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simplify and improve notebooks requiring aligned data and densenet notebook
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‎datasets/README.md‎

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# Spatial omics datasets
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Here you can find all datasets necessary to run the example notebooks already converted to the ZARR file format.
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If you want to convert additional datasets check out the scripts available in the [spatialdata sandbox](https://github.com/giovp/spatialdata-sandbox).
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:::{note}
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S3 URLs cannot be opened directly in a web browser. They should be treated as Zarr stores.
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For example, appending `.zgroup` to any of the URLs will allow you to see that file.
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:::
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| Technology | Sample | File Size | Filename (spatialdata-sandbox) | download data | work with data remotely (**see note below**) | license |
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| :---------------------------------------- | :-------------------------------------------------------- | --------: | :----------------------------- | :---------------------------------------------------------------------------------------------- | :----------------------------------------------------------------------------------------- | :---------------- |
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| Visium HD | Mouse intestin [^2] | 1 GB | visium_hd_3.0.0_id | [.zarr.zip](https://s3.embl.de/spatialdata/spatialdata-sandbox/visium_hd_3.0.0_io.zip) | [S3](https://s3.embl.de/spatialdata/spatialdata-sandbox/visium_hd_3.0.0_io.zarr/) | CCA |
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| Visium | Breast cancer [^3] | 1.5 GB | visium_associated_xenium_io | [.zarr.zip](https://s3.embl.de/spatialdata/spatialdata-sandbox/visium_associated_xenium_io.zip) | [S3](https://s3.embl.de/spatialdata/spatialdata-sandbox/visium_associated_xenium_io.zarr/) | CCA |
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| Xenium | Breast cancer [^3] | 2.8 GB | xenium_rep1_io | [.zarr.zip](https://s3.embl.de/spatialdata/spatialdata-sandbox/xenium_rep1_io.zip) | [S3](https://s3.embl.de/spatialdata/spatialdata-sandbox/xenium_rep1_io.zarr/) | CCA |
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| Xenium | Breast cancer [^3] | 3.7 GB | xenium_rep2_io | [.zarr.zip](https://s3.embl.de/spatialdata/spatialdata-sandbox/xenium_rep2_io.zip) | [S3](https://s3.embl.de/spatialdata/spatialdata-sandbox/xenium_rep2_io.zarr/) | CCA |
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| CyCIF (MCMICRO output) | Small lung adenocarcinoma [^4] | 250 MB | mcmicro_io | [.zarr.zip](https://s3.embl.de/spatialdata/spatialdata-sandbox/mcmicro_io.zip) | [S3](https://s3.embl.de/spatialdata/spatialdata-sandbox/mcmicro_io.zarr/) | CC BY-NC 4.0 DEED |
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| MERFISH | Mouse brain [^5] | 50 MB | merfish | [.zarr.zip](https://s3.embl.de/spatialdata/spatialdata-sandbox/merfish.zip) | [S3](https://s3.embl.de/spatialdata/spatialdata-sandbox/merfish.zarr/) | CC0 1.0 DEED |
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| MIBI-TOF | Colorectal carcinoma [^6] | 25 MB | mibitof | [.zarr.zip](https://s3.embl.de/spatialdata/spatialdata-sandbox/mibitof.zip) | [S3](https://s3.embl.de/spatialdata/spatialdata-sandbox/mibitof.zarr/) | CC BY 4.0 DEED |
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| Imaging Mass Cytometry (Steinbock output) | 4 different cancers (SCCHN, BCC, NSCLC, CRC) [^7][^8][^9] | 820 MB | steinbock_io | [.zarr.zip](https://s3.embl.de/spatialdata/spatialdata-sandbox/steinbock_io.zip) | [S3](https://s3.embl.de/spatialdata/spatialdata-sandbox/steinbock_io.zarr/) | CC BY 4.0 DEED |
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| Molecular Cartography (SPArrOW output) | Mouse Liver [^10][^11] | 70 MB | MouseLiver | [.zarr.zip](https://s3.embl.de/spatialdata/spatialdata-sandbox/mouse_liver.zip) | [S3](https://s3.embl.de/spatialdata/spatialdata-sandbox/mouse_liver.zarr) | CC BY 4.0 DEED |
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| SpaceM | T cells [^12] | 116 MB | spacem_scseahorse1 | [.zarr.zip](https://s3.embl.de/spatialdata/raw_data/20220121_ScSeahorse1.zip) | NA | CC BY 4.0 DEED |
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| SpaceM | Hepa and NIH3T3 cells [^13] | 59 MB | spacem_hepanih3t3 | [.zarr.zip](https://s3.embl.de/spatialdata/spatialdata-sandbox/spacem_helanih3t3.zip) | [.zarr.zip](https://s3.embl.de/spatialdata/spatialdata-sandbox/spacem_helanih3t3.zarr) | CC BY 4.0 DEED |
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For the first 3 datasets, we also provide a version of them in which they are all aligned in a common coordinate system, and where we added the cell-type information, as described in our paper, to annotate the Xenium cells.
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| Technology | Sample | File Size | Filename (spatialdata-sandbox) | download data | work with data remotely (**see note below**) | license |
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| :---------------------------------------- | :-------------------------------------------------------- | --------: | :-------------------------- | :---------------------------------------------------------------------------------------------- | :----------------------------------------------------------------------------------------- | :---------------- |
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| Visium | Breast Cancer [^3] | 1.5 GB | visium_associated_xenium_io | [.zarr.zip](https://s3.embl.de/spatialdata/spatialdata-sandbox/visium_associated_xenium_io_aligned.zip) | [S3](https://s3.embl.de/spatialdata/spatialdata-sandbox/visium_associated_xenium_io_aligned.zarr/) | CCA |
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| Xenium | Breast Cancer [^3] | 2.8 GB | xenium_rep1_io | [.zarr.zip](https://s3.embl.de/spatialdata/spatialdata-sandbox/xenium_rep1_io_aligned.zip) | [S3](https://s3.embl.de/spatialdata/spatialdata-sandbox/xenium_rep1_io_aligned.zarr/) | CCA |
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| Xenium | Breast Cancer [^3] | 3.7 GB | xenium_rep2_io | [.zarr.zip](https://s3.embl.de/spatialdata/spatialdata-sandbox/xenium_rep2_io_aligned.zip) | [S3](https://s3.embl.de/spatialdata/spatialdata-sandbox/xenium_rep2_io_aligned.zarr/) | CCA |
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Here you can find all datasets necessary to run the example notebooks already converted
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to the SpatialData Zarr file format.
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Scripts to convert data from several other technologies into SpatialData Zarr are
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available in the [spatialdata sandbox](https://github.com/giovp/spatialdata-sandbox); in
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particular:
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- CyCIF (MCMICRO output)
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- Imaging Mass Cytometry, IMC (Steinbock output)
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- seqFISH
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| Technology | Sample | File Size | Filename (spatialdata-sandbox) | download data (latest stable release) | license |
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|:------------------------------------------|:----------------------------------------------------------|----------:|:-------------------------------|:------------------------------------------------------------------------------------------------|:------------------|
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| Visium HD | Mouse intestin [^1] | ~2.4 GB | visium_hd_3.0.0_io | [.zarr.zip](https://s3.embl.de/spatialdata/spatialdata-sandbox/visium_hd_3.0.0_io.zip) | CCA |
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| Visium | Breast cancer [^2] | ~1.5 GB | visium_associated_xenium_io | [.zarr.zip](https://s3.embl.de/spatialdata/spatialdata-sandbox/visium_associated_xenium_io.zip) | CCA |
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| Xenium | Breast cancer [^2] | ~2.8 GB | xenium_rep1_io | [.zarr.zip](https://s3.embl.de/spatialdata/spatialdata-sandbox/xenium_rep1_io.zip) | CCA |
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| Xenium | Lung cancer [^3] | ~5.4 GB | xenium_2.0.0_io | [.zarr.zip](https://s3.embl.de/spatialdata/spatialdata-sandbox/xenium_2.0.0_io.zip) | CCA |
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| CyCIF (MCMICRO output) | Small lung adenocarcinoma [^4] | ~250 MB | mcmicro_io | [.zarr.zip](https://s3.embl.de/spatialdata/spatialdata-sandbox/mcmicro_io.zip) | CC BY-NC 4.0 DEED |
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| MERFISH | Mouse brain [^5] | ~50 MB | merfish | [.zarr.zip](https://s3.embl.de/spatialdata/spatialdata-sandbox/merfish.zip) | CC0 1.0 DEED |
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| MIBI-TOF | Colorectal carcinoma [^6] | ~25 MB | mibitof | [.zarr.zip](https://s3.embl.de/spatialdata/spatialdata-sandbox/mibitof.zip) | CC BY 4.0 DEED |
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| Imaging Mass Cytometry (Steinbock output) | 4 different cancers (SCCHN, BCC, NSCLC, CRC) [^7][^8][^9] | ~800 MB | steinbock_io | [.zarr.zip](https://s3.embl.de/spatialdata/spatialdata-sandbox/steinbock_io.zip) | CC BY 4.0 DEED |
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| Molecular Cartography (SPArrOW output) | Mouse Liver [^10][^11] | ~70 MB | MouseLiver | [.zarr.zip](https://s3.embl.de/spatialdata/spatialdata-sandbox/mouse_liver.zip) | CC BY 4.0 DEED |
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| SpaceM | Hepa and NIH3T3 cells [^13] | ~60 MB | spacem_hepanih3t3 | [.zarr.zip](https://s3.embl.de/spatialdata/spatialdata-sandbox/spacem_helanih3t3.zip) | CC BY 4.0 DEED |
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## Licenses abbreviations
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The data retains the license of the original published data.
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<!-- to add: raccoon, blobs, "additional resources for methods developers" -->
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<!-- Artificial datasets
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| Description | File Size| Filename | download data | work with data remotely [^1] |
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| :--------------------- | :------------------------- | --------:| :-------------------------- | :---------------------------------------------------------------------------------------------- | :----------------------------------------------------------------------------------------- || - | - | 11 kB| toy | [.zarr.zip](https://s3.embl.de/spatialdata/spatialdata-sandbox/toy.zip) | [S3](https://s3.embl.de/spatialdata/spatialdata-sandbox/toy.zarr/) | -->
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# Artificial datasets
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Also, here you can find [additional datasets and resources for methods developers](https://github.com/scverse/spatialdata-notebooks/blob/main/notebooks/developers_resources/storage_format/).
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Also, here you can
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find [additional datasets and resources for methods developers](https://github.com/scverse/spatialdata-notebooks/blob/main/notebooks/developers_resources/storage_format/).
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# References
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If you use the datasets please cite the original sources and double-check their license.
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[^2]: From https://www.10xgenomics.com/datasets/visium-hd-cytassist-gene-expression-libraries-of-mouse-intestine
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[^1]:
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From https://www.10xgenomics.com/datasets/visium-hd-cytassist-gene-expression-libraries-of-mouse-intestine
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[^2]: Janesick, A. et al. High resolution mapping of the breast cancer tumor
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microenvironment using integrated single cell, spatial and in situ analysis of FFPE
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tissue. bioRxiv 2022.10.06.510405 (2022) doi:10.1101/2022.10.06.510405.
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[^3]: Janesick, A. et al. High resolution mapping of the breast cancer tumor microenvironment using integrated single cell, spatial and in situ analysis of FFPE tissue. bioRxiv 2022.10.06.510405 (2022) doi:10.1101/2022.10.06.510405.
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[^3]:
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From https://www.10xgenomics.com/datasets/preview-data-ffpe-human-lung-cancer-with-xenium-multimodal-cell-segmentation-1-standard
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[^4]: Schapiro, D. et al. MCMICRO: A scalable, modular image-processing pipeline for multiplexed tissue imaging. Cold Spring Harbor Laboratory 2021.03.15.435473 (2021) doi:10.1101/2021.03.15.435473.
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[^4]: Schapiro, D. et al. MCMICRO: A scalable, modular image-processing pipeline for
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multiplexed tissue imaging. Cold Spring Harbor Laboratory 2021.03.15.435473 (2021) doi:
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10.1101/2021.03.15.435473.
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[^5]: Moffitt, J. R. et al. Molecular, spatial, and functional single-cell profiling of the hypothalamic preoptic region. Science 362, (2018).
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[^5]: Moffitt, J. R. et al. Molecular, spatial, and functional single-cell profiling of
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the hypothalamic preoptic region. Science 362, (2018).
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[^6]: Hartmann, F. J. et al. Single-cell metabolic profiling of human cytotoxic T cells. Nat. Biotechnol. (2020) doi:10.1038/s41587-020-0651-8.
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[^6]: Hartmann, F. J. et al. Single-cell metabolic profiling of human cytotoxic T cells.
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Nat. Biotechnol. (2020) doi:10.1038/s41587-020-0651-8.
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[^7]: Windhager, J., Bodenmiller, B. & Eling, N. An end-to-end workflow for multiplexed image processing and analysis. bioRxiv 2021.11.12.468357 (2021) doi:10.1101/2021.11.12.468357.
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[^7]: Windhager, J., Bodenmiller, B. & Eling, N. An end-to-end workflow for multiplexed
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image processing and analysis. bioRxiv 2021.11.12.468357 (2021) doi:
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10.1101/2021.11.12.468357.
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[^8]: Eling, N. & Windhager, J. Example imaging mass cytometry raw data. (2022). doi:10.5281/zenodo.5949116.
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[^8]: Eling, N. & Windhager, J. Example imaging mass cytometry raw data. (2022). doi:
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10.5281/zenodo.5949116.
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[^9]: Eling, N. & Windhager, J. steinbock results of IMC example data. (2022). doi:10.5281/zenodo.7412972.
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[^9]: Eling, N. & Windhager, J. steinbock results of IMC example data. (2022). doi:
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10.5281/zenodo.7412972.
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[^10]: Guilliams, Martin, et al. "Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches." Cell 185.2 (2022) doi:10.1016/j.cell2021.12.018
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[^10]: Guilliams, Martin, et al. "Spatial proteogenomics reveals distinct and
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evolutionarily conserved hepatic macrophage niches." Cell 185.2 (2022) doi:
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10.1016/j.cell2021.12.018
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[^11]: Pollaris, Lotte, et al. "SPArrOW: a flexible, interactive and scalable pipeline for spatial transcriptomics analysis." bioRxiv (2024) doi:10.1101/2024.07.04.601829
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[^11]: Pollaris, Lotte, et al. "SPArrOW: a flexible, interactive and scalable pipeline
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for spatial transcriptomics analysis." bioRxiv (2024) doi:10.1101/2024.07.04.601829
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[^12]: See https://github.com/giovp/spatialdata-sandbox/blob/main/spacem_scseahorse1/README.md
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[^12]:
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See https://github.com/giovp/spatialdata-sandbox/blob/main/spacem_scseahorse1/README.md
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[^13]: See https://github.com/giovp/spatialdata-sandbox/blob/main/spacem_helanih3t3/README.md
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[^13]:
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See https://github.com/giovp/spatialdata-sandbox/blob/main/spacem_helanih3t3/README.md

‎notebooks/examples/alignment_using_landmarks.ipynb‎

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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.0"
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"version": "3.11.0"
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},
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"vscode": {
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"interpreter": {

‎notebooks/examples/alignment_utils.py‎

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from spatialdata.transformations import Affine
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from spatialdata import SpatialData
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from spatialdata.transformations import (
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BaseTransformation,
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Sequence,
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get_transformation,
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set_transformation,
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)
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AFFINE_VISIUM_XENIUM = Affine(
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[
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[1.61711846e-01, 2.58258090e00, -1.24575040e04],
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[-2.58258090e00, 1.61711846e-01, 3.98647301e04],
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[0.0, 0.0, 1.0],
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],
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input_axes=("x", "y"),
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output_axes=("x", "y"),
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)
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def postpone_transformation(
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sdata: SpatialData,
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transformation: BaseTransformation,
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source_coordinate_system: str,
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target_coordinate_system: str,
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):
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for element_type, element_name, element in sdata._gen_elements():
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old_transformations = get_transformation(element, get_all=True)
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if source_coordinate_system in old_transformations:
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old_transformation = old_transformations[source_coordinate_system]
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sequence = Sequence([old_transformation, transformation])
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set_transformation(element, sequence, target_coordinate_system)

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