diff --git a/docs/examples/human_metastatic/0_environment.ipynb b/docs/examples/human_metastatic/0_environment.ipynb new file mode 100644 index 0000000..36b4e4e --- /dev/null +++ b/docs/examples/human_metastatic/0_environment.ipynb @@ -0,0 +1,199 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Preparing the environment" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First, we will walk you through the basics of using the command line interface (CLI), which is essential for running tools like `spacemake` and `Open-ST`. We assume you are running Linux (e.g., Ubuntu 22.04). A computer with at least 128 GB of RAM is recommended for `spacemake` and `openst`. Future optimizations to `spacemake` will reduce memory requirements." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Installing dependencies\n", + "\n", + "We will install two dependencies: `spacemake` and `openst`.\n", + "\n", + "For this, we assume that you have installed `mamba` or any similar (e.g., `micromamba`), and you have opened a terminal (local or remote machine).\n", + "\n", + "Create a new directory for this tutorial." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "vscode": { + "languageId": "plaintext" + } + }, + "source": [ + "```bash\n", + "WORKDIR=\"~/openst_demo\" # change this to any folder you want\n", + "mkdir -p $WORKDIR\n", + "cd $WORKDIR\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "During the rest of the tutorial, the root working directory will be\n", + "```bash\n", + "~/openst_demo\n", + "```\n", + "\n", + "Which we store in the environment variable `$WORKDIR`. You can change this to anything else." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Installing `spacemake`" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Then, create a new environment for `spacemake`:" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "vscode": { + "languageId": "plaintext" + } + }, + "source": [ + "```bash\n", + "wget \"https://raw.githubusercontent.com/rajewsky-lab/spacemake/master/environment.yaml\"\n", + "mamba env create -n openst -f environment.yaml\n", + "mamba activate openst\n", + "pip install spacemake\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Check that your `spacemake` version matches the one that will be used for this tutorial (0.7.9)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "vscode": { + "languageId": "plaintext" + } + }, + "source": [ + "```bash\n", + "spacemake --version\n", + "# make sure it is 0.7.9 or greater\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Installing `openst`" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, create another environment for `openst`. We have developed `openst` separately from `spacemake`, because `spacemake` is tech-agnostic (can run on scRNA-seq, Visium, Open-ST), while `openst` provides tools that are specific to the Open-ST technology (for segmenting, aligning, visualizing, image pre-processing...)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "vscode": { + "languageId": "plaintext" + } + }, + "source": [ + "```bash\n", + "# make sure the environment is still active!\n", + "# mamba activate openst\n", + "pip install openst\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Check that your `openst` version matches the one that will be used for this tutorial (0.2.3)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "vscode": { + "languageId": "plaintext" + } + }, + "source": [ + "```bash\n", + "openst --version\n", + "# make sure it is 0.2.3 or greater\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "!!! note\n", + " You might need to leave things running in the background (some things take hours to finish). We recommend using tmux; from the RedHat Sysadmin article:\n", + "\n", + " >Tmux is a terminal multiplexer; it allows you to create several **\"pseudo terminals\"** from a single terminal. \n", + "\n", + " >This is very useful for running multiple programs with a single connection, \n", + "\n", + " >such as when you're remotely connecting to a machine using Secure Shell (SSH).\n", + "\n", + " Open your `~/.bashrc` (or `~/.zshrc`) file with a text editor, or VIM, or anything else, and add the following lines:\n", + "\n", + " ```bash\n", + " alias ta='tmux attach -t'\n", + " alias tl='tmux ls'\n", + " alias tn='tmux new -s'\n", + " ```\n", + "\n", + " The next time you open a terminal, these aliases will be available:\n", + "\n", + " - `tn pepa`: create a new *pseudo-terminal* with name \"pepa\"\n", + " - `ta pepa`: attaches to the *pseudo-terminal* called \"pepa\"\n", + " - `tl`: lists all *pseudo-terminals* that are available\n", + "\n", + " To deattach a *pseudo-terminal*, you have the keyboard shortcut `Ctrl`+`b`+`d`\n", + "\n", + " To close a *pseudo-terminal*, you close all panels type `exit` and press `Enter`, or to close all at once you can use `Ctrl`+`b`+`&`" + ] + } + ], + "metadata": { + "language_info": { + "name": "python" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/examples/human_metastatic/1_spacemake.ipynb b/docs/examples/human_metastatic/1_spacemake.ipynb new file mode 100644 index 0000000..ade2d5e --- /dev/null +++ b/docs/examples/human_metastatic/1_spacemake.ipynb @@ -0,0 +1,483 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Preprocessing sequencing data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "After the experimental protocol, there are two major computational steps:\n", + "\n", + "1. Mapping the barcodes from the flow cell to spatial coordinates. **Important**: this is done only once per flow cell, and will be useful for ~80-300 experiments when capture areas are sized 3x4 mm.\n", + "2. Map the transcriptomic reads to reference genome and tissue space. This is done once per sample. In the case of 3D reconstruction, all steps are the same, but you will repeat them for each individual library (i.e., one per section). " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First of all, make sure that the working directory is set to the one you created previously:\n", + "\n", + "```bash\n", + "WORKDIR=\"/home/user/openst_demo\"\n", + "mkdir -p $WORKDIR\n", + "cd $WORKDIR\n", + "```\n", + "\n", + "Also, we set the base URL for the server where data will be downloaded from\n", + "```bash\n", + "BASE_URL=\"http://bimsbstatic.mdc-berlin.de/rajewsky/openst-public-data/openst_metastatic_lymph_node\"\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Generating barcode-to-coordinate map\n", + "\n", + "For each flow cell (thus, done only once), we generate plain text files with three columns: `cell_bc`, `x_pos`, and `y_pos`. These files are later used by `spacemake` to reconstruct the spatial coordinates from transcriptomic libraries. This process is performed only once per barcoded flow cell.\n", + "\n", + "!!! warning \"Software dependencies\"\n", + " Running `openst flowcell_map` below requires installing either `bcl2fastq` or `bclconvert`.\n", + " You can find instructions for [`bcl2fastq`](https://emea.support.illumina.com/sequencing/sequencing_software/bcl2fastq-conversion-software.html),\n", + " and [`bclconvert`](https://emea.support.illumina.com/sequencing/sequencing_software/bcl-convert.html).\n", + " \n", + " Then, make sure they are added to the `PATH` environment variable.\n", + " \n", + " For instance, in Linux: \n", + " ```bash\n", + " export PATH=/path/to/bcl2fastq:$PATH\n", + " # or\n", + " # export PATH=/path/to/bclconvert:$PATH\n", + " ```\n", + "\n", + " Make sure you use a version of these softwares compatible with your sequencer." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To reproduce this human metastatic lymph node example, we provide the [BCLs](https://emea.illumina.com/informatics/sequencing-data-analysis/sequence-file-formats.html) of the flow cell used for these tissue sections. When you run your own data, you or your sequencing facility will need to preprocess the flow-cell barcodes, only once.\n", + "\n", + "Download the BCLs to your machine:\n", + "\n", + "```bash\n", + "mkdir -p $WORKDIR/raw_data/flowcell_data\n", + "mkdir -p $WORKDIR/raw_data/tiles\n", + "\n", + "wget ${BASE_URL}/flowcell_data.tar.gz\n", + "tar xvf flowcell_data.tar.gz $WORKDIR/raw_data/flowcell_data\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "After the `bcl2fastq` or `bclconvert` dependencies have been installed, and you have downloaded the BCL, you can create the barcode-to-coordinate map for all tiles:\n", + "\n", + "```sh\n", + "openst flowcell_map \\\n", + " --bcl-in $WORKDIR/raw_data/flowcell_data \\\n", + " --tiles-out $WORKDIR/raw_data/tiles \\\n", + " --crop-seq 5:30 \\\n", + " --rev-comp\n", + "```\n", + "\n", + "This command will barcode-to-coordinate maps at `$WORKDIR/raw_data/tiles` - as many `.txt.gz` files as tiles in the barcoded flow cell." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Alternatively, you can skip the step above by downloading the expected results and putting them directly into the `$WORKDIR/raw_data/tiles` folder, e.g.:\n", + "\n", + "```bash\n", + "wget ${BASE_URL}/tile_data.tar.gz\n", + "tar xvf tile_data.tar.gz --strip-components=1 -C $WORKDIR/raw_data/tiles/.\n", + "```\n", + "\n", + "This is most likely not compatible with your own barcoded flow cell, as it will have completely different barcode-to-coordinate maps." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "!!! warning\n", + "\n", + " `spacemake`, when using the default `openst` mode, needs a coordinate system whose column `cell_bc` matches the names of the files in the root of `$WORKDIR/raw_data/tiles`.\n", + "\n", + " By default, `spacemake` includes a coordinate system that follows the naming:\n", + "\n", + " ```\n", + " fc_1_L1_tile_1101\n", + " fc_1_L1_tile_1102\n", + " ...\n", + " fc_1_L4_tile_2678\n", + " ```\n", + "\n", + " However, the files provided in our example follow the names:\n", + " ```\n", + " fc_1_1_1101.txt.gz\n", + " fc_1_1_1102.txt.gz\n", + " ...\n", + " fc_1_4_2678.txt.gz\n", + " ```\n", + "\n", + " To accomodate for this, you can use the coordinate system provided [here](http://bimsbstatic.mdc-berlin.de/rajewsky/openst-public-data/openst_metastatic_lymph_node/coordinate_system/openst_coordinate_system.csv)\n", + "\n", + " This can be downloaded directly with:\n", + " ```bash\n", + " mkdir -p $WORKDIR/raw_data/coordinate_system\n", + " wget http://bimsbstatic.mdc-berlin.de/rajewsky/openst-public-data/openst_metastatic_lymph_node/coordinate_system/openst_coordinate_system.csv -O $WORKDIR/raw_data/coordinate_system/openst_coordinate_system.csv\n", + " ```\n", + "\n", + " Later, we will configure it in `spacemake`." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Preparing the data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The complete dataset that inspires this tutorial is available to download from GEO. Here, to make all processing quicker, we will use a downsampled version (~50M reads instead of 500M-1B reads), that is available from our server.\n", + "\n", + "Below we use the `wget` command to download the files to the correct relative locations:" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "vscode": { + "languageId": "plaintext" + } + }, + "source": [ + "```bash\n", + "# create folder for the raw data\n", + "mkdir -p $WORKDIR/raw_data/reads\n", + "mkdir -p $WORKDIR/raw_data/images\n", + "\n", + "# download all raw data\n", + "## reads\n", + "cd $WORKDIR/raw_data/reads\n", + "wget ${BASE_URL}/reads/mLN_S2_R1.fastq.gz &\n", + "wget ${BASE_URL}/reads/mLN_S2_R2.fastq.gz &\n", + "wget ${BASE_URL}/reads/mLN_S3_R1.fastq.gz &\n", + "wget ${BASE_URL}/reads/mLN_S3_R2.fastq.gz &\n", + "wget ${BASE_URL}/reads/mLN_S4_R2.fastq.gz &\n", + "wget ${BASE_URL}/reads/mLN_S4_R1.fastq.gz\n", + "\n", + "## images\n", + "cd $WORKDIR/raw_data/images\n", + "wget ${BASE_URL}/images/mLN_S2.tif &\n", + "wget ${BASE_URL}/images/mLN_S3.tif &\n", + "wget ${BASE_URL}/images/mLN_S4.tif\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Transcriptomic & spatial mapping with `spacemake`" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Initialize\n", + "\n", + "Create the folder where `spacemake` will be initialized and run" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "vscode": { + "languageId": "plaintext" + } + }, + "source": [ + "```bash\n", + "mkdir -p $WORKDIR/spacemake\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Then, intialize the conda environment we created for `spacemake` (see notebook `0_environment.ipynb`)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "vscode": { + "languageId": "plaintext" + } + }, + "source": [ + "```bash\n", + "cd $WORKDIR/spacemake\n", + "\n", + "mamba activate openst\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You will have a folder structure like:\n", + "\n", + "```bash\n", + "/home/user # or other root folder\n", + "|-- openst_demo\n", + "| |-- raw_data\n", + "| | |-- raw_reads\n", + "| | | |-- mLN_S2_R1.fastq.gz\n", + "| | | `-- ...\n", + "| | `-- ... \n", + "| `-- spacemake\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Then, following the `spacemake` Quick start guide, browse to the spacemake directory you just created in the `openst_demo` folder, and run the initialization." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "vscode": { + "languageId": "plaintext" + } + }, + "source": [ + "```bash\n", + "wget https://github.com/broadinstitute/Drop-seq/releases/download/v2.5.1/Drop-seq_tools-2.5.1.zip -O Drop-seq_tools-2.5.1.zip\n", + "unzip Drop-seq_tools-2.5.1.zip\n", + "\n", + "spacemake init \\\n", + " --dropseq_tools Drop-seq_tools-2.5.1\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Configure" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As `spacemake` comes with no default value for species, before anything can be done, a new species has to be added. In this case, we add mouse; you will need to download the correct `fa` and `gtf` files. For instance, you can download the mouse genome from gencode, as well as the annotation.\n", + "\n", + "Then, you need to run the following commands:" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "vscode": { + "languageId": "plaintext" + } + }, + "source": [ + "```bash\n", + "mkdir -p $WORKDIR/genomes\n", + "wget http://bimsbstatic.mdc-berlin.de/rajewsky/openst-public-data/genomes/GRCh38p13.genome.fa -O $WORKDIR/genomes/GRCh38p13.genome.fa\n", + "wget http://bimsbstatic.mdc-berlin.de/rajewsky/openst-public-data/genomes/gencodev41.annotation.gtf -O $WORKDIR/genomes/gencodev41.annotation.gtf\n", + "wget http://bimsbstatic.mdc-berlin.de/rajewsky/openst-public-data/genomes/human.rRNA.fa -O $WORKDIR/genomes/human.rRNA.fa\n", + "\n", + "spacemake config add_species \\\n", + " --name human \\\n", + " --reference genome \\\n", + " --sequence $WORKDIR/genomes/GRCh38p13.genome.fa \\\n", + " --annotation $WORKDIR/genomes/gencodev41.annotation.gtf\n", + "\n", + "spacemake config add_species \\\n", + " --name human \\\n", + " --reference rRNA \\\n", + " --sequence $WORKDIR/genomes/human.rRNA.fa\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Additionally, make sure you use the correct coordinate system for the tile data relevant for your flow cell. In this specific example, you will need to copy the coordinate system downloaded above, as:\n", + "\n", + "```bash\n", + "cp $WORKDIR/raw_data/coordinate_system/openst_coordinate_system.csv $WORKDIR/spacemake/puck_data/openst_coordinate_system.csv\n", + "``` " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Add sample" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now you add the sample data and metadata to `spacemake`.\n", + "\n", + "For simplicity, we provide the tile barcode files that are related to this sample, as well as the coordinate system for the Illumina flow cell that was used to generate the capture area of this experiment. Notice that we wrap it inside a `for` loop to add the three sections (with IDs 2, 3, 4) at once." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "vscode": { + "languageId": "plaintext" + } + }, + "source": [ + "```bash\n", + "for sample in mLN_S{2..4}; do\n", + " spacemake projects add_sample \\\n", + " --project_id mLN \\\n", + " --sample_id \"$sample\" \\\n", + " --R1 $WORKDIR/raw_data/reads/\"${sample}\"_R1.fastq.gz \\\n", + " --R2 $WORKDIR/raw_data/reads/\"${sample}\"_R2.fastq.gz \\\n", + " --species human \\\n", + " --puck openst \\\n", + " --run_mode openst \\\n", + " --barcode_flavor openst \\\n", + " --puck_barcode_file $WORKDIR/raw_data/tiles/*.txt.gz \\\n", + " --map_strategy \"bowtie2:rRNA->STAR:genome:final\"\n", + "done\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Run\n", + "\n", + "That's it! Now, you can run `spacemake`:" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "vscode": { + "languageId": "plaintext" + } + }, + "source": [ + "```bash\n", + "spacemake run --cores 16 --keep-going\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Modify the number of `--cores` depending on your machine (minimum of 4 cores). Using more cores will also use more memory.\n", + "\n", + "!!! warning\n", + " Since this is a subsampled dataset (<100M reads, too few for these tissue section size), there's very few cells that go beyond the UMI cutoffs set at the run-mode, for the generation of automated reports. You can change these to lower values. Otherwise, some rules (concerning automated reports) might fail if you run `spacemake==0.7.9`. If only rules that fail are related to `automated_analysis`, you can ignore the errors (important to use `--keep-going`), and still proceed with the rest of the workflow." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Quality control & troubleshooting" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`spacemake` automatically creates `html` reports with convenient information about library QC and automated analysis (clustering, gene markers...). \n", + "\n", + "These are found at the sample's folders (e.g., for `mLN_S2`):\n", + "\n", + "`$WORKDIR/spacemake/projects/mLN/processed_data/mLN_S2/illumina/complete_data/`\n", + "\n", + "inside the `qc_sheets` and `automated_analysis` subfolders.\n", + "\n", + "1. The folder `qc_sheets` contains `html` reports with basic visualizations like histograms of unique molecules and genes per spatial unit (e.g., meshed/pseudo-cells of default size), and other metrics such as PCR bias\n", + "2. The folder `automated_analysis` contains different subfolders with different UMI thresholds, and the results of automated clustering, neighborhood analysis, and differential gene expression between clusters (i.e., marker gene analysis)\n", + "\n", + "Here you can browse the QC reports we obtained for these (downsampled) data:\n", + "\n", + "- [mLN_S2](http://bimsbstatic.mdc-berlin.de/rajewsky/openst-public-data/openst_metastatic_lymph_node/output_qc/qc_sheet_mLN_S2_puck_collection.html)\n", + "- [mLN_S3](http://bimsbstatic.mdc-berlin.de/rajewsky/openst-public-data/openst_metastatic_lymph_node/output_qc/qc_sheet_mLN_S3_puck_collection.html)\n", + "- [mLN_S4](http://bimsbstatic.mdc-berlin.de/rajewsky/openst-public-data/openst_metastatic_lymph_node/output_qc/qc_sheet_mLN_S4_puck_collection.html)\n", + "\n", + "Taking a look at these files gives a first impression of the quality of the data: \n", + "\n", + "- *Did the data yield the expected genes or molecules per ~cell with the chosen sequencing depth?*\n", + "- *Can one tell the tissue structure apart from the background by looking at UMIs or genes?*\n", + "- *Was the library efficiently amplified, or did the capture work well?*\n", + "- *Are there any noticeable spatial artifacts, e.g., missing areas of tissue?*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Next steps" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "After you successfully run `spacemake`, you can proceed with the specific steps of Open-ST data with the `openst` package.\n", + "\n", + "Otherwise, you can use the *hexbin* objects, which are a good approximation for expression at cellular resolution (but not suitable for subcellular localization, or neighborhood analysis).\n", + "\n", + "These files are found at (e.g., for `mLN_S2`):\n", + "\n", + "`$WORKDIR/spacemake/projects/mLN/processed_data/mLN_S2/illumina/complete_data/dge/dge.all.polyA_adapter_trimmed.mm_included.spatial_beads.mesh_7_hexagon_puck_collection`\n", + "\n", + "These can be used for pairwise alignment to the images, but cannot be used for segmentation. In that case, you need to use the 0.6 µm-resolved spots, and not this 7 µm-side hexagon binning.\n", + "\n", + "Also, these can be used for 3D registration, but we recommend using the segmented objects." + ] + } + ], + "metadata": { + "language_info": { + "name": "python" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/examples/human_metastatic/2_openst.ipynb b/docs/examples/human_metastatic/2_openst.ipynb new file mode 100644 index 0000000..0b7d3dd --- /dev/null +++ b/docs/examples/human_metastatic/2_openst.ipynb @@ -0,0 +1,349 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Integrating imaging and sequencing" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the previous step, the flow-cell barcodes were mapped to spatial coordinates, and the transcriptomic reads were processed and mapped in tissue space with `spacemake`. \n", + "\n", + "With those steps, you obtained `h5ad` objects where each cell is a [hexbin](https://h3geo.org/docs/comparisons/hexbin/), containing around ~400 of the 0.6µm barcoded spots, and can be used already for analysis.\n", + "\n", + "For more accurate single-cell representations from the spatial data, we perform image-informed segmentation. This involves three major steps:\n", + "\n", + "1. Pairwise alignment between the imaging and spatial transcriptomics modality\n", + "2. Segmentation of the imaging modality\n", + "3. Assign transcripts into individual (segmented) cells.\n", + "\n", + "We will illustrate how to do this in a semiautomatic manner: that is, running the coarse alignment in an automatic fashion, and the fine alignment (to fiducial marks) via a Graphical User Interface (GUI), in an interactive manner. This is typically a quick process (~5 minutes per sample of 12 tiles)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We start again at the `spacemake` root folder:" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "vscode": { + "languageId": "plaintext" + } + }, + "source": [ + "```bash\n", + "WORKDIR=\"/home/user/openst_demo\"\n", + "cd $WORKDIR/spacemake\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Stitching of spatial coordinates" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If you ran `spacemake` with a run mode that meshes the data, and no run mode that does not mesh the data, you will need to spatially stitch the single spots data into a single file before pairwise alignment and segmentation. That is, the typical output for `spacemake` consists of several `h5ad` files, one per `tile`, and we require a single file for the sample that contains all tiles in their correct spatial offsets. We do this automatically by running the following:" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "vscode": { + "languageId": "plaintext" + } + }, + "source": [ + "```bash\n", + "for sample in mLN_S{2..4}; do\n", + " openst from_spacemake \\\n", + " --project-id mLN \\\n", + " --sample-id \"$sample\" \\\n", + " spatial_stitch \\\n", + " --tile-coordinates $WORKDIR/spacemake/puck_data/openst_coordinate_system.csv\n", + "done\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "There will be a new `h5ad` file that contains all spatial coordinates at the single spot level (0.6 micron resolution), instead of the arbitrary meshing to pseudocells. This is what we require for taking advantage of image-based cell segmentation." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Merging the modalities (image and ST)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We assume that the image was stitched automatically with the `openst` code above. Then, all files will be at the expected locations and you can run the following:" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "vscode": { + "languageId": "plaintext" + } + }, + "source": [ + "```bash\n", + "for sample in mLN_S{2..4}; do\n", + " openst from_spacemake \\\n", + " --project-id mLN \\\n", + " --sample-id \"$sample\" \\\n", + " merge_modalities \\\n", + " --image-in \"${WORKDIR}\"/raw_data/images/\"${sample}\".tif\n", + "done\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This will store the image data into the `h5ad` object, for pairwise alignment and cell segmentation." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Pairwise alignment" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "!!! info\n", + " Pairwise alignment should be done in a per-section basis, since manual, qualitative validation of the results is recommended. \n", + "\n", + " Therefore, make sure to repeat these steps for the sections `mLN_S2`, `mLN_S3` and `mLN_S4`.\n", + "\n", + "You can run automatic pairwise alignment of the two modalities by running this command:" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "vscode": { + "languageId": "plaintext" + } + }, + "source": [ + "```bash\n", + "openst from_spacemake \\\n", + " --project-id mLN \\\n", + " --sample-id mLN_S2 \\\n", + " pairwise_aligner \\\n", + " --device cuda\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Pairwise alignment happens in two steps, coarse and fine. At the coarse level, low-resolution image and ST data are used, then once the rotation, flip level and offset is more or less set, high-resolution image and ST are used for finding the most accurate alignment, relying on small features from the tissue and the concentric circles (fiducial marks) that are visible at both image and the ST modalities.\n", + "\n", + "It is good practice to check the quality of the alignment before proceeding with the rest of the pipeline. We provide a GUI tool to do this:" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "vscode": { + "languageId": "plaintext" + } + }, + "source": [ + "```bash\n", + "openst from_spacemake \\\n", + " --project-id mLN \\\n", + " --sample-id mLN_S2 \\\n", + " manual_pairwise_aligner\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can find more instructions for how to use this tool in this [YouTube video](https://www.youtube.com/watch?v=Fjh4huosVPY). \n", + "\n", + "If the alignment does not look optimal (e.g., fiducial circles visible at each tile, across both modalities, do not look perfectly aligned), you can use this tool for manual refinement of the alignment. \n", + "\n", + "This tool helps to store user-selected keypoints as a standard `json` file, that can be used to compute the transformation matrices that align the ST data into the imaging modality. When such a `json` file is available, you can compute the optimal transformation with the following tool:" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "vscode": { + "languageId": "plaintext" + } + }, + "source": [ + "```bash\n", + "openst from_spacemake \\\n", + " --project-id mLN \\\n", + " --sample-id mLN_S2 \\\n", + " apply_transform \\\n", + " --keypoints-in keypoints.json \\\n", + " --spatial-key-in obsm/spatial_pairwise_aligned_coarse \\ # or obsm/spatial_pairwise_aligned_fine\n", + " --spatial-key-out obsm/spatial_manual_fine \\\n", + " --per-tile\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Cell segmentation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Once the ST and imaging modalities have been aligned, you can segment the images into single cells/nuclei, and then aggregate the spot locations into individual cells for subsequent analysis." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "vscode": { + "languageId": "plaintext" + } + }, + "source": [ + "```bash\n", + "for sample in mLN_S{2..4}; do\n", + " openst from_spacemake \\\n", + " --project-id mLN \\\n", + " --sample-id \"$sample\" \\\n", + " segment \\\n", + " --model HE_cellpose_rajewsky \\\n", + " --device cuda # ignore if no CUDA-compatible GPU is available \n", + "done\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can assess the segmentation masks with the `openst preview` tool, based on the `napari` image viewer. You can do this section by section, e.g., for section `mLN_S2`:" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "vscode": { + "languageId": "plaintext" + } + }, + "source": [ + "```bash\n", + "openst from_spacemake \\\n", + " --project-id mLN \\\n", + " --sample-id mLN_S2 \\\n", + " preview \\\n", + " --image-keys uns/spatial/staining_image uns/spatial/staining_image_mask\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "!!! note\n", + " You can also segment the images with other tools (e.g., QuPath, ImageJ, Cellpose3...) and then integrate in the Open-ST object with `merge_modalities`, as above." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Single-cell quantification" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Then, you can create a single file containing the transcriptomic information aggregated into (segmented) single-cells." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "vscode": { + "languageId": "plaintext" + } + }, + "source": [ + "```bash\n", + "for sample in mLN_S{2..4}; do\n", + " openst from_spacemake \\\n", + " --project-id mLN \\\n", + " --sample-id \"$sample\" \\\n", + " transcript_assign \\\n", + " --spatial-key obsm/spatial_manual_fine \\ # or obsm/spatial_pairwise_fine, if the automated results are satisfying\n", + " --mask-in uns/spatial/staining_image_mask\n", + "done\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Next steps" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "After running all steps above, you will have a single `h5ad` file that contains the cell-by-gene matrix, using the segmented cells and not the pseudocells that were generated by `spacemake` (by default, a regular grid of 7-micron side hexagons).\n", + "\n", + "This file can be found at (for `mLN_S2`):\n", + "\n", + "`$WORKDIR/spacemake/projects/mLN/processed_data/mLN_S2/multimodal/stitched_segmented.h5ad`\n", + "\n", + "This single object contains transcriptomic data per segmented cell, the imaging and segmentation information, and can be used for downstream analysis." + ] + } + ], + "metadata": { + "language_info": { + "name": "python" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/examples/human_metastatic/3_stim.ipynb b/docs/examples/human_metastatic/3_stim.ipynb new file mode 100644 index 0000000..14d0466 --- /dev/null +++ b/docs/examples/human_metastatic/3_stim.ipynb @@ -0,0 +1,910 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Aligning Open-ST metastatic lymph node\n", + "\n", + "In this tutorial, we will illustrate how to perform the alignment of the 3 serial sections from a [human metastatic lymph node profiled with Open-ST](https://doi.org/10.1016/j.cell.2024.05.055), that we just preprocessed with `spacemake` and `openst`.\n", + "\n", + "Make sure you have activated the `openst` environment you created previously." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Installing dependencies\n", + "\n", + "We will install [`STIM`](https://www.biorxiv.org/content/10.1101/2021.12.07.471629v3) (both the Java-based components, and the Python bindings)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "```bash\n", + "mamba install -c conda-forge stim\n", + "pip install stimwrap\n", + "pip install scanpy\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Running from Jupyter\n", + "\n", + "First of all, you must have a Python environment with [ipykernel](https://ipython.readthedocs.io/en/stable/install/kernel_install.html) for running this notebook, or [Jupyter](https://jupyter.org/). If so, you can run the steps below as an interactive notebook." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Importing `stimwrap`\n", + "\n", + "[`STIM`](https://www.biorxiv.org/content/10.1101/2021.12.07.471629v3) is a console-based tool.\n", + "\n", + "However, when running your analysis in the `Python` ecosystem (like here), you can transparently run `STIM` from `Python` by leveraging the wrapper [`stimwrap`](https://github.com/rajewsky-lab/stimwrap).\n", + "\n", + "`stimwrap` provides bindings for all commands, and additional tools for data preprocessing and conversion prior to downstream analysis." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import stimwrap as st" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'/home/user/mambaforge/envs/malva/bin'" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "st.set_bin_path(\"/home/user/mambaforge/envs/malva/bin\") # change depending on your conda environment" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Creating a N5 container from `AnnData` objects\n", + "\n", + "STIM requires that the individual section data is resaved into a single N5 container.\n", + "\n", + "This allows to have a single directory containing all data (spatial expression values), metadata (cell annotations), and the output from STIM registration (landmarks, transformation matrices).\n", + "\n", + "You can find more about the N5 standard here.\n", + "\n", + "!!! note\n", + " In this tutorial, we assume you could pairwise-align and segment all sections. However, you can also do 3D registration of *hexbin* objects, which are created automatically by `spacemake`. In that case, adapt the paths accordingly." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Section ID: /home/user/openst_demo/spacemake/projects/fc_sts_063/processed_data/fc_sts_063_2/multimodal/fc_sts_063_2_stitched_segmented.h5ad; Z-axis: 2\n", + "Section ID: /home/user/openst_demo/spacemake/projects/fc_sts_063/processed_data/fc_sts_063_3/multimodal/fc_sts_063_3_stitched_segmented.h5ad; Z-axis: 3\n", + "Section ID: /home/user/openst_demo/spacemake/projects/fc_sts_063/processed_data/fc_sts_063_4/multimodal/fc_sts_063_4_stitched_segmented.h5ad; Z-axis: 4\n" + ] + } + ], + "source": [ + "import glob\n", + "import os\n", + "import re\n", + "\n", + "workdir = \"/home/user/openst_demo\"\n", + "spacemake_folder = \"spacemake/projects/mLN/processed_data/mLN_*/multimodal\"\n", + "sections = glob.glob(os.path.join(workdir, spacemake_folder, \"stitched_segmented.h5ad\"))\n", + "sections_numbers = [2, 3, 4]\n", + "sections_numbers, sections = zip(*sorted(zip(sections_numbers, sections)))\n", + "\n", + "# we create an alias because STIM needs unique file names\n", + "for section, number in zip(sections, sections_numbers):\n", + " alias = re.search(r'mLN_S\\w+', section).group(0)\n", + " directory = os.path.dirname(section)\n", + " link_path = os.path.join(os.path.join(directory, f\"{alias}_stitched_segmented.h5ad\"))\n", + " if os.path.exists(link_path):\n", + " os.remove(link_path)\n", + " os.symlink(section, link_path)\n", + "\n", + "sections = glob.glob(os.path.join(workdir, spacemake_folder, \"*_stitched_segmented.h5ad\"))\n", + "sections_numbers = [2, 3, 4]\n", + "sections_numbers, sections = zip(*sorted(zip(sections_numbers, sections)))\n", + "\n", + "print(*[f\"Section ID: {s}; Z-axis: {n}\" for s, n in zip(sections, sections_numbers)], sep=\"\\n\")" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "('/home/user/openst_demo/spacemake/projects/fc_sts_063/processed_data/fc_sts_063_2/multimodal/fc_sts_063_2_stitched_segmented.h5ad',\n", + " '/home/user/openst_demo/spacemake/projects/fc_sts_063/processed_data/fc_sts_063_3/multimodal/fc_sts_063_3_stitched_segmented.h5ad',\n", + " '/home/user/openst_demo/spacemake/projects/fc_sts_063/processed_data/fc_sts_063_4/multimodal/fc_sts_063_4_stitched_segmented.h5ad')" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sections" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Make sure the sections are in order, because these will be used as the relative Z-axis order." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With the code above, we have sorted the files according to their names, which contain the Z-axis offset (equal to section number).\n", + "\n", + "Now, we add the slices into a single N5 container:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[2024-10-30T13:21:49,394] [main] INFO : Log level set to: INFO\n", + "[2024-10-30T13:21:49,471] [main] INFO : Container '/home/user/openst_demo/openst_metastatic_lymph_node.n5' is new ...\n", + "[2024-10-30T13:21:49,621] [main] INFO : Linked dataset '/home/user/openst_demo/spacemake/projects/fc_sts_063/processed_data/fc_sts_063_2/multimodal/fc_sts_063_2_stitched_segmented.h5ad' to container '/home/user/openst_demo/openst_metastatic_lymph_node.n5'.\n", + "[2024-10-30T13:21:50,527] [main] INFO : Log level set to: INFO\n", + "[2024-10-30T13:21:50,603] [main] INFO : Container '/home/user/openst_demo/openst_metastatic_lymph_node.n5' exists\n", + "[2024-10-30T13:21:50,750] [main] INFO : Linked dataset '/home/user/openst_demo/spacemake/projects/fc_sts_063/processed_data/fc_sts_063_3/multimodal/fc_sts_063_3_stitched_segmented.h5ad' to container '/home/user/openst_demo/openst_metastatic_lymph_node.n5'.\n", + "[2024-10-30T13:21:51,647] [main] INFO : Log level set to: INFO\n", + "[2024-10-30T13:21:51,723] [main] INFO : Container '/home/user/openst_demo/openst_metastatic_lymph_node.n5' exists\n", + "[2024-10-30T13:21:51,865] [main] INFO : Linked dataset '/home/user/openst_demo/spacemake/projects/fc_sts_063/processed_data/fc_sts_063_4/multimodal/fc_sts_063_4_stitched_segmented.h5ad' to container '/home/user/openst_demo/openst_metastatic_lymph_node.n5'.\n" + ] + } + ], + "source": [ + "container_path = os.path.join(workdir, \"openst_metastatic_lymph_node.n5\")\n", + "\n", + "st.add_slices(container=container_path, inputs=sections)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Pairwise registration\n", + "\n", + "As indicated in its name, STIM will handle ST data as images. These are multi-channel images where the XY dimensions can be specified by a scaling factor (e.g., 1:1 to map 1 pixel to 1 ST unit), and the channels are genes.\n", + "\n", + "During pairwise registration, STIM will automatically find corresponding points between pairs of sections for a subset of genes, and keep those with _high quality/agreement_ across all genes for a pair of sections. This is required prior to assembling a global alignment model (when more than 2 sections are provided).\n", + "\n", + "A subset of genes is used to avoid registering with all genes (in sequencing-based ST, this can lead to ~30,000 channels). This might be too time-consuming, and also most genes do not have sufficient information to render images with spatial patterns that can be used for feature detection (sparsity problem). By default, STIM detects genes with highest variance as a proxy for genes that might show suitable spatial patterns. Otherwise, the user can specify a set of genes used to render images for pairwise alignment. This is what we will do in this tutorial." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This assumes that you have installed `scanpy`. You can do that by running\n", + "```bash\n", + "pip install scanpy\n", + "```\n", + "\n", + "Then, you can use it to detect highly variable genes, following the typical workflow for single-cell/spatial analysis" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/user/mambaforge/envs/malva/lib/python3.11/site-packages/stimwrap/_stimwrap.py:399: FutureWarning: The N5Store is deprecated and will be removed in a Zarr-Python version 3, see https://github.com/zarr-developers/zarr-python/issues/1274 for more information.\n", + " self.container: zarr.N5Store = zarr.N5Store(self.path)\n" + ] + } + ], + "source": [ + "# Run the code below if you get issues with __DATA_TYPES__\n", + "container = st.Container(container_path)\n", + "container.cleanup_container()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/user/mambaforge/envs/malva/lib/python3.11/site-packages/scanpy/preprocessing/_highly_variable_genes.py:693: ImplicitModificationWarning: Trying to modify attribute `._uns` of view, initializing view as actual.\n", + " adata.uns[\"hvg\"] = {\"flavor\": flavor}\n" + ] + } + ], + "source": [ + "import scanpy as sc\n", + "import h5py\n", + "\n", + "# Compute common genes\n", + "common_genes = set([g.decode(\"utf-8\") for g in h5py.File(sections[0], 'r')['var/_index'][:].tolist()])\n", + "for s in sections[1:]:\n", + " with h5py.File(s, 'r') as s_data:\n", + " common_genes.intersection(set([g.decode(\"utf-8\") for g in s_data['var/_index'][:].tolist()]))\n", + "\n", + "# Load one of the sections; these data is already normalized\n", + "adata = sc.read_h5ad(sections[0])\n", + "\n", + "# Filter and normalize\n", + "sc.pp.calculate_qc_metrics(adata, inplace=True)\n", + "sc.pp.filter_cells(adata, min_counts=50)\n", + "sc.pp.filter_cells(adata, max_counts=10000)\n", + "sc.pp.normalize_total(adata, inplace=True)\n", + "sc.pp.log1p(adata)\n", + "\n", + "# Subset to genes common across all sections\n", + "adata = adata[:, adata.var_names.isin(common_genes)]\n", + "\n", + "# Detect and select highly variable genes (common across all sections), this leads to 15 of them\n", + "sc.pp.highly_variable_genes(adata, flavor=\"seurat\", min_mean=0.2, max_mean=0.6)\n", + "hvg_genes = adata.var_names[adata.var['highly_variable']].tolist()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['CD74',\n", + " 'COL1A1',\n", + " 'COL1A2',\n", + " 'COL3A1',\n", + " 'IGHG3',\n", + " 'RPL13A',\n", + " 'RPL23A',\n", + " 'RPL27A',\n", + " 'RPL32',\n", + " 'RPLP2',\n", + " 'RPS12',\n", + " 'RPS23',\n", + " 'RPS27',\n", + " 'RPS29',\n", + " 'RPS6']" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "hvg_genes" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "!!! note\n", + " Adding more genes might increase the accuracy of pairwise alignment, but 10 seems to be enough for this example dataset. Adding more genes increases the time required for pairwise alignment." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "706" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# we remove `adata` from memory because it is not required anymore\n", + "import gc\n", + "del adata\n", + "gc.collect()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, we run the pairwise alignment with the `hvg_genes`" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[2024-10-30T13:23:29,229] [main] INFO : Log level set to: INFO\n", + "[2024-10-30T13:23:29,462] [main] WARN : No input datasets specified. Trying to open all datasets in '/home/user/openst_demo/openst_metastatic_lymph_node.n5' ...\n", + "[2024-10-30T13:23:29,463] [main] INFO : Opening 3 datasets\n", + "[2024-10-30T13:23:29,850] [main] WARN : Could not read annotation 'puck_id'. Skipping\n", + "[2024-10-30T13:23:30,481] [main] WARN : Could not read annotation 'puck_id'. Skipping\n", + "[2024-10-30T13:23:30,980] [main] WARN : Could not read annotation 'puck_id'. Skipping\n", + "[2024-10-30T13:23:31,330] [main] WARN : Parameter maxEpsilon is unset or negative; using 10 * average distance between sequenced locations = 3210.8364692356026\n", + "[2024-10-30T13:23:31,330] [main] INFO : Overwriting previous results for: fc_sts_063_2_stitched_segmented.h5ad-fc_sts_063_4_stitched_segmented.h5ad\n", + "[2024-10-30T13:23:31,336] [main] INFO : Retrieving standard deviation of genes for all sections\n", + "[2024-10-30T13:23:31,348] [main] INFO : Aligning fc_sts_063_2_stitched_segmented.h5ad <> fc_sts_063_3_stitched_segmented.h5ad on 10 genes (8 threads)\n", + "\n", + "[ ] 0.90%\n", + "[ ] 1.80%\n", + "[= ] 2.70%\n", + "[= ] 3.60%\n", + "[== ] 4.50%\n", + "[== ] 5.40%\n", + "[=== ] 6.30%\n", + "[=== ] 7.20%\n", + "[==== ] 9.45%\n", + "[===== ] 11.70%\n", + "[====== ] 13.95%\n", + "[======== ] 16.20%\n", + "[========= ] 18.45%\n", + "[========== ] 20.70%\n", + "[=========== ] 22.95%\n", + "[============ ] 25.20%\n", + "[============== ] 29.70%\n", + 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need to specify the argument `overwrite=True` when calling `st.align_pairs`.\n", + "\n", + "Otherwise, `STIM` assumes that pairwise alignment was performed and will exit." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Visualization of results\n", + "\n", + "!!! note\n", + " To run the interactive visualization or alignment tools via `st.align_pairs_view`, `st.align_interactive` or `st.explorer`, make sure you are running this notebook in a computer with a graphical environment, or that you are doing redirection of the window server (e.g., X11 redirection via `ssh -X ...`).\n", + "\n", + " You can learn more about this [here](https://goteleport.com/blog/x11-forwarding/).\n", + "\n", + "It is good practice to manually assess the results of pairwise alignment before proceeding or using these data for analysis, as the set of parameters used for registration might have not been suitable for the data at hand. Some reasons leading to poor alignment might be:\n", + "\n", + "- Poor selection of the subset of genes used for alignment\n", + "- Scale (or render factor) parameter too large or too small\n", + "- Poor selection of alignment error (`--maxEpsilon`) parameter\n", + "- Data is too noisy and might need some filtering (e.g., with `--ffMedian` or `--ffSingleSpot`)\n", + "\n", + "STIM provides GUI-based tools to interactively assess the result from pairwise alignment:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "st.align_pairs_view(container=container_path,\n", + " datasets=[s[2:] for s in sections],\n", + " gene=hvg_genes[0])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Alternatively, you can use an interactive pairwise alignment tool to find suitable parameters, iteratively" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "st.align_interactive(container=container_path,\n", + " section_a=sections[0],\n", + " section_b=sections[1])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Global alignment\n", + "\n", + "Once you are satisfied with the results from the pairwise alignment of pairs of sections, you can proceed with the global alignment. \n", + "\n", + "This last step optimizes a global model taking into account all pairs of keypoints. \n", + "\n", + "This reduces error propagation across sections, which might lead to very large distortions in the reconstruction." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[2024-10-30T14:01:06,116] [main] INFO : Log level set to: INFO\n", + "[2024-10-30T14:01:06,347] [main] WARN : No input datasets specified. Trying to open all datasets in '/home/user/openst_demo/openst_metastatic_lymph_node.n5' ...\n", + "[2024-10-30T14:01:06,773] [main] WARN : Could not read annotation 'puck_id'. Skipping\n", + "[2024-10-30T14:01:07,466] [main] WARN : Could not read annotation 'puck_id'. Skipping\n", + "[2024-10-30T14:01:08,007] [main] WARN : Could not read annotation 'puck_id'. Skipping\n", + "i=0: 0=0.0 1=0.27046238733935246 2=0.0 \n", + "i=1: 0=0.27046238733935246 1=0.0 2=1.0 \n", + "i=2: 0=0.0 1=1.0 2=0.0 \n", + "[2024-10-30T14:01:08,466] [main] INFO : Prealigned all tiles\n", + "Shuffling took 0 ms\n", + "First apply took 49 ms\n", + "Concurrent tile optimization loop took 1820 ms, total took 1869 ms\n", + "[2024-10-30T14:01:10,346] [main] INFO : Global optimization of 3\n", + "[2024-10-30T14:01:10,347] [main] INFO : Avg Error: 1371.906349455969px\n", + "[2024-10-30T14:01:10,347] [main] INFO : Min Error: 1310.9160679095392px\n", + "[2024-10-30T14:01:10,348] [main] INFO : Max Error: 1455.886006863696px\n", + "[2024-10-30T14:01:10,433] [main] INFO : Removed link from 0 to 2\n", + "[2024-10-30T14:01:10,436] [main] INFO : Prealigned all tiles\n", + "Shuffling took 0 ms\n", + "First apply took 20 ms\n", + "Concurrent tile optimization loop took 902 ms, total took 922 ms\n", + "[2024-10-30T14:01:11,364] [main] INFO : Global optimization of 3\n", + "[2024-10-30T14:01:11,365] [main] INFO : Avg Error: 1361.4490413691617px\n", + "[2024-10-30T14:01:11,367] [main] INFO : Min Error: 1222.1076612871498px\n", + "[2024-10-30T14:01:11,368] [main] INFO : Max Error: 1519.2326629326167px\n", + "WARNING: can not remove any more links without disconnecting components\n", + "[2024-10-30T14:01:11,423] [main] INFO : Removed 0 to 2 (gui.STDataAssembly@11ee02f8 to gui.STDataAssembly@71154f21)\n", + "[2024-10-30T14:01:11,533] [main] INFO : Avg error: 1361.4490413691617\n" + ] + } + ], + "source": [ + "st.align_global(container=container_path,\n", + " skip_icp=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Visualization of results\n", + "\n", + "Similarly as before, it is good practice to visualize the results after the global alignment procedure. \n", + "\n", + "STIM can leverage `BigDataViewer` for 3D visualization of these data:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "st.bdv_view3d(input=container_path,\n", + " genes=hvg_genes)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "!!! note\n", + " To run `BigDataViewer`, similarly to above, make sure you are running this notebook in a computer with a graphical environment, or that you are doing redirection of the window server (e.g., X11 redirection via `ssh -X ...`)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Storing the 3D coordinates in `AnnData`\n", + "\n", + "Prior to analysing these objects with `scanpy` or other tools from the `scverse` ecosystem, you can apply the transformation model \n", + "and store the transformed 3D coordinates as a new layer in the `AnnData` (or `N5`) objects." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "# load N5 container with stimwrap\n", + "container = st.Container(container_path)\n", + "container.cleanup_container()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# iterate over datasets and apply the computed transformation\n", + "for z_axis, dataset_name in zip(sections_numbers, container.get_dataset_names()):\n", + " with container.get_dataset(dataset_name, mode=\"r+\") as dataset:\n", + " dataset.apply_save_transform(transformation=\"model_sift\",\n", + " locations='spatial',\n", + " destination='spatial_transform_sift',\n", + " z_coord=z_axis)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Demo: interoperability with `scanpy`\n", + "\n", + "Here, we showcase the interoperability of STIM (via `AnnData`-backed N5) by plotting genes and running some data processing with `scanpy`.\n", + "\n", + "First of all, you can create a single `AnnData` object that can be loaded at once with `scanpy` (all cells in the same file)." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "import scanpy as sc" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "import anndata as ad\n", + "\n", + "adata_concatenated = ad.concat([ad.read_h5ad(adata_path) for adata_path in sections], join='inner', index_unique=\"_\")\n", + "adata_concatenated.write_h5ad(os.path.join(workdir, \"metastatic_lymph_node_aligned.h5ad\"))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Note\n", + "\n", + "If you get errors in the cell above because of `__DATA_TYPES__` or because of `column-order`, run the following command:" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING:root:Dataset fc_sts_063_2_stitched_segmented.h5ad is clean (does not contain '__DATA_TYPES__'). Skipping...\n", + "WARNING:root:Dataset fc_sts_063_3_stitched_segmented.h5ad is clean (does not contain '__DATA_TYPES__'). Skipping...\n", + "WARNING:root:Dataset fc_sts_063_4_stitched_segmented.h5ad is clean (does not contain '__DATA_TYPES__'). Skipping...\n" + ] + } + ], + "source": [ + "# run if necessary (comment out line below)\n", + "container.cleanup_container()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Plotting gene expression" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We transpose the Z-axis coordinates for plotting with scanpy (from a different point of view)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "adata_concatenated.obsm['spatial_transform_sift_plotting'] = adata_concatenated.obsm['spatial_transform_sift'][:, [2, 0, 1]]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We use the `pl.embedding` function for faster plotting (also, axes are scaled to the same magnitude). One can alternatively use the `pl.spatial` function" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sc.pl.embedding(adata_concatenated, color=['IGKC'], projection='3d', size=1, basis='spatial_transform_sift_plotting', cmap='Reds')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Plotting normalized gene expression" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the plot above, raw counts are shown. As different sections had different sequencing depth, the intensities are not fully comparable. \n", + "\n", + "For improved visualization (and downstream analysis), we can normalize the values across sections. This follows the typical scanpy workflow." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "# Filter and normalize\n", + "sc.pp.calculate_qc_metrics(adata_concatenated, inplace=True)\n", + "sc.pp.filter_cells(adata_concatenated, min_counts=50)\n", + "sc.pp.filter_cells(adata_concatenated, max_counts=10000)\n", + "sc.pp.normalize_total(adata_concatenated, inplace=True)\n", + "sc.pp.log1p(adata_concatenated)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we can plot again, which will show depth and log-normalized counts." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sc.pl.embedding(adata_concatenated, color=['IGKC', 'FDCSP', 'COL1A2'], projection='3d', size=1, basis='spatial_transform_sift_plotting', cmap='Reds')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "From here we can proceed with downstream analysis, like cell type clustering, differential expression, discovery of spatial features..." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "malva", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.9" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/examples/human_metastatic/introduction.md b/docs/examples/human_metastatic/introduction.md new file mode 100644 index 0000000..5a3a02e --- /dev/null +++ b/docs/examples/human_metastatic/introduction.md @@ -0,0 +1,9 @@ +# Introduction + +In our paper, we used a human metastatic lymph node sample to benchmark the reproducibility and efficiency of Open-ST for 3D reconstruction. + +We profiled patient-matched samples from human head and neck squamous cell carcinoma (HNSCC), due to the interesting transcriptional diversity and spatial organization. For the human metastatic lymph node, we applied Open-ST to serial sections spanning 350 μm. We constructed a 3D virtual tissue block with over a million cells and hundreds of millions of transcripts embedded in the H&E stainings. + +In the following sections, we explore how to reproduce the (pre)processing for these data. + +These steps are introduced in our [_STAR Protocols_ paper](https://www.cell.com/star-protocols/home). The data is described in our [Open-ST paper, in _Cell_](https://doi.org/10.1016/j.cell.2024.05.055). \ No newline at end of file diff --git a/mkdocs.yml b/mkdocs.yml index 50fb420..e7337dc 100755 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -43,6 +43,12 @@ nav: - Pairwise alignment: examples/e13_mouse/pairwise_alignment.md - Segmentation and single-cell quantification: examples/e13_mouse/generate_expression_matrix.md - Exploratory analysis (Jupyter): static/examples/notebooks/e13_head_eda.ipynb + - (3D) Metastatic Lymph Node: + - Introduction: examples/human_metastatic/introduction.md + - Environment setup: examples/human_metastatic/0_environment.md + - Preprocessing sequencing data: examples/human_metastatic/1_spacemake.md + - Alignment and single-cell quantification: examples/human_metastatic/2_openst.md + - 3D reconstruction: examples/human_metastatic/3_stim.md theme: name: material