From e947f0324a03ad585bff822eecb5c35e2e5611aa Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Daniel=20Le=C3=B3n-Perin=C3=A1n?= Date: Fri, 19 Apr 2024 18:34:59 +0200 Subject: [PATCH] docs: big refactoring --- .../generate_expression_matrix.md | 167 ++++--- docs/computational/getting_started.md | 53 +- docs/computational/pairwise_alignment.md | 471 ++++++++++++------ .../preprocessing_capture_area.md | 102 ++++ .../preprocessing_openst_library.md | 95 ++++ .../computational/preprocessing_sequencing.md | 178 ------- .../adult_mouse/generate_expression_matrix.md | 64 +-- .../adult_mouse/pairwise_alignment.md | 136 +++-- .../adult_mouse/preprocessing_imaging.md | 27 - .../adult_mouse/preprocessing_sequencing.md | 10 +- .../e13_mouse/generate_expression_matrix.md | 60 +-- docs/examples/e13_mouse/pairwise_alignment.md | 131 +++-- .../e13_mouse/preprocessing_imaging.md | 27 - .../e13_mouse/preprocessing_sequencing.md | 22 +- docs/static/img/adult_mouse_histology.webp | Bin 0 -> 483556 bytes mkdocs.yml | 23 +- 16 files changed, 878 insertions(+), 688 deletions(-) create mode 100644 docs/computational/preprocessing_capture_area.md create mode 100644 docs/computational/preprocessing_openst_library.md delete mode 100644 docs/computational/preprocessing_sequencing.md delete mode 100644 docs/examples/adult_mouse/preprocessing_imaging.md delete mode 100644 docs/examples/e13_mouse/preprocessing_imaging.md create mode 100644 docs/static/img/adult_mouse_histology.webp diff --git a/docs/computational/generate_expression_matrix.md b/docs/computational/generate_expression_matrix.md index 14bf01b..fccb173 100644 --- a/docs/computational/generate_expression_matrix.md +++ b/docs/computational/generate_expression_matrix.md @@ -1,49 +1,58 @@ -# Generating a cell-by-gene matrix -After pairwise alignment, the same coordinate system is shared between the spatial barcodes and the -staining images. +# Segmentation and single-cell quantification +Once the ST and imaging modalities [have been aligned](pairwise_alignment.md#option-a-automated-alignment), +you can segment the images into single cells/nuclei, and then aggregate the spot locations into individual cells +for subsequent analysis. -However, analysis (e.g., clustering, pseudotime, DGE...) is performed on single cells, not on individual capture areas -(0.6 μm in the current version of the protocol). +## Cell segmentation from tissue image +Let's create a new Open-ST h5 object containing a cell-by-gene expression matrix. First, you will need a cell +(or nuclear) segmentation mask. -So, we show how to aggregate the $N\times G$ matrix ($N$ spots; $G$ genes) -into a $M\times G$ matrix ($M$ segmented cells; $G$ genes), where $N$ maps to $M$ via the segmentation mask. +```sh +openst from_spacemake \ + --project-id openst_demo_project \ + --sample-id openst_demo_sample \ + segment \ + --model HE_cellpose_rajewsky # default model for segmentation of H&E images +``` -## Segmentation of staining image -To create such a spatial cell-by-gene ($M\times G$) expression matrix, you will first need a segmentation mask. +=== "From (semi)automatic alignment" -We efficiently segment cells (or nuclei) from staining images using [cellpose](https://github.com/MouseLand/cellpose). -We provide a model that we fine-tuned for segmentation of fresh-frozen, H&E-stained tissue, -[here](http://bimsbstatic.mdc-berlin.de/rajewsky/openst-public-data/models/HE_cellpose_rajewsky). -You can specify any other model that works best for your data - -refer to the [cellpose](https://cellpose.readthedocs.io/en/latest/index.html) documentation. + ```sh + openst from_spacemake \ + --project-id openst_demo_project \ + --sample-id openst_demo_sample \ + segment \ + --model HE_cellpose_rajewsky \ + --image-in uns/spatial_pairwise_aligned/staining_image_transformed + ``` -```sh -openst segment \ - --h5-in spatial_stitched_spots.h5ad \ # after running the pairwise alignment - --image-in uns/spatial/staining_image \ - --mask-out uns/spatial/staining_image_mask \ - --model HE_cellpose_rajewsky - # --device cuda \ # uses GPU for segmentation, if available - # --chunked \ # specify if you run out of GPU memory - segments in chunks -``` -By default, segmentation is extended radially 10 pixels. This can be changed with the argument `--dilate-px`. +=== "From manual alignment" -Make sure to populate the arguments with the values specific to your dataset. Here, we provide `--h5-in` consistent -with the previous steps, `--image-in` and `--mask-out` will read and write the staining and mask inside the Open-ST h5 object, -and `--model` is `HE_cellpose_rajewsky`, the default used in our manuscript. This is the model we recommend for H&E images, and -weights are automatically downloaded. It is also [provided in our repo](http://bimsbstatic.mdc-berlin.de/rajewsky/openst-public-data/models/HE_cellpose_rajewsky). -The rest of parameters can be checked with `openst segment --help`. + ```sh + openst from_spacemake \ + --project-id openst_demo_project \ + --sample-id openst_demo_sample \ + segment \ + --model HE_cellpose_rajewsky + ``` -!!! tip - **If your sample also contains very large cells** (e.g., adipocytes) that are not segmented with the previous parameters, - you can perform a second segmentation with a cellpose model, adjusting the diameter parameter. +We segment cells (or nuclei) from staining images using [cellpose](https://github.com/MouseLand/cellpose). +We provide a model that we fine-tuned for segmentation of fresh-frozen, H&E-stained tissue, +[here](http://bimsbstatic.mdc-berlin.de/rajewsky/openst-public-data/models/HE_cellpose_rajewsky), but you can use +any other model (e.g., pretrained from cellpose, like `cyto2` or `nuclei`, or your own). +Also, by default, segmentation is extended radially 10 pixels (see `--dilate-px`), to account for cytoplasm surrounding +the nucleus as a first approximation of cell shape (might hold or not depending on the tissue). + +??? question "I want to segment very small and very large cells..." + + You can perform an additional round of segmentation by, e.g., adjusting the diameter parameter. ```sh - openst segment \ - --h5-in spatial_stitched_spots.h5ad \ - --image-in uns/spatial/staining_image \ + openst from_spacemake \ + --project-id openst_demo_project \ + --sample-id openst_demo_sample \ + segment \ --mask-out uns/spatial/staining_image_mask_large \ - --model HE_cellpose_rajewsky \ --dilate-px 50 \ --diameter 50 # diameter for the larger cell type ``` @@ -61,41 +70,81 @@ The rest of parameters can be checked with `openst segment --help`. --mask-out uns/spatial/staining_image_mask_combined ``` -You can assess the quality of the segmentation mask using `openst preview`, which leverages `napari` for visualization: +## Quality control of segmentation +You can assess the quality of segmentation with `openst preview`: -```sh -openst preview \ - --h5-in spatial_stitched_spots.h5ad \ - --image-key uns/spatial/staining_image uns/spatial/staining_image_mask -``` +=== "From (semi)automatic alignment" + + ```sh + openst from_spacemake \ + --project-id openst_demo_project \ + --sample-id openst_demo_sample \ + preview \ + --image-key uns/spatial_pairwise_aligned/staining_image_transformed uns/spatial/staining_image_mask + ``` + +=== "From manual alignment" + + ```sh + openst from_spacemake \ + --project-id openst_demo_project \ + --sample-id openst_demo_sample \ + preview \ + --image-key uns/spatial/staining_image uns/spatial/staining_image_mask + ``` -This will create a `napari` window with two image layers. We recommend changing the mask _image_ layer into a +This will create a `napari` window with two image layers. Change the mask _image_ layer into a [_label_ layer](https://napari.org/stable/howtos/layers/labels.html), which is designed for _displaying each integer (ID from the segmentation mask) as a different random color, with background rendered as transparent_. -## Assigning transcripts to segmented cells -Now, we aggregate the initial $N\times G$ matrix into an $M\times G$ matrix, -where $N$ maps to $M$ via the segmentation mask. +If you are satistied with the quality of the segmentation, **you are all set to continue with single-cell quantification**. -This step allows you to associate capture spots with segmented cells. +## Single-cell quantification -```sh -openst transcript_assign \ - --h5-in spatial_stitched_spots.h5ad \ - --spatial-key obsm/spatial_pairwise_aligned_fine \ - --mask-in uns/spatial/staining_image_mask \ - --h5-out spatial_per_segmented_cell.h5ad -``` +Then, you can create a single file containing the transcriptomic information aggregated into (segmented) single-cells. + +=== "From automatic alignment" -In this case, the argument `--mask-in` was set to a single mask, but it can be set to the previously -introduced _combined_ masks (e.g., `--mask-in uns/spatial/staining_image_mask_combined`). + ```sh + openst from_spacemake \ + --project-id openst_demo_project \ + --sample-id openst_demo_sample \ + transcript_assign \ + --spatial-key obsm/spatial_pairwise_aligned_fine \ + --image-key uns/spatial_pairwise_aligned/staining_image_transformed + ``` + +=== "From semiautomatic alignment" + + ```sh + openst from_spacemake \ + --project-id openst_demo_project \ + --sample-id openst_demo_sample \ + transcript_assign \ + --spatial-key obsm/spatial_manual_fine \ + --image-key uns/spatial_pairwise_aligned/staining_image_transformed + ``` + +=== "From manual alignment" + + ```sh + openst from_spacemake \ + --project-id openst_demo_project \ + --sample-id openst_demo_sample \ + transcript_assign \ + --spatial-key obsm/spatial_manual_fine + ``` ## Expected output -After running the steps above, you will have a single `h5ad` file, containing the transcriptomic information per segmented cell, -with spatial coordinates compatible with the staining image. The staining image and the segmented image are provided in this object, +After the steps above, you will have a single `h5ad` file with transcriptomic information per segmented cell, +with spatial coordinates aligned to the staining image. The staining image and the segmented image are provided in this object, so it is possible to visualize it with [squidpy](https://github.com/scverse/squidpy) or [spatialdata](https://github.com/scverse/spatialdata), among other tools. -So, this concludes the preprocessing of 2D spatial transcriptomics and imaging data +!!! warning + In the Open-ST h5 object, the cell with ID 0 will correspond to the background. Please remove it before + proceeding with analysis. + +This concludes the preprocessing of 2D spatial transcriptomics and imaging data of the Open-ST protocol. Next steps include 3D reconstruction, and downstream analysis of nD data. \ No newline at end of file diff --git a/docs/computational/getting_started.md b/docs/computational/getting_started.md index 653cf70..b891a45 100644 --- a/docs/computational/getting_started.md +++ b/docs/computational/getting_started.md @@ -2,28 +2,19 @@ After folowing the [experimental protocol](../experimental/getting_started.md), we provide the [`openst`](https://pypi.org/project/openst/) python package for transforming the raw sequencing data into objects that can be used for spatial, single-cell -analysis. - -More specifically, our pipeline consists of the following steps: +analysis, in four steps: 1. [Preprocessing of sequencing](preprocessing_sequencing.md) -2. [Preprocessing of imaging](preprocessing_imaging.md) -3. [Align image to transcriptome](pairwise_alignment.md): the spatial coordinates of transcripts are aligned - to the imaging modality. -4. [Generating a cell-by-gene matrix](generate_expression_matrix.md): transcripts - are quantified per cell using the segmentation information. -5. [3D reconstruction](threed_reconstruction.md) of tissue imaging and transcriptome from serial sections. +2. [Pairwise alignment](pairwise_alignment.md): the spatial coordinates of transcriptomics data are aligned + to tissue imaging. +3. [Segmentation and single-cell quantification](generate_expression_matrix.md): transcriptomic data + are aggregated into single cells using the information from cell segmentation of tissue images. +4. [3D reconstruction](threed_reconstruction.md) of tissue imaging and transcriptome from serial sections. *We provide tutorials for interactive visualization of 3D data.* -If you're familiar with Python, you can install `openst` with [pip], the Python package manager. -If not, we recommend using [docker]. - -[pip]: #with-pip -[docker]: #with-docker - ## Installation -### with pip recommended +### with pip, recommended The computational tools of the Open-ST workflow are published as a [Python package] and can be installed with `pip`, ideally by using a [virtual environment]. @@ -66,6 +57,16 @@ pip install openst pip install napari ``` +### from git + +`openst` can be directly installed from the source [GitHub repository]: +``` +git clone https://github.com/rajewsky-lab/openst.git +pip install -e openst +``` + + [GitHub repository]: https://github.com/rajewsky-lab/openst + ### with docker The official [Docker image] is a great way to get up and running in a few @@ -107,22 +108,4 @@ Now, you can execute PyQt5-based applications, and the GUI will be displayed on xhost - ``` - [Docker image]: https://hub.docker.com/r/rajewsky/openst/ - -### with git - -`openst` can be directly used from [GitHub] by cloning the -repository into a subfolder of your project root which might be useful if you -want to use the very latest version: - -``` -git clone https://github.com/rajewsky-lab/openst.git -``` - -Next, install the theme and its dependencies with: - -``` -pip install -e openst -``` - - [GitHub]: https://github.com/rajewsky-lab/openst \ No newline at end of file + [Docker image]: https://hub.docker.com/r/rajewsky/openst/ \ No newline at end of file diff --git a/docs/computational/pairwise_alignment.md b/docs/computational/pairwise_alignment.md index 97cd0d9..777869c 100644 --- a/docs/computational/pairwise_alignment.md +++ b/docs/computational/pairwise_alignment.md @@ -1,106 +1,325 @@ -# Align image to transcriptome -In the previous step, the transcriptomic reads were processed and mapped in tissue space with spacemake. -Now, in order to assign transcripts to cells from the staining images, we perform a pairwise alignment -between the imaging and spatial transcriptomics modality. - -`openst` provides tools to automatically or manually carry out this pairwise alignment operation. For this, -our tool generates *pseudoimages* of the spatial transcriptomics data, such that computer vision algorithms -can be used for the alignment of true staining images and the ST modalities. - -The alignment workflow consists of two steps, that can be performed [automatically] or [manually]: - -1. Coarse alignment of H&E images to pseudoimages of ST data - at low resolution. -2. Fine alignment, using fiducial marks detected at both modalities for very precise alignment. - -[automatically]: #automated-workflow -[manually]: #manual-workflow - -## Required input data -For automatic and manual alignment, two inputs are required: (1) a stitched tile-scan of -the staining image (see [Preprocessing of imaging](preprocessing_imaging.md)), and (2) a -single [h5ad] file containing all the [barcoded tiles](preprocessing_sequencing.md#flow-cell-related-terms) of a sample -(see [Preprocessing of sequencing](preprocessing_sequencing.md)). - -!!! warning - Remember that spacemake generates one file per [barcoded tile](preprocessing_sequencing.md#flow-cell-related-terms), - and it will be necessary to perform [stitching of tiles](preprocessing_sequencing.md) to - obtain a single file with all tiles for a sample. This is done with `openst spatial_stitch`. - -### Image modality -The expected input for the image modality is a single tiff file of the whole tile-scan. -Other formats, i.e., `jpeg` and `png` are also compatible. Make sure that the image is the full-resolution -and not a downsampled version. As a rule of thumb, at least a few fiducial markers must be clearly distinguishable. - -=== "Good visibility" - ![Good visibility](../static/img/compu_pairwise_align_fiducial_visibility/good_visibility.webp){ loading=lazy } - -=== "_Fair_ visibility" - ![Fair visibility](../static/img/compu_pairwise_align_fiducial_visibility/fair_visibility.webp){ loading=lazy } - -=== "_Poor_ visibility" - ![Poor visibility](../static/img/compu_pairwise_align_fiducial_visibility/poor_visibility.webp){ loading=lazy } - -### Spatial transcriptomics modality -The expected input for the spatial transcriptomics modality is a single [h5ad] file containing all -the [barcoded tiles](preprocessing_sequencing.md#flow-cell-related-terms) of a sample. - -!!! warning - Remember that spacemake generates one file per [barcoded tile](preprocessing_sequencing.md#flow-cell-related-terms), and it will be necessary to perform - [stitching of tiles](preprocessing_sequencing.md#global-spatial-coordinates-tile-stitching) to obtain a single file with all tiles for a sample. - -The structure of this single file must follow the [h5ad] standard. In particular, we expect that there is a -column under `obs` called `tile_id`, telling which tile a barcode belongs to. Also, we expect a column -`total_counts` indicating the total count of unique transcripts per barcode. Finally, we expect that there is -a barcodes-by-2d matrix under `obsm` (with the name `spatial`), containing the spatial coordinates of the barcoded -spots. - -[h5ad]: https://anndata.readthedocs.io/en/latest/fileformat-prose.html - -## Automated workflow -If you want to save time, we provide a script that performs coarse and fine steps of alignment -automatically, by leveraging computer vision algorithms. To do so, make sure that you have the [necessary -input data](#required-input-data); then, open a terminal, type and run the following command (just an example): - -```bash -# Add the image data to the Open-ST h5 object -openst merge_modalities \ - --h5-in spatial_stitched_spots.h5ad \ - --image-in Image_Stitched_Composite.tif - -# Run the automatic pairwise alignment -openst pairwise_aligner \ - --h5-in spatial_stitched_spots.h5ad \ - --metadata alignment_metadata.json - # --device cuda # by default, cpu. Only specify if you have cuda-compatible GPU - # --only-coarse # does not run the fine registration; can be done manually with the GUI +# Pairwise alignment +In the previous step, you processed and mapped transcriptomic reads into spatial coordinates with `spacemake`. +Before aggregating transcriptomic information per cell, we need to align this transcriptomic data into tissue space, +leveraging imaging data. + +`openst` can [automatically] (or [manually]) perform pairwise alignment of transcriptomic and imaging modalities, +via advanced computer vision algorithms. Alignment is performed in two steps: + +1. Coarse (low-resolution) of H&E images to pseudoimages of ST data, to **roughly align the transcriptome to the tissue**. +2. Fine (high-resolution), using **fiducial marks** detected at both modalities for **sub-micron accurate alignment**. + +[automatically]: #option-a-automated-alignment +[manually]: #option-b-manual-alignment + +## Data required +Two data modalities are used for pairwise alignment: + +1. A single Open-ST h5 object (from [`spacemake`](preprocessing_openst_library.md)) containing all the spatially-resolved locations of a sample. +2. A single, high-resolution image of the capture area and tissue + +### Tissue transcriptome +If you ran `spacemake` with a `puck` with a `coordinate_system`, **and** a `run_mode` without meshing, e.g.: + +```yaml +pucks: + openst: + coordinate_system: puck_data/openst_coordinate_system.csv + spot_diameter_um: 0.6 + width_um: 1200 +... +run_modes: + openst: + clean_dge: false + count_intronic_reads: true + count_mm_reads: true + detect_tissue: false + mesh_data: false + mesh_type: 'hexagon' + n_beads: 100000 + polyA_adapter_trimming: true + spatial_barcode_min_matches: 0.1 ``` -Make sure to specify a path where the metadata output file -should be created via `--metadata`, for later visual assessment of the alignment. +Then you are **all set to proceed** with the [Tissue image](#tissue-image) preprocessing (unless you had the issues below). -If you want to run only the coarse phase of the pairwise alignment (i.e., to run the fine -alignment [yourself](#manual-workflow)), you can specify the argument `--only-coarse`. +??? question "My `run_mode` and/or `puck` do not look like that" -!!! note - For aligning STS to H&E-stained tissues, **we recommend** leaving the arguments with the default values. - Anyway, you can get a full list of configurable parameters by running `openst pairwise_aligner --help`. + It is likely that you didn't specify a `coordinate_system` or you only have `mesh`(ed) Open-ST h5 objects, + therefore you need to generate a `puck_collection` Open-ST h5 object, manually: -!!! tip - Right after automatic alignment and before - proceeding to segmentation and aggregating into a cell-by-gene matrix, - **we strongly recommend** visually assessing the alignment results. Specifically, that - the tissue is overall well-aligned in both modalities, and that the fiducial markers are - overlapping across all tiles. + ```sh + openst from_spacemake \ + --project-id openst_demo_project \ + --sample-id openst_demo_sample \ + spatial_stitch \ + --tile-coordinates fc_1_coordinate_system.csv + ``` + + Please replace `--tile-coordinates` with the adequate coordinate_system for your barcoded flow cell. + This one is available under the [examples section](https://rajewsky-lab.github.io/openst/latest/examples/datasets) + + If you want to select only specific tiles for a sample, e.g., to exclude some spurious tiles, + you can `puck_collection` files fully manually, without the `from_spacemake` command: + + ```sh + openst spatial_stitch \ + --tiles dge.fc_1_tile_2267.h5ad dge.fc_1_tile_2268.h5ad \ + --tile-id fc_1_tile_2267 fc_1_tile_2268 \ + --tile-coordinates fc_1_coordinate_system.csv \ + --h5-out spatial_stitched_spots.h5ad + ``` + +### Tissue image +The tissue transcriptome data you just preprocessed will be aligned into a tissue (hi-res) image. +For this, `openst` expects a single, high-resolution `tiff` image +where the tissue is visible (and preferrably at least a small part of the capture area, too). + +Expand one of the cases below to decide how to proceed with image data: +??? question "My microscope gives a single, hi-res image" + Fantastic! Just copy it to the relevant `spacemake` subfolder for the specific project and sample, + for instance: + + ```sh + spacemake_folder + `-- projects + `-- + `-- processed_data + `-- + `-- imaging # you might need to create + `-- Image_Stitched_Composite.tif # copy with this name + ``` + + **Make sure** to copy the image to that location, with the name `Image_Stitched_Composite.tif`. + ```bash + projects/openst_demo_project/processed_data/openst_demo_sample/imaging/Image_Stitched_Composite.tif + ``` + + +??? question "My microscope doesn't give a single image, but many _tile_ files" + When performing imaging, i.e. as tile-scans, you might need to assemble a single, + large image from a collection of smaller tiles (fields-of-view, FOVs). + + We provide tools to perform tile stitching from raw data with the microscopy setup showcased in + [our implementation](../experimental/library_preparation.md#he-staining-and-imaging) (a Keyence + BZ-X710 inverted fluorescence phase contrast microscope). If you use a different microscope setup, + please refer to the documentation of your microscope for how to stitch tile-scans into a single image. + + For this keyence microscope, copy the raw data (including `*.tiff` and `.bcf` files) into the + `spacemake` folder specific for that project and sample: + + ```sh + spacemake_folder + `-- projects + `-- + `-- raw_data + `-- imaging # you might need to create it + `-- + |-- Image.bcf + `-- *.tif + ``` + + Then, run: + ```bash + openst image_stitch \ + --microscope='keyence' \ + --imagej-bin= \ + --image-indir=projects//raw_data/imaging/ \ + --image-out=projects//processed_data//imaging/Image_Stitched_Composite.tif + ``` + + This leverages the Grid/Collection stitching plugin included in [Fiji](https://imagej.net/software/fiji/downloads) + to create a single, composite tile-scan image. Replace `` with the path where the + [Fiji](https://imagej.net/software/fiji/downloads) executable is. + + !!! question + If you don't know how to specify the ``, please follow the official instructions provided + for [Running Headless](https://imagej.net/learn/headless). For example, under linux you can download it using + + ```bash + cd ~ + wget "https://downloads.imagej.net/fiji/latest/fiji-linux64.zip" + unzip fiji-linux64.zip + # then, will be "~/Fiji.app/ImageJ-linux64" + ``` + + !!! note + If your imaging setup consisted of a Z-stack, each tile image will likely be a FOV and a specific Z plane + (e.g., file names like Image_00001_Z001.tif, Image_00001_Z002.tif,...). In this case, you can specify a + regular expression (regex) to parse the Z plane from the file names, and a single file per FOV + will be created before stitching. + + This can be specified in `openst image_stitch` with the argument `--join-zstack-regex` (empty, by default) + +Either way, `openst` expects a +single `.tif` file at `projects//processed_data//imaging/Image_Stitched_Composite.tif`. + +Unless you had the issue below... + +??? warning "The quality of images is suboptimal (e.g., for segmentation)" + Most of the times, large tile-scans may have uneven illumination, focus or noise. This can be challenging for downstream + processing, like segmentation, feature extraction or quantification (i.e., of fluorescence images). + There is a plethora of methods to address these issues (e.g., [Flatfield Correction](https://imagej.net/plugins/bigstitcher/flatfield-correction) + from *BigStitcher*, or [CARE](https://imagej.net/plugins/care), to name some). This might be highly dependent on your microscope, + imaging settings, sample type, sample width... **Always look at your images** so you can take an informed decision. + + In our publication, we leveraged a [CUT](https://github.com/taesungp/contrastive-unpaired-translation) model that allowed + to homogeneize the *style* of the whole tile-scan - that is, reduce possible biases in illumination, noise and focus across the entire + tile-scan. You can run this by running the following command on the stitched image. + + ![CUT restoration showcased in the paper](../static/img/compu_image_preprocessing/preprocessing_CUT.webp){ loading=lazy } + + ```bash + openst image_preprocess \ + --image-in Image_Stitched_Composite.tif \ + --image-out Image_Stitched_Composite_Restored.tif + # --device cuda # in case you have a CUDA-compatible GPU + ``` + + If you ran `openst merge_modalities`, then imaging data will be contained inside the Open-ST h5 object, and the command + can be adapted: + + ```bash + openst image_preprocess \ + --h5-in multimodal/spots_stitched.h5ad # just a placeholder, adapt + # --device cuda # in case you have a CUDA-compatible GPU + ``` + + By default, the image will be loaded from the key `uns/spatial/staining_image`, and the CUT-restored image will be saved + to `uns/spatial/staining_image_restored`. You can preview the image restoration results using: + + ```bash + openst from_spacemake \ + --project-id openst_demo_project \ + --sample-id openst_demo_sample \ + --image-key uns/spatial/staining_image uns/spatial/staining_image_restored + ``` + + This will load the two images and visualize it using `napari`. Later, you can run segmentation and pairwise alignment + using either the default merged image (`uns/spatial/staining_image`), or the restored image (`uns/spatial/staining_image_restored`). + Always assess these preprocessing choices (quantitatively and qualitatively) to decide whether these make sense for your data. + + Once the single image has been generated, make sure that the image is the full-resolution + and not a downsampled version. As a rule of thumb, at least a few fiducial markers must be clearly distinguishable. + +... you are **all set to proceed with pairwise alignment**. + +## Merging data modalities +Once the ST and imaging data have been stitched, you can create a single object containing both modalities: + +```sh +openst from_spacemake \ + --project-id openst_demo_project \ + --sample-id openst_demo_sample \ + merge_modalities +``` + +## Pairwise alignment +From the merged object you created above, you can do pairwise alignment of the transcriptomic +data into the tissue image automatically, semiautomatically, or fully manually: + +=== "Automatic (or semiautomatic) alignment" + + For aligning transcriptomic data into H&E-stained tissue sections, **we recommend** leaving + the rest of arguments with default parameters - you can get a full list of configurable parameters + by running `openst pairwise_aligner --help`. + + ``` sh + openst from_spacemake \ + --project-id openst_demo_project \ + --sample-id openst_demo_sample \ + pairwise_aligner + # --metadata alignment.json # to create a visual report + # --only-coarse for semiautomatic + ``` + + When running automatic alignment, the spatial keys will be: + 1. `obsm/spatial`: the original (unaligned) coordinates + 2. `obsm/spatial_pairwise_aligned_coarse`: after low-res alignment + 3. `obsm/spatial_pairwise_aligned_fine`: after hi-res refinement + + Similarly, the aligned image will be saved under `uns/spatial_pairwise_aligned/staining_image_transformed`. + + Therefore, if you want to visualize with the GUI or perform manual fine-tuning of the alignment, you + need to specify the correct `--spatial-key` and `--image-key`: + + ```bash + # for visualization and/or manual fine-tuning + openst from_spacemake \ + --project-id openst_demo_project \ + --sample-id openst_demo_sample \ + manual_pairwise_aligner \ + --spatial-key obsm/spatial_pairwise_aligned_fine \ + --image-key uns/spatial_pairwise_aligned/staining_image_transformed + + # if you want to perform semiautomatic alignment, + # continue with openst manual_pairwise_aligner + # see "Manual alignment" tab + ``` + + Also, in some of the following steps (e.g., [segmentation](generate_expression_matrix.md)), make sure to populate + the `--image-key` and `--spatial-key` arguments with the correct values. + +=== "Manual alignment" + We provide a Graphical User Interface (GUI) for selecting keypoints between imaging & ST modalities, + for full manual alignment or refinement of automatic results. This GUI requires a single Open-ST h5 object + (after spatial stitching). There are two kinds of workflow: + + ``` sh + openst from_spacemake \ + --project-id openst_demo_project \ + --sample-id openst_demo_sample \ + manual_pairwise_aligner \ + --spatial-key obsm/spatial \ + --image-key uns/spatial/staining_image + ``` + + In the GUI, you first perform _coarse_ alignment (**not** tile by tile), and then click 'Apply to data'. + Save the transformed coordinates as `obsm/spatial_manual_coarse`. Then, re-render with with spatial key, + select at least 3 corresponding keypoints per tile, and save a keypoints file, e.g., as `keypoints.json`. + + Then you can apply the transformation, per tile, separately: + + ```bash + openst from_spacemake \ + --project-id openst_demo_project \ + --sample-id openst_demo_sample \ + apply_transform \ + --keypoints-in keypoints.json \ + --spatial-key-in obsm/spatial_manual_coarse \ + --spatial-key-out obsm/spatial_manual_fine \ + --per-tile + ``` + + This will save the refined (similar to automatic hi-res alignment) coordinates into `obsm/spatial_manual_fine`. + The image data will still be (by default) under `uns/spatial/staining_image`. Make sure to populate `--image-key` + and `--spatial-key` arguments in later steps with the correct values. + + We provide a video tutorial showcasing the GUI, with an illustrative example + of refinement from (only coarse) automatic alignment. + + --- + + :fontawesome-brands-youtube:{ style="color: #EE0F0F" } + __[Walkthrough of the GUI for manual alignment]__ by @danilexn – :octicons-clock-24: + 5m – Learn how to visualize and align STS and imaging data in a step-by-step guide. + + [Walkthrough of the GUI for manual alignment]: https://youtu.be/Fjh4huosVPY + + --- + +Now you are **all set to proceed with the visual (qualitative) assessment of the alignment** (unless you had the issues below). + +??? question "The coarse (or fine) alignment didn't work" + There are several parameters that you can change. -!!! tip - If the coarse (or fine) registration doesn't work, there are several parameters that you can change. The most important ones are: - `--rescale-factor-coarse` or `--rescale-factor-fine`: the highest, the lower resolution the image; thus, more _global_ features are used for registration - `--threshold-counts-coarse` or `--threshold-counts-fine`: the highest, the less spots there will be on the image These allow to increase the number of matches and possibly the number of RANSAC inliers. There are other RANSAC-specific parameters that can be changed, such as `--ransac-coarse-residual-threshold`, `--ransac-coarse-max-trials` and `--ransac-fine-min-samples`. For more parameters, check `openst pairwise_aligner --help`. -!!! warning +??? warning "I get an error: 'This application failed to start [...]'" In some environments, the following error might happen: ```sh @@ -113,16 +332,17 @@ alignment [yourself](#manual-workflow)), you can specify the argument `--only-co This can be solved by removing the files inside `/home/user/miniconda3/envs/openst/lib/python3.11/site-packages/cv2/qt/plugins`, e.g. see this [StackOverflow thread](https://stackoverflow.com/questions/71029300/python-pyqt-xcb-plugin-with-thread-error) - -### Visual assessment with report -You can generate an HTML report that contains a qualitative summary of the alignment (images, parameters...) +## Visual assessment of alignment +### With HTML report +With automatic alignment, if you specified the `--metadata` argument, you can generate an HTML report +showing a qualitative summary of the alignment (images, parameters...) ```sh -openst report --metadata=alignment_metadata.json --html-out=alignment_report.html +openst report --metadata=alignment.json --html-out=alignment_report.html ``` -### Visual assessment with GUI -Alternatively, you can visualize the images & ST data interactively using the GUI. +### With interactive GUI +Alternatively, you can visualize the images & ST data interactively using the integrated interactive GUI. ```sh openst manual_pairwise_aligner @@ -132,62 +352,7 @@ We provide a Graphical User Interface (GUI) for selecting keypoints between imag for visualization and refinement of automatic results. This GUI requires a single Open-ST h5 object, the output of `openst pairwise_aligner`. - - -## Manual/semiautomatic workflow -We provide a Graphical User Interface (GUI) for selecting keypoints between imaging & ST modalities, -for full manual alignment or refinement of automatic results. This GUI requires a single Open-ST h5 object -(after spatial stitching). There are two kinds of workflow: - -=== "Fully manual alignment" - - ``` sh - # Add the image data to the Open-ST h5 object - openst merge_modalities \ - --h5-in spatial_stitched_spots.h5ad \ - --image-in Image_Stitched_Composite.tif - - # Use the GUI to select the keypoints.json file - openst manual_pairwise_aligner - - # Compute a rigid transformation from keypoints.json and apply it to the data - openst apply_transform \ - --keypoints-in keypoints.json \ - --h5-in spatial_stitched_spots.h5ad \ - --spatial-key-in 'obsm/spatial' \ - --spatial-key-out 'obsm/spatial_manual_transformed' - ## --per-tile # when specified, there's one rigid transform per tile - ``` - -=== "Semiautomatic alignment" - - ``` sh - # Use the GUI to select the keypoints.json file - # For per-tile alignment, select keypoints per tile (see video below) - openst manual_pairwise_aligner - - # Compute a rigid transformation from keypoints.json and apply it to the data - openst apply_transform \ - --keypoints-in keypoints.json \ - --h5-in spatial_stitched_spots.h5ad \ - --spatial-key-in 'obsm/spatial_pairwise_aligned_fine' \ - --spatial-key-out 'obsm/spatial_pairwise_aligned_refined' - ## --per-tile # when specified, there's one rigid transform per tile - ``` - -We provide a video showcasing the GUI, with an illustrative example of refinement from (coarse) automated alignment. - ---- - -:fontawesome-brands-youtube:{ style="color: #EE0F0F" } -__[Walkthrough of the GUI for manual alignment]__ by @danilexn – :octicons-clock-24: -5m – Learn how to visualize and align STS and imaging data in a step-by-step guide. - - [Walkthrough of the GUI for manual alignment]: https://youtu.be/Fjh4huosVPY - ---- - ## Expected output -After running the automatic or manual alignment, you must have a single `h5ad` file, containing the transformed spatial coordinates. +After automatic or manual alignment, you will have a single `h5ad` file, containing the transformed spatial coordinates. This will be used in the following step to aggregate the transcripts by a spatially-corresponding cell, in order to get a cell-by-gene matrix that can be used in later downstream analysis. diff --git a/docs/computational/preprocessing_capture_area.md b/docs/computational/preprocessing_capture_area.md new file mode 100644 index 0000000..0390ed1 --- /dev/null +++ b/docs/computational/preprocessing_capture_area.md @@ -0,0 +1,102 @@ +# Preprocessing capture area library +After [sequencing the Open-ST capture areas](../experimental/capture_area_generation.md), you will get basecall files in `bcl` format, or raw reads in `fastq` +format (see [sequence file formats](https://www.illumina.com/informatics/sequencing-data-analysis/sequence-file-formats.html) +from Illumina's website). + +We have designed a simple computational workflow to transform the raw `bcl` or `fastq` files +from the sequencing of the barcoded library into table-like files (`csv`, or `tsv`) with contain the following +information: + +|cell_bc|x_pos|y_pos| +|----|----|----| +|CGCGAGGGGAAAATGGGGACTAGCG|6343|1016| +|GGTCCCGTCCAAGAAGTAAATCGAA|9272|1016| +|...|...|...| + +Where `cell_bc` is the 32 nucleotide-long spatial barcode, and `x_pos`/`y_pos` are 2D spatial coordinates +of a specific **tile** in the capture area (see below). + +Before diving into the code, let's clarify some of the +[terms](#flow-cell-related-terms) that are specific to using Illumina flow cells as capture areas. We quote from +[Illumina's documentation](https://support-docs.illumina.com/IN/NextSeq_550-500/Content/IN/NextSeq/FlowCell_Tiles_fNS.htm) + +### Flow cell-related terms +!!! info "Tiles" + "Small imaging areas on the flow cell defined as the field of view by the + camera. The total number of tiles depends on the number of lanes, swaths, and surfaces that are imaged on + the flow cell, and how the cameras work together to collect the images." + +!!! info "Lane" + "A physical channel with dedicated input and output ports." + +!!! info "Top/bottom" + "The flow cell is imaged on two surfaces, the top and bottom. + The top surface of 1 tile is imaged, then the bottom surface of the same tile is imaged before moving to the next tile." + +!!! info "Swath" + "A column of tiles in a lane." + +### Retrieve spatial barcodes coordinates for one tile +The `x_pos` and `y_pos` coordinates from the table above are given for each tile, separately. This information is +encoded in the `bcl` and `fastq` files. To obtain per-tile barcodes and coordinates, run the following code: + + +```sh +openst barcode_preprocessing \ + --fastq-in \ + --tilecoords-out \ + --out-suffix \ + --out-prefix \ + --crop-seq \ + --rev-comp \ + --single-tile +``` + +Make sure to replace the placeholders: +`` to the `fastq` file of a specific tile; `` where the table-like +files will be written; `` and `` are suffixes and prefixes that are added to the tile file names; +`` from the `--crop-seq` argument is a string in the [Python slice](https://docs.python.org/3/tutorial/datastructures.html) format +(e.g., 2:32 will take nucleotides 2nd until 32th of the sequence in the `fastq` file); `--rev-comp` is provided whether the barcode sequences +must be written into the `csv` as their reverse-complementary; `--single-tile` argument is provided when the `fastq` file only contains data for +a single tile (**our recommendation**). + +### Retrieve spatial barcodes coordinates for all tiles +Above you generated a single tile coordinate. To process all tiles from a flow cell (in parallel), you can run the +following snippets for Linux, assuming you have access to the basecalls folder. + +First create a `lanes_and_tiles.txt` file: + +```sh +cat RunInfo.xml | grep "" | sed 's/ *//' | sed 's/<\/Tile>//' | sed 's/^[ \t]*//;s/[ \t]*$//' > lanes_and_tiles.txt +``` + +where `RunInfo.xml` is a file contained in the basecalls directory. + +Then, run demultiplexing and conversion to `fastq` +simultaneously to generating the barcode spatial coordinate file: + +```sh +cat lanes_and_tiles.txt | xargs -n 1 -P -I {} \ + sh -c 'bcl2fastq -R --no-lane-splitting \ + -o /"{}" --tiles s_"{}"; \ + + openst barcode_preprocessing \ + --fastq-in /{}/Undetermined_S0_R1_001.fastq.gz \ + --tilecoords-out \ + --out-suffix .txt \ + --out-prefix "{}" \ + --crop-seq \ + --rev-comp \ + --single-tile' +``` + +Again, make sure to replace the placeholders: `` and `` are the directories where the +basecall files are contained and where the converted output `fastq` files will be saved; The rest of +arguments have the same meaning as above. If you generated full `fastq` yourself, you can adapt the command +above to remove the call to `bcl2fastq`. + +## Expected output + +After running all the steps of this section, you will have a folder with many `*.txt.gz` files +containing the spatial coordinates of flow cell tiles (you will only need to generate this once per flow cell), +in the tab format described above. \ No newline at end of file diff --git a/docs/computational/preprocessing_openst_library.md b/docs/computational/preprocessing_openst_library.md new file mode 100644 index 0000000..d41eaf3 --- /dev/null +++ b/docs/computational/preprocessing_openst_library.md @@ -0,0 +1,95 @@ +# Preprocessing transcriptomic library +After sequencing the [transcriptomic sequences of Open-ST library](../experimental/sequencing_of_openst_library.md#sequencing-of-Open-ST-library), +you will get basecall files in `bcl` format, or raw reads in `fastq` format +(see [sequence file formats](https://www.illumina.com/informatics/sequencing-data-analysis/sequence-file-formats.html) +from Illumina's website). + +For the Open-ST workflow, we leverage [`spacemake`](https://spacemake.readthedocs.io/en/latest/), an +an automated pipeline designed for the preprocessing, alignment, and quantification of single-cell and spatial transcriptomics data. + +We have developed computational tools for processing each of these sequencing rounds. +By processing (1), you will get a *database* of barcodes and their spatial locations. +This will used during the automated processing of (2), such that the transcriptome is mapped back to space. + +## Configuring `spacemake` +We refer to the [official documentation](https://spacemake.readthedocs.io/en/latest/) for a complete tutorial on how to +[install](https://spacemake.readthedocs.io/en/latest/install.html) and +[initialize](https://spacemake.readthedocs.io/en/latest/initialize.html) spacemake. + +Once installed, initialized and [species data have been added](https://spacemake.readthedocs.io/en/latest/config.html#configure-species), +an Open-ST sample can be added: + +```bash +spacemake projects add_sample \ + --project_id \ + --sample_id \ + --R1 \ # single R1 or several R1 files + --R2 \ # single R2 or several R2 files + --species \ + --puck openst \ + --puck_barcode_file \ + --run_mode openst \ + --barcode_flavor openst +``` + +The above will add a new Open-ST project with `barcode_flavor`, `run_mode`, `puck` all set to `openst`. + +??? question "How to populate `--puck_barcode_file`" + With Open-ST data, each sample covers a piece of capture area, which contains at least one tile (puck). + + Thus, we need to provide `--puck_barcode_file` (each tile in a sample has different barcodes, unlike for visium samples). + This file should be a comma or tab separated, containing column names as first row. Acceptable column names are: + + - `cell_bc`, `barcodes` or `barcode` for cell-barcode + - `xcoord` or `x_pos` for x-positions + - `ycoord` or `y_pos` for y-positions + + These are generated by the `openst` package as [previously described](preprocessing_capture_area.md). + + All `puck_barcode_files` generated in the [previous step](preprocessing_capture_area.md) need to be specified after + `--puck_barcode_file`, e.g., with the wildcards `path_to_puck_files/*.txt.gz`. + + To generate output files and reports only for the relevant tiles per sample, you can configure the variable + `spatial_barcode_min_matches` under `run_mode` (see [spacemake documentation](https://spacemake.readthedocs.io/en/latest/config.html#configure-run-modes)). + This represents the minimum proportion of spatial barcodes that a tile must have in common + with the sample transcriptomic data to be further included during quantification and downstream analysis. + + !!! tip + If some tiles are wrongly missing (present), this might be because the threshold was too high (low). + You can update the sample to add missing tiles (see [spacemake documentation](https://spacemake.readthedocs.io/en/latest/projects/index.html)). + Then, rerun spacemake by configuring `spatial_barcode_min_matches` to zero. + +## Running `spacemake` +After a sample is added spacemake can be run with: + +```bash +spacemake run --cores --keep-going +``` + +The `--keep-going` flag is optional, however it will ensure that spacemake runs all the jobs it can, +even if one job fails (this logic is directly taken from snakemake). + +## Expected output + +After running all the steps of this section, `spacemake` generates the following folder structure (e.g., for a single sample): +```sh +spacemake_folder +`-- projects + `-- + |-- processed_data + | `-- + | `-- illumina + | `-- complete_data + | |-- dge # folder, spatial gene expression as h5ad files + | |-- qc_sheets # folder, sequencing QC as HTML + | |-- automated_analysis # folder, automated analysis results as HTML + | `-- ... # intermediate output files and folders + `-- raw_data # folder, contains R1 and R2 reads (fastq) +``` + +Importantly for the `openst` pipeline are the `h5ad` file(s) per sample (under the `dge` folder), +which contain the gene expression and spatial coordinates of each barcoded spot. + +In the following sections, you will learn how to merge and align these with imaging data, to later aggregate +transcriptomic information into single cells, rather than using a more arbitrary regular binning of +spatial data into squares or hexagons (part of the output from `spacemake` for QC purposes). \ No newline at end of file diff --git a/docs/computational/preprocessing_sequencing.md b/docs/computational/preprocessing_sequencing.md deleted file mode 100644 index bb9cf43..0000000 --- a/docs/computational/preprocessing_sequencing.md +++ /dev/null @@ -1,178 +0,0 @@ -# Preprocessing sequencing data -After sequencing, you will get basecall files in `bcl` format, or raw reads in `fastq` -format (see [sequence file formats](https://www.illumina.com/informatics/sequencing-data-analysis/sequence-file-formats.html) -from Illumina's website). In the Open-ST experimental protocol, there are two points at which -sequencing is performed: (1) for obtaining the [sequences of the barcoded library](../experimental/capture_area_generation.md#sequencing-of-barcoded-library), -and (2) for obtaining the [transcriptomic sequences of Open-ST library](../experimental/sequencing_of_openst_library.md#sequencing-of-Open-ST-library). - -We have developed computational tools for processing each of these sequencing rounds. -By processing (1), you will get a *database* of barcodes and their spatial locations. -This will used during the automated processing of (2), such that the transcriptome is mapped back to space. - -## Processing the sequencing of barcoded library -We have designed a simple computational workflow that allows to transform the `bcl` or `fastq` files -from the sequencing of the barcoded library into table-like files (`csv`, or `tsv`) that contain the following -structure: - -|cell_bc|x_pos|y_pos| -|----|----|----| -|CGCGAGGGGAAAATGGGGACTAGCG|6343|1016| -|GGTCCCGTCCAAGAAGTAAATCGAA|9272|1016| -|...|...|...| - -Where `cell_bc` is the 32 nucleotide-long spatial barcode, and `x_pos`/`y_pos` are the 2d spatial coordinates -of a specific **tile** in the capture area (see below). Before diving into the code, let's clarify some of the -[terms](#flow-cell-related-terms) that are specific to using Illumina flow cells as capture areas. We quote from -[Illumina's documentation](https://support-docs.illumina.com/IN/NextSeq_550-500/Content/IN/NextSeq/FlowCell_Tiles_fNS.htm) - -### Flow cell-related terms -!!! info - **Tiles**: "small imaging areas on the flow cell defined as the field of view by the - camera. The total number of tiles depends on the number of lanes, swaths, and surfaces that are imaged on - the flow cell, and how the cameras work together to collect the images." - -!!! info - **Lane**: "a physical channel with dedicated input and output ports." - -!!! info - **Top/bottom**: "the flow cell is imaged on two surfaces, the top and bottom. - The top surface of 1 tile is imaged, then the bottom surface of the same tile is imaged before moving to the next tile." - -!!! info - **Swath**: "a column of tiles in a lane." - -### Computing barcodes and spatial coordinates of all tiles -The `x_pos` and `y_pos` coordinates from the table above are given for each tile, separately. This information is -encoded in the `bcl` and `fastq` files. To obtain per-tile barcodes and coordinates, run the following code: - - -```sh -openst barcode_preprocessing \ - --fastq-in \ - --tilecoords-out \ - --out-suffix \ - --out-prefix \ - --crop-seq \ - --rev-comp \ - --single-tile -``` - -Make sure to replace the placeholders (`<...>`). For instance, -`` is the full path to the `fastq` file of a specific tile; `` is the folder where the table-like -files will be written; `` and `` are suffixes and prefixes that are added to the tile file names; -`` from the `--crop-seq` argument is a string in the [Python slice](https://docs.python.org/3/tutorial/datastructures.html) format -(e.g., 2:32 will take nucleotides 2nd until 32th of the sequence in the `fastq` file); `--rev-comp` is provided whether the barcode sequences -must be written into the `csv` as their reverse-complementary; `--single-tile` argument is provided when the `fastq` file only contains data for -a single tile (**our recommendation**). - -The code above will generate a file in `" | sed 's/ *//' | sed 's/<\/Tile>//' | sed 's/^[ \t]*//;s/[ \t]*$//' > lanes_and_tiles.txt -``` - -where `RunInfo.xml` is a file contained in the basecalls directory. *We don't endorse parsing xml like this, but -this code snippet works* 🙈. Then, you can process various `fastq` files in the basecalls directory as follows: - -```sh -cat lanes_and_tiles.txt | xargs -n 1 -P -I {} \ - sh -c 'openst barcode_preprocessing \ - --fastq-in /{}/Undetermined_S0_R1_001.fastq.gz \ - --tilecoords-out \ - --out-suffix .txt \ - --out-prefix "{}" \ - --crop-seq \ - --rev-comp \ - --single-tile' -``` - -Make sure to replace the placeholders (`<...>`). For instance, -`` is the number of parallel processes that will be spawned (recommended: less than the number -of cores in your machine); `` is the subdirectory of the basecalls directory where `fastq` files are contained; -`` is the prefix added to the file names (e.g., fc_1 as an internal unique identifier for a flow cell, -so you can keep track when having more than one flow cell). - -Otherwise, if you start from `bcl` files (raw basecalls), you can run demultiplexing and conversion to `fastq` -simultaneously to generating the barcode spatial coordinate file: - -```sh -cat lanes_and_tiles.txt | xargs -n 1 -P -I {} \ - sh -c 'bcl2fastq -R --no-lane-splitting \ - -o /"{}" --tiles s_"{}"; \ - - openst barcode_preprocessing \ - --fastq-in /{}/Undetermined_S0_R1_001.fastq.gz \ - --tilecoords-out \ - --out-suffix .txt \ - --out-prefix "{}" \ - --crop-seq \ - --rev-comp \ - --single-tile' -``` - -Again, make sure to replace the placeholders (`<...>`). Now, `` and `` are the directories where the -basecall files are contained and where the converted output `fastq` files will be saved; The rest of -arguments have the same meaning as above. - -## Processing of the Open-ST library -The transformation of raw sequencing data into spatially-mapped expression matrices was carried out utilizing -[spacemake](https://spacemake.readthedocs.io/en/latest/) (see also in [GitHub](https://github.com/rajewsky-lab/spacemake)), -an automated pipeline designed for the preprocessing, alignment, and quantification of single-cell and spatial transcriptomics data. - -We refer to the [official documentation](https://spacemake.readthedocs.io/en/latest/) for a complete tutorial on how to install and -run spacemake. In summary, the user needs to specify a configuration file (by default, we included run modes that are compatible -with Open-ST), and a project configuration, containing the locations to the `fastq` files, to the **spatial barcode coordinate files** -(generated in the [previous step](#processing-the-sequencing-of-barcoded-library)), -and other metadata. Then, spacemake automatically processes these data into [h5ad](https://anndata.readthedocs.io/en/latest/fileformat-prose.html) -files, which basically contain a matrix of barcodes (rows) and genes (columns), with associated metadata and spatial coordinates. - -As well, several HTML reports are generated by spacemake, such that the user can quickly and visually -assess the quality of samples prior to any downstream processing and analysis. - -## Global spatial coordinates: *tile* stitching -The `x_pos` and `y_pos` coordinates from the [table above](#processing-the-sequencing-of-barcoded-library) are given relative to each individual tile, not absolute to the -whole extension of the flow cell - which consists of many *tiles* arranged in *lanes* and *swaths*, see above. -This manner, we provide code in `openst` and `spacemake` that allows to compute the global coordinates in the -case of having a sample whose placement has cover more than one tile -during the Open-ST library preparation. - -When specifying a `run_mode` without meshing and with a `puck_collection` -(see [spacemake documentation](https://spacemake.readthedocs.io/en/latest/config.html), -under the section *Configure run_modes*), spacemake will automatically generate a single file out of -all individual (per-tile) `h5ad` files, with the suffix `_puck_collection`. This file must be used for all -subsequent steps of this tutorial, not the individual `h5ad` files. - -Otherwise, if these `puck_collection` files were not automatically generated, we provide code within the -`openst` package with that same functionality. This must be ran one sample at a time, and requires two types -of input file: (1) all the `h5ad` files generated for a single sample (can be specified implicitly, via a wildcard in Linux, -or explicitly as a list separated by spaces); and (2) a coordinate system file, that specifies that is the relative offset, -in units equivalent to `x_pos` and `y_pos`, of the tiles respect to their *column* and *swath*. These coordinate system files -have a standardized format, and are provided in the [spacemake](https://github.com/rajewsky-lab/spacemake/blob/master/spacemake/data/puck_collection/openst_coordinate_system.csv) repo and [openst](https://rajewsky-lab.github.io/openst/latest/examples/datasets/) website for the Illumina® NovaSeq 6000 S4 flow cell. -If you have used other flow cell, you might need to generate these files following the same convention. Feel free to open an -issue in our [repo](https://github.com/rajewsky-lab/openst/issues/new?assignees=&labels=&template=bug-report.md&title=), and we can generate it for you. - -To manually create 'puck_collection' files, you can run the following in a terminal: - -```sh -openst spatial_stitch \ - --tiles dge.fc_1_tile_2267.h5ad dge.fc_1_tile_2268.h5ad \ - --tile-id fc_1_tile_2267 fc_1_tile_2268 \ - --tile-coordinates fc_1_coordinate_system.csv \ - --h5-out spatial_stitched_spots.h5ad -``` - -This program has additional arguments that are explained when running `openst spatial_stitch --help`. -Make sure to populate the arguments with the proper values. In this example, `--tiles` refer to two h5ad files -for two tiles (would need to be changed to your specific spacemake folder configuration and file names); -`--tile-id` are the tile identifiers for each tile in `--tiles`, in order. These tile IDs must correspond to the first -column of the `--tile-coordinates` file - here, we use fc_1 provided in the examples, but you might need to adapt this -to your own flow cell. Lastly, `--h5-out` is specified for consistency with the following steps. - -## Expected output - -After running all the steps of this section, you will end up with two types of file: (1) the spatial coordinates of flow cell -tiles (you will only need to generate this once per flow cell); and (2) one `h5ad` file per sample, containing the gene expression and -spatial coordinates of each barcoded spot. In the following sections, we will use the file (2) and the -[previously preprocessed images](preprocessing_imaging.md) \ No newline at end of file diff --git a/docs/examples/adult_mouse/generate_expression_matrix.md b/docs/examples/adult_mouse/generate_expression_matrix.md index 801133b..cc5e204 100644 --- a/docs/examples/adult_mouse/generate_expression_matrix.md +++ b/docs/examples/adult_mouse/generate_expression_matrix.md @@ -1,61 +1,43 @@ -# Generating a cell-by-gene matrix -After pairwise alignment, the same coordinate system is shared between the spatial barcodes and the -staining images. - -However, analysis (e.g., clustering, pseudotime, DGE...) is performed on single cells, not on individual capture areas -(0.6 μm in the current version of the protocol). - -Here we show how to aggregate the barcoded spots-by-gene matrix -into a cell-by-genes matrix, where cells are defined from the segmentation mask. +# Segmentation and single-cell quantification +Once the ST and imaging modalities [have been aligned](pairwise_alignment.md), you can segment the images into single cells/nuclei, +and then aggregate the spot locations into individual cells for subsequent analysis. ## Segmentation of staining image -To create such a spatial cell-by-gene ($M\times G$) expression matrix, you will first need a segmentation mask. - -We efficiently segment cells (or nuclei) from staining images using [cellpose](https://github.com/MouseLand/cellpose). -For the H&E-stained tissue provided in this example, we used our [fine-tuned model](http://bimsbstatic.mdc-berlin.de/rajewsky/openst-public-data/models/HE_cellpose_rajewsky). -Make sure to download it and save it into a new `models` folder that you need to create under the `openst_adult_demo` main folder. - -You can run the segmentation on the previously created `openst_demo_adult_mouse_spatial_beads_puck_collection_aligned.h5ad` file, which -contains the spatial transcriptome coordinates and staining image after coarse+fine pairwise alignment. +First, segment the imaging data into single nuclei. +For this dataset, the default `HE_cellpose_rajewsky` works really well. ```sh -openst segment \ - --h5-in alignment/openst_demo_adult_mouse_spatial_beads_puck_collection_aligned.h5ad \ +openst from_spacemake \ + --project-id openst_demo \ + --sample-id openst_demo_adult_mouse \ + segment \ --image-in 'uns/spatial_pairwise_aligned/staining_image_transformed' \ - --mask-out 'uns/spatial_pairwise_aligned/mask_transformed_0px' \ + --mask-out 'uns/spatial/staining_image_mask' \ --model models/HE_cellpose_rajewsky \ - --chunked \ - --device cuda \ - --num-workers 8 + --dilate-px 0 \ + --device cuda ``` -After running this command, the segmentation mask is created and stored in the same `--h5-in` file, under -the dataset `uns/spatial_pairwise_aligned/mask_transformed_0px`. - ## Assigning transcripts to segmented cells -Now, we aggregate the initial barcoded spots-by-gene matrix into a cells-by-genes matrix, by leveraging the +Now, aggregate the initial barcoded spots-by-gene matrix into a cells(nuclei)-by-genes matrix, by leveraging the segmentation mask. -!!! note - In this dataset, we did not specify `--dilate-px`. Since this model segments nuclei from H&E, we are referring - to nuclei when we talk about cells. We use this terminology for consistency with the rest of the documentation. - -This step allows you to aggregate capture spots by segmented cells: +This step allows you to aggregate capture spots by segmented cells (nuclei): ```sh -openst transcript_assign \ - --h5-in alignment/openst_demo_adult_mouse_spatial_beads_puck_collection_aligned.h5ad \ - --spatial-key spatial_pairwise_aligned_fine \ - --mask-in 'uns/spatial_pairwise_aligned/mask_transformed_0px' \ - --h5-out alignment/openst_demo_adult_mouse_by_cell.h5ad +openst from_spacemake \ + --project-id openst_demo \ + --sample-id openst_demo_adult_mouse \ + transcript_assign \ + --spatial-key obsm/spatial_manual_fine ``` ## Expected output -That's it! After running the steps above, you will have that single `alignment/openst_demo_adult_mouse_by_cell.h5ad` file, +That's it! After running the steps above, you will have that single file which contains the transcriptomic information per segmented cell (nucleus). !!! warning - Do not expect the files to be exactly the same, as there are several - steps on the pipeline that are not deterministic (e.g., alignment, segmentation). + Do not expect the files to be exactly the same to the ones we generated, as there are several + steps on the pipeline that may not be deterministic (e.g., alignment, segmentation). -Now, the following sections provide some examples of exploratory data analysis that can be performed with this type of data. \ No newline at end of file +Now, the following section provides some examples of exploratory data analysis. \ No newline at end of file diff --git a/docs/examples/adult_mouse/pairwise_alignment.md b/docs/examples/adult_mouse/pairwise_alignment.md index b6478cc..5e43d8d 100644 --- a/docs/examples/adult_mouse/pairwise_alignment.md +++ b/docs/examples/adult_mouse/pairwise_alignment.md @@ -1,4 +1,4 @@ -# Align image to transcriptome +# Pairwise alignment In the previous step, the transcriptomic reads were processed and mapped in tissue space with `spacemake`. Now, we perform a pairwise alignment between the imaging and spatial transcriptomics modality, such that @@ -7,84 +7,118 @@ we can later aggregate transcripts into individual cells delimited by the segmen 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 GUI, in a manual manner. Although we provide models for fiducial feature detection, the accuracy might be affected by the type of microscope, -imaging strategy, tissue type and width... Thus, manual fine alignment is a good option. Thanks to the -GUI specifically designed for this task, the necessary amount of time invested is reasonable -(~5 minutes per sample consisting of 12 tiles). +imaging strategy, tissue type and width... Thus, manual fine alignment is a good option. This can be done +very quickly thanks to the GUI specifically designed for this task (~5 minutes per sample of 12 tiles). -## Coarse alignment -We assume that you have the single `h5ad` file that contains all the tiles for the experiment (you got it automatically -from `spacemake`, or you followed the [stitching instructions](preprocessing_sequencing.md#expected-output)). -Now, make sure of two things: -1. You have an environment where the `openst` package is installed (we recommend doing this -in an environment different to the one used for `spacemake`). -1. You are in the `/home/user/openst_adult_demo` folder (or similar, depending on your system, username...) +## Download and copy image data + +For this dataset, we archived the [stitched tile-scan image](https://bimsbstatic.mdc-berlin.de/rajewsky/openst-public-data/adult_mouse_hippocampus.tif). +This single image was generated from multiple, independently imaged tiles, by leveraging `openst image_stitch`. So, +you don't need to use this command, since we already provide the stitched image. Anyway, let us know +if you want access to this tile images, in case you want to try. + +As well, the imaging from this dataset did not require any further postprocessing prior to segmentation, +as visual inspection of the images did not reveal any strong illumination or focus biases. + +![H&E staining of adult mouse hippocampus (Open-ST)](../../static/img/adult_mouse_histology.webp){ loading=lazy width=300} + +You can download the image data from the link above + +```bash +wget "https://bimsbstatic.mdc-berlin.de/rajewsky/openst-public-data/adult_mouse_hippocampus.tif" +``` + +and then copy it into relevant path, for example: + +```bash +mkdir spacemake_folder/projects/openst_demo/processed_data/openst_demo_adult_mouse/imaging +cp adult_mouse_hippocampus.tif \ + spacemake_folder/projects/openst_demo/processed_data/openst_demo_adult_mouse/imaging/Image_Stitched_Composite.tif +``` + + +the final folder structure should look like: ```sh -mkdir alignment +spacemake_folder +`-- projects + `-- openst_demo + `-- processed_data + `-- openst_demo_adult_mouse + `-- imaging + `-- Image_Stitched_Composite.tif +``` -cp projects/openst_demo/processed_data/openst_demo_adult_mouse/illumina/complete_data/dge/dge.all.polyA_adapter_trimmed.mm_included.spatial_beads_puck_collection.h5ad alignment/spatial_beads_adult_mouse.h5ad +## Merging data modalities -# download the image data -wget "http://bimsbstatic.mdc-berlin.de/rajewsky/openst-public-data/adult_mouse_hippocampus.tif" +From the relevant `spacemake` main folder, merge both transcriptomics and imaging modalities + +```bash +openst from_spacemake \ + --project-id openst_demo \ + --sample-id openst_demo_adult_mouse \ + merge_modalities +``` -openst merge_modalities \ - --h5-in alignment/spatial_beads_adult_mouse.h5ad \ - --image-in adult_mouse_hippocampus.tif -openst pairwise_aligner \ - --h5-in alignment/spatial_beads_adult_mouse.h5ad \ +## Coarse pairwise alignment (auto) +Once the two modalities have been merged, you can run automatic alignment. Here we only run coarse (`--only-coarse`), +so we can showcase how to do manual refinement with the GUI. + +```sh +openst from_spacemake \ + --project-id openst_demo \ + --sample-id openst_demo_adult_mouse \ + pairwise_aligner \ --only-coarse \ --device cuda ``` -This will create the file `alignment/spatial_beads_adult_mouse.h5ad`, that hopefully contains a proper coarse alignment of the two modalities (at low resolution). - !!! note For this image data, and the spatial coordinates after alignment, the conversion factor to physical distance is **1 pixel = 0.345 µm**. -!!! tip - From `openst>=0.0.7`, there is `openst from_spacemake` to populate the data input/output arguments automatically, and keep the processing inside the `spacemake` folder structure. You can read more in the [tutorial](../../computational/from_spacemake.md). - -## Fine alignment (manual) +## Fine pairwise alignment (manual) -You will now visually asses it and refine the previous coarse alignment using the provided GUI tool. -From the terminal, run: +Now you can assess the pairwise alignment visually, and refine it using the provided GUI tool. ```sh -openst manual_pairwise_aligner +openst from_spacemake \ + --project-id openst_demo \ + --sample-id openst_demo_adult_mouse \ + manual_pairwise_aligner \ + --spatial-key obsm/spatial_pairwise_aligned_coarse \ + --image-key uns/spatial_pairwise_aligned/staining_image_transformed ``` -Then, follow these instructions on the GUI, by pressing the *buttons*: +Then, follow the following steps: -1. Click on *Load h5ad* and browse to the location where the `spatial_beads_adult_mouse.h5ad` file -is located. Select it and click *Open*. -2. Go to *Data properties*, then click under *Image data*, and select from the tree `uns>spatial_pairwise_aligned>staining_image_transformed`. Click *Ok*. -3. Click under *Spatial coordinates*, and select from the tree `obsm/spatial_pairwise_aligned_coarse`. Click *Ok*. -4. Go to rendering settings, and keep in mind the *Threshold counts* parameter. -5. Now select the *all_tiles_coarse* option from the *Layer selector*, and click on *Render*. You will see the staining image on the upper left, +1. Select the *all_tiles_coarse* layer from the *Layer selector*, and click on *Render*. You will see the staining image on the upper left, the transcriptomic image on the top right, and the merge on the bottom left. These two modalities should *roughly* match. If not, you would need to - run the coarse alignment in manual mode, too. -6. Once you've checked that the coarse alignment is fine, start selecting the layer 0, and click *Render*. -7. Now, you need to select the pairs of corresponding fiducial markers on both modalities (top left and top right) by double clicking on the left image, first, and on the right image, second. You can drag the points to new locations, zoom into the images with the mouse wheel, pan the image by holding the mouse right cursor and moving, and remove the points by pressing backspace on your keyboard. -8. You need to select at least 2 pairs of points per layer. Don't forget to click on *Render* after you select a new layer (e.g., 1, 2, 3...) so it displays. Also, you can preview how the alignment will look like after transformation by pressing *Preview alignment*. This will update the merge view (bottom left). You can change the opacity of the merge images under *Rendering settings*, by adjusting the Image A/B opacity sliders. Also, if you don't see a lot of signal in the transcriptomic pseudoimage, try decreasing the *Threshold counts* and *Pseudoimage size* values. Per tile (layer), adjust the keypoints until you get a good alignment as many times as necessary. -9. Once you've finished, go under *Keypoint properties* and click on *Save keypoints*. Go to the `alignment` folder under `openst_adult_demo`, and save the file as `openst_adult_demo_fine_keypoints`. + run the coarse (low-res) alignment in manual mode, too. +2. If the coarse alignment looks good, select layer '0' and click *Render*. +3. Select pairs of corresponding fiducial markers (at least 3) on both modalities by double clicking on the left image, first, and on the right image, second. You can drag the points to new locations, zoom into the images with the mouse wheel, pan the image by holding the mouse right cursor and moving, and remove the points by pressing backspace on your keyboard. You can preview how the alignment will look like after transformation by pressing *Preview alignment*. +4. Repeat from 2, but selecting a new layer every time ('1', '2', ...). +5. Once you've finished with all tiles, go under *Keypoint properties* and click *Save keypoints* as `keypoints.json`. -Great! You have just created a file with the corresponding points between the respective coordinate systems of -the spatial trancriptome and image modalities. +With this, you have created a `keypoints.json` file containing pairs of corresponding points between the +spatial trancriptome and image modalities. -## Apply keypoint transformation to coarse alignment -Now, you need to run a program that takes the `h5ad` file with the -coarse alignment, and the keypoints file, to perform the fine alignment: +## Apply keypoint transformation +Then, compute apply the rigid transformations to the coarsely aligned spatial coordinates: ```sh -openst apply_transform \ - --keypoints-in alignment/openst_adult_demo_fine_keypoints.json \ - --h5-in alignment/spatial_beads_adult_mouse.h5ad \ +openst from_spacemake \ + --project-id openst_demo \ + --sample-id openst_demo_adult_mouse \ + apply_transform \ + --keypoints-in keypoints.json \ + --spatial-key-out obsm/spatial_manual_fine \ --per-tile ``` -After this, no file will be created nor removed; the coordinates of the fine alignment will be added to the existing -`spatial_beads_adult_mouse.h5ad` file. +The `--per-tile` is important to perform this operation per tile. Otherwise, `apply_transform` will assume +that the transform needs to be done for all coordinates, and will expect that you have selected corresponding +keypoints for the layer *all_tiles_coarse*. That's it! Now you're ready to go to the next step. \ No newline at end of file diff --git a/docs/examples/adult_mouse/preprocessing_imaging.md b/docs/examples/adult_mouse/preprocessing_imaging.md deleted file mode 100644 index 319a482..0000000 --- a/docs/examples/adult_mouse/preprocessing_imaging.md +++ /dev/null @@ -1,27 +0,0 @@ -# Preprocessing of imaging data - -For this dataset, we archived the [stitched tile-scan image](https://bimsbstatic.mdc-berlin.de/rajewsky/openst-public-data/adult_mouse_hippocampus.tif). -This single image was generated from multiple, independently imaged tiles, by leveraging `openst image_stitch`. So, -you don't need to use this command, since we already provide the stitched image. Anyway, let us know -if you want access to this tile images, in case you want to try. - -As well, the imaging from this dataset did not require any further postprocessing prior to segmentation, -as visual inspection of the images did not reveal any strong illumination or focus biases. - -*(provide a screenshot of the image here)* - -Therefore, feel free to download the data from the link above, and continue to the next section. -Then, browse back to the `openst_adult_demo` folder, create a folder `alignment`, and save the -`adult_mouse_hippocampus.tif` file inside the `alignment` folder. You will have the following file structure: - -```sh -/home/user -|-- openst_adult_demo -| |-- data -| | `-- fastq -| |-- spacemake -| | `-- ... -| |-- bins -| `-- alignment -| `-- adult_mouse_hippocampus.tif -``` \ No newline at end of file diff --git a/docs/examples/adult_mouse/preprocessing_sequencing.md b/docs/examples/adult_mouse/preprocessing_sequencing.md index f0dadc2..4ec6827 100644 --- a/docs/examples/adult_mouse/preprocessing_sequencing.md +++ b/docs/examples/adult_mouse/preprocessing_sequencing.md @@ -1,4 +1,4 @@ -# Preprocessing of sequencing +# Preprocessing transcriptomic library After sequencing, we proceed with the preprocessing of the data, to go from raw reads to transcriptomic information mapped to the mouse genome, in space. @@ -37,7 +37,7 @@ First of all, intialize the conda environment for `spacemake` (spacemake) user@computer:~$ ``` -### Initialization +### Initialize Create two folders inside your `openst_adult_demo` folder, called `spacemake` and `bins`, so you will have: ```sh @@ -61,7 +61,7 @@ browse to the `spacemake` directory you just created in the `openst_adult_demo` --dropseq_tools /home/user/bins/Drop-seq_tools-2.5.1 ``` -### Configuring spacemake +### Configure 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 @@ -99,7 +99,7 @@ spacemake config add_species \ # etc... ``` -### Adding sample +### Add sample Now you need to add the sample data and metadata to `spacemake`. For this, you will also need the puck (tile) barcode files, which [can be generated](../../computational/preprocessing_sequencing.md#computing-barcodes-and-spatial-coordinates-of-all-tiles) with the `openst` package. @@ -162,7 +162,7 @@ wget "http://bimsbstatic.mdc-berlin.de/rajewsky/openst-public-data/fc_2_coordina cp fc_2_coordinate_system.csv puck_data/. ``` -### Running `spacemake` +### Run That's all you need to configure! Now, you can run spacemake with the following: ```sh diff --git a/docs/examples/e13_mouse/generate_expression_matrix.md b/docs/examples/e13_mouse/generate_expression_matrix.md index 0f2bd7c..317cb7a 100644 --- a/docs/examples/e13_mouse/generate_expression_matrix.md +++ b/docs/examples/e13_mouse/generate_expression_matrix.md @@ -1,57 +1,43 @@ -# Generating a cell-by-gene matrix -After pairwise alignment, the same coordinate system is shared between the spatial barcodes and the -staining images. - -However, analysis (e.g., clustering, pseudotime, DGE...) is performed on single cells, not on individual capture areas -(0.6 μm in the current version of the protocol). - -Here we show how to aggregate the barcoded spots-by-gene matrix -into a cell-by-genes matrix, where cells are defined from the segmentation mask. +# Segmentation and single-cell quantification +Once the ST and imaging modalities [have been aligned](pairwise_alignment.md), you can segment the images into single cells/nuclei, +and then aggregate the spot locations into individual cells for subsequent analysis. ## Segmentation of staining image -To create such a spatial cell-by-gene ($M\times G$) expression matrix, you will first need a segmentation mask. - -We efficiently segment cells (or nuclei) from staining images using [cellpose](https://github.com/MouseLand/cellpose). -For the H&E-stained tissue provided in this example, we used our [fine-tuned model](http://bimsbstatic.mdc-berlin.de/rajewsky/openst-public-data/models/HE_cellpose_rajewsky). -Make sure to download it and save it into a new `models` folder that you need to create under the `openst_e13_demo` main folder. - -You can run the segmentation on the previously created `openst_demo_e13_mouse_head_spatial_beads_puck_collection_aligned.h5ad` file, which -contains the spatial transcriptome coordinates and staining image after coarse+fine pairwise alignment. +First, segment the imaging data into single nuclei. +For this dataset, the default `HE_cellpose_rajewsky` works really well. ```sh -openst segment \ - --h5-in alignment/openst_demo_e13_mouse_head_spatial_beads_puck_collection_aligned.h5ad \ +openst from_spacemake \ + --project-id openst_demo \ + --sample-id openst_demo_e13_mouse_head \ + segment \ --image-in 'uns/spatial_pairwise_aligned/staining_image_transformed' \ - --mask-out 'uns/spatial_pairwise_aligned/mask_transformed_0px' \ + --mask-out 'uns/spatial/staining_image_mask' \ --model models/HE_cellpose_rajewsky \ - --chunked \ - --device cuda \ - --num-workers 8 + --dilate-px 0 \ + --device cuda ``` -After running this command, the segmentation mask is created and stored in the same `--h5-in` file, under -the dataset `uns/spatial_pairwise_aligned/mask_transformed_0px`. - ## Assigning transcripts to segmented cells -Now, we aggregate the initial barcoded spots-by-gene matrix into a cells-by-genes matrix, by leveraging the +Now, aggregate the initial barcoded spots-by-gene matrix into a cells(nuclei)-by-genes matrix, by leveraging the segmentation mask. -This step allows you to aggregate capture spots by segmented cells: +This step allows you to aggregate capture spots by segmented cells (nuclei): ```sh -openst transcript_assign \ - --h5-in alignment/openst_demo_e13_mouse_head_spatial_beads_puck_collection_aligned.h5ad \ - --spatial-key spatial_pairwise_aligned_fine \ - --mask-in 'uns/spatial_pairwise_aligned/mask_transformed_0px' \ - --h5-out alignment/openst_demo_e13_mouse_head_by_cell.h5ad +openst from_spacemake \ + --project-id openst_demo \ + --sample-id openst_demo_e13_mouse_head \ + transcript_assign \ + --spatial-key obsm/spatial_manual_fine ``` ## Expected output -That's it! After running the steps above, you will have that single `alignment/openst_demo_e13_mouse_head_by_cell.h5ad` file, +That's it! After running the steps above, you will have that single file which contains the transcriptomic information per segmented cell (nucleus). !!! warning - Do not expect the files to be exactly the same, as there are several - steps on the pipeline that are not deterministic (e.g., alignment, segmentation). + Do not expect the files to be exactly the same to the ones we generated, as there are several + steps on the pipeline that may not be deterministic (e.g., alignment, segmentation). -Now, the following sections provide some examples of exploratory data analysis that can be performed with this type of data. \ No newline at end of file +Now, the following section provides some examples of exploratory data analysis. \ No newline at end of file diff --git a/docs/examples/e13_mouse/pairwise_alignment.md b/docs/examples/e13_mouse/pairwise_alignment.md index fc91b40..28f8bd7 100644 --- a/docs/examples/e13_mouse/pairwise_alignment.md +++ b/docs/examples/e13_mouse/pairwise_alignment.md @@ -1,4 +1,4 @@ -# Align image to transcriptome +# Pairwise alignment In the previous step, the transcriptomic reads were processed and mapped in tissue space with `spacemake`. Now, we perform a pairwise alignment between the imaging and spatial transcriptomics modality, such that @@ -7,84 +7,113 @@ we can later aggregate transcripts into individual cells delimited by the segmen 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 GUI, in a manual manner. Although we provide models for fiducial feature detection, the accuracy might be affected by the type of microscope, -imaging strategy, tissue type and width... Thus, manual fine alignment is a good option. Thanks to the -GUI specifically designed for this task, the necessary amount of time invested is reasonable -(~5 minutes per sample consisting of 12 tiles). +imaging strategy, tissue type and width... Thus, manual fine alignment is a good option. This can be done +very quickly thanks to the GUI specifically designed for this task (~5 minutes per sample of 12 tiles). -## Coarse alignment -We assume that you have the single `h5ad` file that contains all the tiles for the experiment (you got it automatically -from `spacemake`, or you followed the [stitching instructions](preprocessing_sequencing.md#expected-output)). -Now, make sure of two things: -1. You have an environment where the `openst` package is installed (we recommend doing this -in an environment different to the one used for `spacemake`). -1. You are in the `/home/user/openst_e13_demo` folder (or similar, depending on your system, username...) +## Download and copy image data + +For this dataset, we archived the [stitched tile-scan image](https://bimsbstatic.mdc-berlin.de/rajewsky/openst-public-data/e13_mouse_head.tif). +This single image was generated from multiple, independently imaged tiles, by leveraging `openst image_stitch`. So, +you don't need to use this command, since we already provide the stitched image. Anyway, let us know +if you want access to this tile images, in case you want to try. + +You can download the iamge data from the link above + +```bash +wget "https://bimsbstatic.mdc-berlin.de/rajewsky/openst-public-data/e13_mouse_head.tif" +``` + +and then copy it into relevant path, for example: + +```bash +mkdir spacemake_folder/projects/openst_demo/processed_data/openst_demo_e13_mouse_head/imaging +cp e13_mouse_head.tif \ + spacemake_folder/projects/openst_demo/processed_data/openst_demo_e13_mouse_head/imaging/Image_Stitched_Composite.tif +``` + + +the final folder structure should look like: ```sh -mkdir alignment +spacemake_folder +`-- projects + `-- openst_demo + `-- processed_data + `-- openst_demo_e13_mouse_head + `-- imaging + `-- Image_Stitched_Composite.tif +``` + +## Merging data modalities + +From the relevant `spacemake` main folder, merge both transcriptomics and imaging modalities -cp projects/openst_demo/processed_data/openst_demo_e13_mouse_head/illumina/complete_data/dge/dge.all.polyA_adapter_trimmed.mm_included.spatial_beads_puck_collection.h5ad alignment/spatial_beads_e13_mouse_head.h5ad +```bash +openst from_spacemake \ + --project-id openst_demo \ + --sample-id openst_demo_e13_mouse_head \ + merge_modalities +``` -# download the image data -wget "http://bimsbstatic.mdc-berlin.de/rajewsky/openst-public-data/e13_mouse_head.tif" -openst merge_modalities \ - --h5-in alignment/spatial_beads_e13_mouse_head.h5ad \ - --image-in e13_mouse_head.tif +## Coarse pairwise alignment (auto) +Once the two modalities have been merged, you can run automatic alignment. Here we only run coarse (`--only-coarse`), +so we can showcase how to do manual refinement with the GUI. -openst pairwise_aligner \ - --h5-in alignment/spatial_beads_e13_mouse_head.h5ad \ +```sh +openst from_spacemake \ + --project-id openst_demo \ + --sample-id openst_demo_e13_mouse_head \ + pairwise_aligner \ --only-coarse \ --device cuda ``` -This will create the file `alignment/spatial_beads_e13_mouse_head.h5ad`, that hopefully contains a proper coarse alignment of the two modalities (at low resolution). - !!! note For this image data, and the spatial coordinates after alignment, the conversion factor to physical distance is **1 pixel = 0.345 µm**. -!!! tip - From `openst>=0.0.7`, there is `openst from_spacemake` to populate the data input/output arguments automatically, and keep the processing inside the `spacemake` folder structure. You can read more in the [tutorial](../../computational/from_spacemake.md). - -## Fine alignment (manual) +## Fine pairwise alignment (manual) -You will now visually asses it and refine the previous coarse alignment using the provided GUI tool. -From the terminal, run: +Now you can assess the pairwise alignment visually, and refine it using the provided GUI tool. ```sh -openst manual_pairwise_aligner +openst from_spacemake \ + --project-id openst_demo \ + --sample-id openst_demo_e13_mouse_head \ + manual_pairwise_aligner \ + --spatial-key obsm/spatial_pairwise_aligned_coarse \ + --image-key uns/spatial_pairwise_aligned/staining_image_transformed ``` -Then, follow these instructions on the GUI, by pressing the *buttons*: +Then, follow the following steps: -1. Click on *Load h5ad* and browse to the location where the `spatial_beads_e13_mouse_head.h5ad` file -is located. Select it and click *Open*. -2. Go to *Data properties*, then click under *Image data*, and select from the tree `uns>spatial_pairwise_aligned>staining_image_transformed`. Click *Ok*. -3. Click under *Spatial coordinates*, and select from the tree `obsm/spatial_pairwise_aligned_coarse`. Click *Ok*. -4. Go to rendering settings, and keep in mind the *Threshold counts* parameter. -5. Now select the *all_tiles_coarse* option from the *Layer selector*, and click on *Render*. You will see the staining image on the upper left, +1. Select the *all_tiles_coarse* layer from the *Layer selector*, and click on *Render*. You will see the staining image on the upper left, the transcriptomic image on the top right, and the merge on the bottom left. These two modalities should *roughly* match. If not, you would need to - run the coarse alignment in manual mode, too. -6. Once you've checked that the coarse alignment is fine, start selecting the layer 0, and click *Render*. -7. Now, you need to select the pairs of corresponding fiducial markers on both modalities (top left and top right) by double clicking on the left image, first, and on the right image, second. You can drag the points to new locations, zoom into the images with the mouse wheel, pan the image by holding the mouse right cursor and moving, and remove the points by pressing backspace on your keyboard. -8. You need to select at least 2 pairs of points per layer. Don't forget to click on *Render* after you select a new layer (e.g., 1, 2, 3...) so it displays. Also, you can preview how the alignment will look like after transformation by pressing *Preview alignment*. This will update the merge view (bottom left). You can change the opacity of the merge images under *Rendering settings*, by adjusting the Image A/B opacity sliders. Also, if you don't see a lot of signal in the transcriptomic pseudoimage, try decreasing the *Threshold counts* and *Pseudoimage size* values. Per tile (layer), adjust the keypoints until you get a good alignment as many times as necessary. -9. Once you've finished, go under *Keypoint properties* and click on *Save keypoints*. Go to the `alignment` folder under `openst_e13_demo`, and save the file as `openst_e13_demo_fine_keypoints`. + run the coarse (low-res) alignment in manual mode, too. +2. If the coarse alignment looks good, select layer '0' and click *Render*. +3. Select pairs of corresponding fiducial markers (at least 3) on both modalities by double clicking on the left image, first, and on the right image, second. You can drag the points to new locations, zoom into the images with the mouse wheel, pan the image by holding the mouse right cursor and moving, and remove the points by pressing backspace on your keyboard. You can preview how the alignment will look like after transformation by pressing *Preview alignment*. +4. Repeat from 2, but selecting a new layer every time ('1', '2', ...). +5. Once you've finished with all tiles, go under *Keypoint properties* and click *Save keypoints* as `keypoints.json`. -Great! You have just created a file with the corresponding points between the respective coordinate systems of -the spatial trancriptome and image modalities. +With this, you have created a `keypoints.json` file containing pairs of corresponding points between the +spatial trancriptome and image modalities. -## Apply keypoint transformation to coarse alignment -Now, you need to run a program that takes the `h5ad` file with the -coarse alignment, and the keypoints file, to perform the fine alignment: +## Apply keypoint transformation +Then, compute apply the rigid transformations to the coarsely aligned spatial coordinates: ```sh -openst apply_transform \ - --keypoints-in alignment/openst_e13_demo_fine_keypoints.json \ - --h5-in alignment/spatial_beads_e13_mouse_head.h5ad \ +openst from_spacemake \ + --project-id openst_demo \ + --sample-id openst_demo_e13_mouse_head \ + apply_transform \ + --keypoints-in keypoints.json \ + --spatial-key-out obsm/spatial_manual_fine \ --per-tile ``` -After this, no file will be created nor removed; the coordinates of the fine alignment will be added to the existing -`spatial_beads_e13_mouse_head.h5ad` file. +The `--per-tile` is important to perform this operation per tile. Otherwise, `apply_transform` will assume +that the transform needs to be done for all coordinates, and will expect that you have selected corresponding +keypoints for the layer *all_tiles_coarse*. That's it! Now you're ready to go to the next step. \ No newline at end of file diff --git a/docs/examples/e13_mouse/preprocessing_imaging.md b/docs/examples/e13_mouse/preprocessing_imaging.md deleted file mode 100644 index 30bd2d3..0000000 --- a/docs/examples/e13_mouse/preprocessing_imaging.md +++ /dev/null @@ -1,27 +0,0 @@ -# Preprocessing of imaging data - -For this dataset, we archived the [stitched tile-scan image](https://bimsbstatic.mdc-berlin.de/rajewsky/openst-public-data/e13_mouse_head.tif). -This single image was generated from multiple, independently imaged tiles, by leveraging `openst image_stitch`. So, -you don't need to use this command, since we already provide the stitched image. Anyway, let us know -if you want access to this tile images, in case you want to try. - -As well, the imaging from this dataset did not require any further postprocessing prior to segmentation, -as visual inspection of the images did not reveal any strong illumination or focus biases. - -*(provide a screenshot of the image here)* - -Therefore, feel free to download the data from the link above, and continue to the next section. -Then, browse back to the `openst_e13_mouse_head_demo` folder, create a folder `alignment`, and save the -`e13_mouse_head.tif` file inside the `alignment` folder. You will have the following file structure: - -```sh -/home/user -|-- openst_e13_mouse_head_demo -| |-- data -| | `-- fastq -| |-- spacemake -| | `-- ... -| |-- bins -| `-- alignment -| `-- e13_mouse_head.tif -``` \ No newline at end of file diff --git a/docs/examples/e13_mouse/preprocessing_sequencing.md b/docs/examples/e13_mouse/preprocessing_sequencing.md index cd989d6..36fe35c 100644 --- a/docs/examples/e13_mouse/preprocessing_sequencing.md +++ b/docs/examples/e13_mouse/preprocessing_sequencing.md @@ -1,4 +1,4 @@ -# Preprocessing of sequencing +# Preprocessing transcriptomic library After sequencing, we proceed with the preprocessing of the data, to go from raw reads to transcriptomic information mapped to the mouse genome, in space. @@ -37,7 +37,7 @@ First of all, intialize the conda environment for `spacemake` (spacemake) user@computer:~$ ``` -### Initialization +### Initialize Create two folders inside your `openst_e13_mouse_head_demo` folder, called `spacemake` and `bins`, so you will have: ```sh @@ -61,7 +61,7 @@ browse to the `spacemake` directory you just created in the `openst_e13_mouse_he --dropseq_tools /home/user/bins/Drop-seq_tools-2.5.1 ``` -### Configuring spacemake +### Configure 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 @@ -99,7 +99,7 @@ spacemake config add_species \ # etc... ``` -### Adding sample +### Add sample Now you need to add the sample data and metadata to `spacemake`. For this, you will also need the puck (tile) barcode files, which [can be generated](../../computational/preprocessing_sequencing.md#computing-barcodes-and-spatial-coordinates-of-all-tiles) with the `openst` package. @@ -111,14 +111,12 @@ When downloading the tile barcode files, create a folder under `openst_e13_mouse into this folder. Also, move the coordinate file to the `puck_data` folder in the `spacemake` directory. Remember! You need to be in the `/home/user/openst_e13_mouse_head_demo/spacemake` directory (or similar, depending on what you created); -then run the following command: +then run the following commands: ```sh # downloading R1 and R2 sequences wget "http://bimsbstatic.mdc-berlin.de/rajewsky/openst-public-data/e13_mouse_head_R1_001.fastq.gz" wget "http://bimsbstatic.mdc-berlin.de/rajewsky/openst-public-data/e13_mouse_head_R2_001.fastq.gz" - -# downloading R1 and R2 (reseq) sequences wget "http://bimsbstatic.mdc-berlin.de/rajewsky/openst-public-data/e13_mouse_head_reseq_R1_001.fastq.gz" wget "http://bimsbstatic.mdc-berlin.de/rajewsky/openst-public-data/e13_mouse_head_reseq_R2_001.fastq.gz" @@ -144,7 +142,7 @@ after you run the `spacemake init` command (see above). Modify the following lin ```yaml openst: - coordinate_system: puck_id/openst_coordinates.csv + coordinate_system: puck_data/openst_coordinates.csv spot_diameter_um: 0.6 width_um: 1200 ``` @@ -153,7 +151,7 @@ into this: ```yaml openst: - coordinate_system: puck_id/fc_1_coordinate_system.csv + coordinate_system: puck_data/fc_1_coordinate_system.csv spot_diameter_um: 0.6 width_um: 1200 ``` @@ -166,7 +164,7 @@ wget "http://bimsbstatic.mdc-berlin.de/rajewsky/openst-public-data/fc_1_coordina cp fc_1_coordinate_system.csv puck_data/. ``` -### Running `spacemake` +### Run That's all you need to configure! Now, you can run spacemake with the following: ```sh @@ -190,4 +188,6 @@ later reconstruct the cell-by-gene matrix. If you specified options for *meshing* in the `run_mode`, there will be a file containing keywords `puck_collection` and `mesh`, `hexagon` or `circle`. This contains *approximate* cell-by-gene information, as the transcripts are aggregated by a regular lattice and not by the true spatial arrangement of -cells. This might be already enough for some analyses. \ No newline at end of file +cells. 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