings are specific to # pre-release and early versions of the Xenium pipeline feat_types <- c( "rna", "UnassignedCodeword", "NegControlCodeword", "NegControlProbe" ) split_keywords = list( c("BLANK"), c("NegControlCodeword"), c("NegControlProbe", "antisense") ) # 4. Create the object xenium_gobj <- createGiottoXeniumObject( xenium_dir = file.path(data_path, "outs"), qv_threshold = 20, # qv of 20 is the default and also what 10x uses feat_type = feat_types, split_keyword = split_keywords, # * if aligned images already converted to .tif, they could be added as named list # * instead, see next section # load_aligned_images = list( # post_he = c( # "path/to/...he_image.tif", # "path/to/...he_imagealignment.csv" # ), # CD20 = ..., # HER2 = ..., # DAPI = ... # ), instructions = instrs ) force(xenium_gobj) An object of class giotto >Active spat_unit: cell >Active feat_type: rna dimensions : 313, 167780 (features, cells) [SUBCELLULAR INFO] polygons : cell nucleus features : rna blank_codeword neg_control_codeword neg_control_probe [AGGREGATE INFO] spatial locations ---------------- [cell] raw [nucleus] raw attached images ------------------ images : dapi Use objHistory() to see steps and params used There are several parameters for additional or alternative items you can load. See dropdowns. Loading from non-standard directories or other provided file formats The convenience function auto-detects filepaths based on the Xenium directory path and the preferred file formats .parquet for tabular (vs .csv) .h5 for matrix over other formats when available (vs .mtx) .zarr is currently not supported. When you need to use a different file format or something is not in the standard Xenium output directory structure or naming scheme shown above, you can supply a specific filepath to createGiottoXeniumObject() using these parameters: expression_path = , cell_metadata_path = , transcript_path = , bounds_path = , gene_panel_json_path = , Note that if loading in the .mtx file, expression_path param should be passed the filepath to the cell_feature_matrix subdirectory instead of the .mtx file. qv_threshold setting qv_threshold = 20 # default The Quality Value is a Phred-based 0-40 value that 10X provides for every detection in their transcripts output. Higher values mean higher confidence in the decoded transcript identity. By default 10X uses a cutoff of QV = 20 for transcripts to use downstream. Setting a value other than 20 will make the loaded dataset different from the 10X-provided expression matrix and cell metadata. QV Calculation Raw Q-score based on how likely it is that an observed code is to be the codeword that it gets mapped to vs less likely codeword. Adjustment of raw Q-score by binning the transcripts by Q-value then adjusting the exact Q per bin based on proportion of Negative Control Codewords detected within. further info from 10x documentation feat_types and split_keywords These parameters govern how transcript types are split into different groups when loading. feat_types <- c( "rna", "UnassignedCodeword", "NegControlCodeword", "NegControlProbe" ) split_keywords = list( c("BLANK"), c("NegControlCodeword"), c("NegControlProbe", "antisense") ) There are 4 types of transcript detections that 10X reports with this dataset. Gene expression (313) - These are the 'rna' gene detections. rna <- xenium_gobj[["feat_info", "rna"]][[1]] plot(rna, dens = TRUE) plot of Gene expression (rna) density # [1] "ABCC11" "ACTA2" "ACTG2" "ADAM9" "ADGRE5" "ADH1B" # [7] "ADIPOQ" "AGR3" "AHSP" "AIF1" "AKR1C1" "AKR1C3" # [13] "ALDH1A3" "ANGPT2" "ANKRD28" "ANKRD29" "ANKRD30A" "APOBEC3A" # [19] "APOBEC3B" "APOC1" "AQP1" "AQP3" "AR" "AVPR1A" # [25] "BACE2" "BANK1" "BASP1" "BTNL9" "C15orf48" "C1QA" # [31] "C1QC" "C2orf42" "C5orf46" "C6orf132" "CAV1" "CAVIN2" # [37] "CCDC6" "CCDC80" "CCL20" "CCL5" "CCL8" "CCND1" # [43] "CCPG1" "CCR7" "CD14" "CD163" "CD19" "CD1C" # [49] "CD247" "CD27" "CD274" "CD3D" "CD3E" "CD3G" # [55] "CD4" "CD68" "CD69" "CD79A" "CD79B" "CD80" # [61] "CD83" "CD86" "CD8A" "CD8B" "CD9" "CD93" # [67] "CDC42EP1" "CDH1" "CEACAM6" "CEACAM8" "CENPF" "CLCA2" # [73] "CLDN4" "CLDN5" "CLEC14A" "CLEC9A" "CLECL1" "CLIC6" # [79] "CPA3" "CRHBP" "CRISPLD2" "CSF3" "CTH" "CTLA4" # [85] "CTSG" "CTTN" "CX3CR1" "CXCL12" "CXCL16" "CXCL5" # [91] "CXCR4" "CYP1A1" "CYTIP" "DAPK3" "DERL3" "DMKN" # [97] "DNAAF1" "DNTTIP1" "DPT" "DSC2" "DSP" "DST" # [103] "DUSP2" "DUSP5" "EDN1" "EDNRB" "EGFL7" "EGFR" # [109] "EIF4EBP1" "ELF3" "ELF5" "ENAH" "EPCAM" "ERBB2" # [115] "ERN1" "ESM1" "ESR1" "FAM107B" "FAM49A" "FASN" # [121] "FBLIM1" "FBLN1" "FCER1A" "FCER1G" "FCGR3A" "FGL2" # [127] "FLNB" "FOXA1" "FOXC2" "FOXP3" "FSTL3" "GATA3" # [133] "GJB2" "GLIPR1" "GNLY" "GPR183" "GZMA" "GZMB" # [139] "GZMK" "HAVCR2" "HDC" "HMGA1" "HOOK2" "HOXD8" # [145] "HOXD9" "HPX" "IGF1" "IGSF6" "IL2RA" "IL2RG" # [151] "IL3RA" "IL7R" "ITGAM" "ITGAX" "ITM2C" "JUP" # [157] "KARS" "KDR" "KIT" "KLF5" "KLRB1" "KLRC1" # [163] "KLRD1" "KLRF1" "KRT14" "KRT15" "KRT16" "KRT23" # [169] "KRT5" "KRT6B" "KRT7" "KRT8" "LAG3" "LARS" # [175] "LDHB" "LEP" "LGALSL" "LIF" "LILRA4" "LPL" # [181] "LPXN" "LRRC15" "LTB" "LUM" "LY86" "LYPD3" # [187] "LYZ" "MAP3K8" "MDM2" "MEDAG" "MKI67" "MLPH" # [193] "MMP1" "MMP12" "MMP2" "MMRN2" "MNDA" "MPO" # [199] "MRC1" "MS4A1" "MUC6" "MYBPC1" "MYH11" "MYLK" # [205] "MYO5B" "MZB1" "NARS" "NCAM1" "NDUFA4L2" "NKG7" # [211] "NOSTRIN" "NPM3" "OCIAD2" "OPRPN" "OXTR" "PCLAF" # [217] "PCOLCE" "PDCD1" "PDCD1LG2" "PDE4A" "PDGFRA" "PDGFRB" # [223] "PDK4" "PECAM1" "PELI1" "PGR" "PIGR" "PIM1" # [229] "PLD4" "POLR2J3" "POSTN" "PPARG" "PRDM1" "PRF1" # [235] "PTGDS" "PTN" "PTPRC" "PTRHD1" "QARS" "RAB30" # [241] "RAMP2" "RAPGEF3" "REXO4" "RHOH" "RORC" "RTKN2" # [247] "RUNX1" "S100A14" "S100A4" "S100A8" "SCD" "SCGB2A1" # [253] "SDC4" "SEC11C" "SEC24A" "SELL" "SERHL2" "SERPINA3" # [259] "SERPINB9" "SFRP1" "SFRP4" "SH3YL1" "SLAMF1" "SLAMF7" # [265] "SLC25A37" "SLC4A1" "SLC5A6" "SMAP2" "SMS" "SNAI1" # [271] "SOX17" "SOX18" "SPIB" "SQLE" "SRPK1" "SSTR2" # [277] "STC1" "SVIL" "TAC1" "TACSTD2" "TCEAL7" "TCF15" # [283] "TCF4" "TCF7" "TCIM" "TCL1A" "TENT5C" "TFAP2A" # [289] "THAP2" "TIFA" "TIGIT" "TIMP4" "TMEM147" "TNFRSF17" # [295] "TOMM7" "TOP2A" "TPD52" "TPSAB1" "TRAC" "TRAF4" # [301] "TRAPPC3" "TRIB1" "TUBA4A" "TUBB2B" "TYROBP" "UCP1" # [307] "USP53" "VOPP1" "VWF" "WARS" "ZEB1" "ZEB2" # [313] "ZNF562" Blank Codeword (159) - (QC) Codewords that should not be used in the current panel. (named Unassigned Codeword in later Xenium pipelines) # [1] "BLANK_0006" "BLANK_0013" "BLANK_0037" "BLANK_0069" "BLANK_0072" # [6] "BLANK_0087" "BLANK_0110" "BLANK_0114" "BLANK_0120" "BLANK_0147" # [11] "BLANK_0180" "BLANK_0186" "BLANK_0272" "BLANK_0278" "BLANK_0319" # [16] "BLANK_0321" "BLANK_0337" "BLANK_0350" "BLANK_0351" "BLANK_0352" # [21] "BLANK_0353" "BLANK_0354" "BLANK_0355" "BLANK_0356" "BLANK_0357" # [26] "BLANK_0358" "BLANK_0359" "BLANK_0360" "BLANK_0361" "BLANK_0362" # [31] "BLANK_0363" "BLANK_0364" "BLANK_0365" "BLANK_0366" "BLANK_0367" # [36] "BLANK_0368" "BLANK_0369" "BLANK_0370" "BLANK_0371" "BLANK_0372" # [41] "BLANK_0373" "BLANK_0374" "BLANK_0375" "BLANK_0376" "BLANK_0377" # [46] "BLANK_0378" "BLANK_0379" "BLANK_0380" "BLANK_0381" "BLANK_0382" # [51] "BLANK_0383" "BLANK_0384" "BLANK_0385" "BLANK_0386" "BLANK_0387" # [56] "BLANK_0388" "BLANK_0389" "BLANK_0390" "BLANK_0391" "BLANK_0392" # [61] "BLANK_0393" "BLANK_0394" "BLANK_0395" "BLANK_0396" "BLANK_0397" # [66] "BLANK_0398" "BLANK_0399" "BLANK_0400" "BLANK_0401" "BLANK_0402" # [71] "BLANK_0403" "BLANK_0404" "BLANK_0405" "BLANK_0406" "BLANK_0407" # [76] "BLANK_0408" "BLANK_0409" "BLANK_0410" "BLANK_0411" "BLANK_0412" # [81] "BLANK_0413" "BLANK_0414" "BLANK_0415" "BLANK_0416" "BLANK_0417" # [86] "BLANK_0418" "BLANK_0419" "BLANK_0420" "BLANK_0421" "BLANK_0422" # [91] "BLANK_0423" "BLANK_0424" "BLANK_0425" "BLANK_0426" "BLANK_0427" # [96] "BLANK_0428" "BLANK_0429" "BLANK_0430" "BLANK_0431" "BLANK_0432" # [101] "BLANK_0433" "BLANK_0434" "BLANK_0435" "BLANK_0436" "BLANK_0437" # [106] "BLANK_0438" "BLANK_0439" "BLANK_0440" "BLANK_0441" "BLANK_0442" # [111] "BLANK_0443" "BLANK_0444" "BLANK_0445" "BLANK_0446" "BLANK_0447" # [116] "BLANK_0448" "BLANK_0449" "BLANK_0450" "BLANK_0451" "BLANK_0452" # [121] "BLANK_0453" "BLANK_0454" "BLANK_0455" "BLANK_0456" "BLANK_0457" # [126] "BLANK_0458" "BLANK_0459" "BLANK_0460" "BLANK_0461" "BLANK_0462" # [131] "BLANK_0463" "BLANK_0464" "BLANK_0465" "BLANK_0466" "BLANK_0467" # [136] "BLANK_0468" "BLANK_0469" "BLANK_0470" "BLANK_0471" "BLANK_0472" # [141] "BLANK_0473" "BLANK_0474" "BLANK_0475" "BLANK_0476" "BLANK_0477" # [146] "BLANK_0478" "BLANK_0479" "BLANK_0480" "BLANK_0481" "BLANK_0482" # [151] "BLANK_0483" "BLANK_0484" "BLANK_0485" "BLANK_0486" "BLANK_0487" # [156] "BLANK_0488" "BLANK_0489" "BLANK_0497" "BLANK_0499" Negative Control Codeword (41) - (QC) Codewords that do not map to genes, but are in the codebook. Used to determine specificity of decoding algorithm # [1] "NegControlCodeword_0500" "NegControlCodeword_0501" # [3] "NegControlCodeword_0502" "NegControlCodeword_0503" # [5] "NegControlCodeword_0504" "NegControlCodeword_0505" # [7] "NegControlCodeword_0506" "NegControlCodeword_0507" # [9] "NegControlCodeword_0508" "NegControlCodeword_0509" # [11] "NegControlCodeword_0510" "NegControlCodeword_0511" # [13] "NegControlCodeword_0512" "NegControlCodeword_0513" # [15] "NegControlCodeword_0514" "NegControlCodeword_0515" # [17] "NegControlCodeword_0516" "NegControlCodeword_0517" # [19] "NegControlCodeword_0518" "NegControlCodeword_0519" # [21] "NegControlCodeword_0520" "NegControlCodeword_0521" # [23] "NegControlCodeword_0522" "NegControlCodeword_0523" # [25] "NegControlCodeword_0524" "NegControlCodeword_0525" # [27] "NegControlCodeword_0526" "NegControlCodeword_0527" # [29] "NegControlCodeword_0528" "NegControlCodeword_0529" # [31] "NegControlCodeword_0530" "NegControlCodeword_0531" # [33] "NegControlCodeword_0532" "NegControlCodeword_0533" # [35] "NegControlCodeword_0534" "NegControlCodeword_0535" # [37] "NegControlCodeword_0536" "NegControlCodeword_0537" # [39] "NegControlCodeword_0538" "NegControlCodeword_0539" # [41] "NegControlCodeword_0540" Negative Control Probe (28) - (QC) Probes in panel but target non-biological sequences. Used to determine specificity of assay. # [1] "NegControlProbe_00042" "NegControlProbe_00041" "NegControlProbe_00039" # [4] "NegControlProbe_00035" "NegControlProbe_00034" "NegControlProbe_00033" # [7] "NegControlProbe_00031" "NegControlProbe_00025" "NegControlProbe_00024" # [10] "NegControlProbe_00022" "NegControlProbe_00019" "NegControlProbe_00017" # [13] "NegControlProbe_00016" "NegControlProbe_00014" "NegControlProbe_00013" # [16] "NegControlProbe_00012" "NegControlProbe_00009" "NegControlProbe_00004" # [19] "NegControlProbe_00003" "NegControlProbe_00002" "antisense_PROKR2" # [22] "antisense_ULK3" "antisense_SCRIB" "antisense_TRMU" # [25] "antisense_MYLIP" "antisense_LGI3" "antisense_BCL2L15" # [28] "antisense_ADCY4" The main thing to watch out for is that the other probe types should be separated out from the the Gene expression or rna feature type so that they do not interfere with expression normalization and other analyses. How to deal with these different types of detections is easily adjustable. With the feat_type param you declare which categories/feat_types you want to split transcript detections into. Then with split_keyword, you provide a list of character vectors containing grep() terms to search for. Note that there are 4 feat_types declared in this set of defaults, but 3 items passed to split_keyword. Any transcripts not matched by items in split_keyword, get categorized as the first provided feat_type (“rna”). # Example plot spatPlot2D(xenium_gobj, # scattermore speeds up plotting plot_method = "scattermore", point_shape = "no_border", point_size = 1 ) # zoomed in region with polygons and image spatInSituPlotPoints(xenium_gobj, show_image = TRUE, polygon_line_size = 0.1, polygon_color = "#BB0000", polygon_alpha = 0.2, xlim = c(1000, 2000), ylim = c(-3000, -2000) ) 3.1 Attaching Post-Xenium Aligned Images (Optional) Xenium runs will come with one or more IF morphology images. In this dataset, it is just the DAPI image. Additional images of the tissue generated after the Xenium run can be added by aligning new images to the rest of the Xenium information. These alignments are provided as affine matrices. Here we will load in the post-Xenium H&E and IF images. The images used in this process are ome.tif images which Giotto is not fully compatible with, so we convert any images we will use to normal tif images using ometif_to_tif(). This image format conversion is a step that is automatically done with the morphology images. # image conversions path_he <- "path/to/...he_image.ome.tif" path_if <- "path/to/...if_image.ome.tif" # conversion output_dir can be specified # default is a new subdirectory called tif_exports conv_path_he <- GiottoClass::ometif_to_tif(path_he) # IF staining is a 3 page .ome.tif conv_path_if <- lapply(1:3, function(p) { GiottoClass::ometif_to_tif(path_if, page = p) }) # with the following channel names # 1. CD20, 2. HER2, 3. DAPI if_channels <- GiottoClass::ometif_metadata(path_if, node = "Channel")$Name # use the importXenium() custom loading utility x <- importXenium(file.path(data_path, "outs")) img_he <- x$load_aligned_image( name = "post_he", path = conv_path_he, # "path/to/tif_exports/...he_image.tif" imagealignment_path = "path/to/...he_imagealignment.csv" ) img_if <- lapply(1:3, function(if_i) { x$load_aligned_image( name = if_channels[[if_i]], path = conv_path_if[[if_i]], # "path/to/tif_exports/...if_image.tif" imagealignment_path = "path/to/...if_imagealignment.csv" ) }) plot(img_he) plot(img_if[[2]]) # append to giotto object xenium_gobj <- setGiotto(xenium_gobj, c(list(img_he), img_if)) # example plot with HER2 spatInSituPlotPoints(xenium_gobj, xlim = c(1000, 2000), ylim = c(-3000, -2000), show_image = TRUE, image_name = "HER2", polygon_line_size = 0.1, polygon_color = "#BB0000", polygon_alpha = 0.2 )

createGiottoXeniumObject()

Pattern 3: Frequently Asked Questions Source: vignettes/faqs.Rmd faqs.Rmd For troubleshooting help, see this page Installation How do I install an R package for the first time? Can I install Python components manually? Usage What does a Giotto workflow look like? How do I create a Giotto analysis object? Is there low-level tooling for importing from popular spatial technologies? What are Giotto instructions? How do I calculate a raw expression matrix from raw features and segementations? How do I set up spatial bins to aggregate with? Is there a convenient workflow function for data processing? How can I build a composable pipeline for processing my data? What are spatial units and feature types? What are important Giotto functions to know? How do I spatially manipulate my data? How do I extract data out of the Giotto Object? How do I interactively select my data? What is the structure of the Giotto Object? How do I register spatial datasets together? Are there any tools for TMA data? How do I perform data filtering? How do I set up a spatial or nearest neighbor network? How do I find variable features? How do I perform dimension reduction? How can I detect spatially organized genes? How do I cluster features into modules with similar spatial expression patterns? How can I check for enrichment of gene/feature signatures? The biology I study is highly spatially organized. Can I improve my clustering? What can I find differentially expressed features? How do I use Harmony with Giotto to remove batch effects? How do I use HMRF? How do I deconvolve my data? How do I perform kriging to super-resolve my Visium data? How do I save my object? How do I save/export my plots? Is there a docker image? Can I use the docker image with singularity? How do I contribute to Giotto? How do I report a bug? Data Availability Where can I find seqFISH+ and other ready-to-use datasets? Checkout our GiottoData extension package to find already preprocessed datasets and Giotto mini Objects. Where else can I find more spatial datasets? Checkout the following for more spatial-omics data: Aquila Tencent's SODB PySODB, a python interface for the SODB How can I automatically download tutorial datasets? Use getSpatialDataset() from GiottoData: # Ensure Giotto Suite is installed if(!"Giotto" %in% installed.packages()) { pak::pkg_install("drieslab/Giotto") } library(Giotto) # Ensure Giotto Data is installed if(!"GiottoData" %in% installed.packages()) { pak::pkg_install("drieslab/GiottoData") } library(GiottoData) # choose your directory my_working_dir = getwd() # merFISH example: # standard download data to working directory getSpatialDataset(dataset = 'merfish_preoptic', directory = my_working_dir) # use wget to download data to working directory (much faster) getSpatialDataset(dataset = 'merfish_preoptic', directory = my_working_dir, method = 'wget') # avoid certification issues with wget getSpatialDataset(dataset = 'merfish_preoptic', directory = my_working_dir, method = 'wget', extra = '--no-check-certificate') Contents

vignettes/faqs.Rmd

Pattern 4: Data Availability Where can I find seqFISH+ and other ready-to-use datasets? Checkout our GiottoData extension package to find already preprocessed datasets and Giotto mini Objects. Where else can I find more spatial datasets? Checkout the following for more spatial-omics data: Aquila Tencent's SODB PySODB, a python interface for the SODB How can I automatically download tutorial datasets? Use getSpatialDataset() from GiottoData: # Ensure Giotto Suite is installed if(!"Giotto" %in% installed.packages()) { pak::pkg_install("drieslab/Giotto") } library(Giotto) # Ensure Giotto Data is installed if(!"GiottoData" %in% installed.packages()) { pak::pkg_install("drieslab/GiottoData") } library(GiottoData) # choose your directory my_working_dir = getwd() # merFISH example: # standard download data to working directory getSpatialDataset(dataset = 'merfish_preoptic', directory = my_working_dir) # use wget to download data to working directory (much faster) getSpatialDataset(dataset = 'merfish_preoptic', directory = my_working_dir, method = 'wget') # avoid certification issues with wget getSpatialDataset(dataset = 'merfish_preoptic', directory = my_working_dir, method = 'wget', extra = '--no-check-certificate')

getSpatialDataset()

Pattern 5: Cross section Source: vignettes/cross_section.Rmd cross_section.Rmd Cross sections in {Giotto} are used to generate an in-silico cross section of a 3D dataset along an arbitrary 2D plane. A plane may be defined in one of four ways: "equation" - Provide a vector of 4 numerics to equation. These will be taken as the A, B, C, D coefficients in the plane-defining function Ax + By + Cz = D This method is shown below. "3 points" - Provide 3D coordinates to point1, point2, point3 params. For example: method = "3 points", point1 = c(100, 200, 300), point2 = c(400, 200, 300), point3 = c(100, 500, 700) "point and norm vector" - Provide a 3D point anchor, then a 3D vector normal to the plane to set the plane rotation. For example: method = "point and norm vector", point1 = c(100, 200, 300), normVector = c(0, 1, 0) # Normal points in y direction (horizontal plane) "point and two plane vectors" - Provide a 3D point anchor and two non-parallel 3D vectors that define the plane rotation. For example: method = "point and two plane vectors", point1 = c(100, 200, 300), planeVector1 = c(1, 0, 0), # Vector along x-axis planeVector2 = c(0, 0, 1) # Vector along z-axis 1 Setup and load example dataset For the example, we use a mini premade giotto object. This was mini dataset is a randomly sampled subset of a STARmap mouse brain dataset. # Ensure Giotto Suite is installed if(!"Giotto" %in% installed.packages()) { pak::pkg_install("drieslab/Giotto") } # Ensure Giotto Data is installed if(!"GiottoData" %in% installed.packages()) { pak::pkg_install("drieslab/GiottoData") } library(Giotto) # load the object g <- GiottoData::loadGiottoMini("starmap") 2 Create Cross Section Next we define the cross section to create using the "equation" method. g <- createCrossSection(g, method = "equation", equation = c(0, 1, 0, 600), extend_ratio = 0.6, name = "new_cs", return_gobject = TRUE ) 3 Plot cross section This cross section can then be used in cross section-specific plotting functions. 3.1 2D crossSectionPlot(g, name = "new_cs") 3.2 3D crossSectionPlot3D(g, name = "new_cs") 4 Plot cross section feature plot 4.1 2D crossSectionFeatPlot(g, feats = c("Mbp", "Flt1"), name = "new_cs", cow_n_col = 1) 4.2 3D crossSectionFeatPlot3D(g, feats = "Mbp", name = "new_cs", cow_n_col = 1) 5 Session Info sessionInfo() R version 4.4.2 (2024-10-31) Platform: x86_64-apple-darwin20 Running under: macOS Sequoia 15.0.1 Matrix products: default BLAS: /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib LAPACK: /Library/Frameworks/R.framework/Versions/4.4-x86_64/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] Giotto_4.1.4 GiottoClass_0.4.3 loaded via a namespace (and not attached): [1] tidyselect_1.2.1 viridisLite_0.4.2 [3] farver_2.1.2 dplyr_1.1.4 [5] GiottoVisuals_0.2.7 fastmap_1.2.0 [7] SingleCellExperiment_1.26.0 lazyeval_0.2.2 [9] digest_0.6.37 lifecycle_1.0.4 [11] terra_1.7-78 magrittr_2.0.3 [13] compiler_4.4.2 rlang_1.1.4 [15] tools_4.4.2 igraph_2.0.3 [17] utf8_1.2.4 yaml_2.3.10 [19] data.table_1.16.0 knitr_1.48 [21] labeling_0.4.3 S4Arrays_1.4.1 [23] htmlwidgets_1.6.4 reticulate_1.39.0 [25] DelayedArray_0.30.1 abind_1.4-8 [27] withr_3.0.1 purrr_1.0.2 [29] BiocGenerics_0.50.0 grid_4.4.2 [31] stats4_4.4.2 fansi_1.0.6 [33] colorspace_2.1-1 ggplot2_3.5.1 [35] scales_1.3.0 gtools_3.9.5 [37] SummarizedExperiment_1.34.0 cli_3.6.3 [39] rmarkdown_2.28 crayon_1.5.3 [41] ragg_1.3.3 generics_0.1.3 [43] rstudioapi_0.16.0 httr_1.4.7 [45] rjson_0.2.23 zlibbioc_1.50.0 [47] parallel_4.4.2 XVector_0.44.0 [49] matrixStats_1.4.1 vctrs_0.6.5 [51] Matrix_1.7-1 jsonlite_1.8.9 [53] GiottoData_0.2.15 IRanges_2.38.1 [55] S4Vectors_0.42.1 ggrepel_0.9.6 [57] scattermore_1.2 crosstalk_1.2.1 [59] systemfonts_1.1.0 magick_2.8.5 [61] GiottoUtils_0.2.0 plotly_4.10.4 [63] tidyr_1.3.1 glue_1.8.0 [65] codetools_0.2-20 cowplot_1.1.3 [67] gtable_0.3.5 GenomeInfoDb_1.40.1 [69] GenomicRanges_1.56.1 UCSC.utils_1.0.0 [71] munsell_0.5.1 tibble_3.2.1 [73] pillar_1.9.0 htmltools_0.5.8.1 [75] GenomeInfoDbData_1.2.12 R6_2.5.1 [77] textshaping_0.4.0 evaluate_1.0.0 [79] lattice_0.22-6 Biobase_2.64.0 [81] png_0.1-8 backports_1.5.0 [83] SpatialExperiment_1.14.0 Rcpp_1.0.13 [85] SparseArray_1.4.8 checkmate_2.3.2 [87] colorRamp2_0.1.0 xfun_0.47 [89] MatrixGenerics_1.16.0 pkgconfig_2.0.3 Contents

vignettes/cross_section.Rmd

Pattern 6: "3 points" - Provide 3D coordinates to point1, point2, point3 params. For example:

"3 points"

Pattern 7: "point and norm vector" - Provide a 3D point anchor, then a 3D vector normal to the plane to set the plane rotation. For example:

"point and norm vector"

Pattern 8: "point and two plane vectors" - Provide a 3D point anchor and two non-parallel 3D vectors that define the plane rotation. For example:

"point and two plane vectors"

Reference Files

This skill includes comprehensive reference documentation organized into four main files:

core_api.md (285 files)

Core Giotto API and essential functions for spatial transcriptomics analysis

  • Data Processing: createGiottoObject(), filterGiottoObject(), normalizeGiottoObject()
  • Spatial Processing: addSpatialGrid(), addSpatialNetwork(), doHMRFanalysis()
  • Dimensionality Reduction: runPCA(), runUMAP(), runtSNE()
  • Visualization: spatPlot(), featPlot(), cellPlot(), networkPlot()
  • Statistics: runSpatialEnrich(), runSpatialCor(), runSpatialDE()
  • Integration: runWNN(), addCellMetadata(), addFeatMetadata()

tutorials.md (37 files)

Step-by-step tutorials for complete spatial transcriptomics workflows

  • Platform Integration: 10x Visium, Xenium, seqFISH+, MERFISH data import
  • Complete Workflows: From raw data to biological insights
  • Advanced Analyses: Multi-omics integration, 3D spatial analysis
  • Best Practices: Quality control, parameter optimization, troubleshooting
  • Case Studies: Real dataset examples with full code and explanation

data.md (4 files)

Data specifications and supported formats

  • Input Formats: 10x Visium, Xenium, seqFISH+, MERFISH, custom formats
  • Data Requirements: Minimum cell/gene counts, quality thresholds
  • File Organization: Directory structure, naming conventions
  • Preprocessing: Quality control steps, data cleaning guidelines

other.md (49 files)

Extended functionality and specialized tools

  • Spatial Statistics: Advanced spatial correlation, domain detection
  • 3D Analysis: Cross-section analysis, volume reconstruction
  • Batch Correction: Harmony integration, multi-sample analysis
  • Export Functions: Save results, generate reports
  • Utility Functions: Helper tools, data conversion, format handling

Working with This Skill

This skill is designed for researchers analyzing spatial transcriptomics data with Giotto Suite. Here's how to use it effectively:

For Beginners

Start with the Getting Started guides in references/getting_started.md:

  • Installation: Set up Giotto Suite and dependencies
  • First Analysis: Basic workflow with example dataset
  • Common Tasks: Data import, quality control, visualization
  • Troubleshooting: Solutions to frequent issues

For Intermediate Users

Use the Core API references for specific functions:

  • Data Processing: Filtering, normalization, quality control
  • Spatial Analysis: Network creation, domain detection, enrichment
  • Visualization: Publication-ready plots and interactive figures
  • Integration: Combine multiple datasets or modalities

For Advanced Users

Leverage specialized tutorials for complex analyses:

  • Multi-omics Integration: Combine spatial transcriptomics with proteomics
  • 3D Spatial Analysis: Analyze tissue sections in 3D space
  • Custom Workflows: Adapt pipelines for specific research questions
  • Advanced Statistics: Spatial correlation, differential expression

Common Analysis Patterns

  1. Start with Data Import: Choose the right function for your platform
  2. Quality Control: Filter cells and genes using filterGiottoObject()
  3. Normalization: Prepare data for analysis with normalizeGiottoObject()
  4. Spatial Processing: Add spatial information with addSpatialNetwork()
  5. Dimensionality Reduction: Reduce complexity with runPCA() and runUMAP()
  6. Clustering: Identify cell types and spatial domains
  7. Visualization: Create publication-ready figures
  8. Statistical Analysis: Test spatial hypotheses
  9. Export Results: Save and share your findings

Getting Help

  • Function Documentation: Check references/core_api.md for specific function details
  • Workflow Examples: See references/tutorials.md for complete analyses
  • Data Requirements: Refer to references/data.md for format specifications
  • Advanced Features: Explore references/other.md for specialized tools

Resources

references/

Organized documentation extracted from official sources. These files contain:

  • Detailed explanations
  • Code examples with language annotations
  • Links to original documentation
  • Table of contents for quick navigation

scripts/

Add helper scripts here for common automation tasks.

assets/

Add templates, boilerplate, or example projects here.

Notes

  • This skill was automatically generated from official documentation
  • Reference files preserve the structure and examples from source docs
  • Code examples include language detection for better syntax highlighting
  • Quick reference patterns are extracted from common usage examples in the docs

Updating

To refresh this skill with updated documentation:

  1. Re-run the scraper with the same configuration
  2. The skill will be rebuilt with the latest information