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| Autore principale: | |
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| Natura: | Recurso digital |
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Zenodo
2025
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| Accesso online: | https://doi.org/10.5281/zenodo.15790389 |
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Sommario:
- <p><strong>Nichesphere</strong> is an sc-verse compatible Python library which allows the user to find differential co-localization domains / niches based on cell type pair co-localization probabilities in different conditions. Cell type pair co-localization probabilities can be obtained in different ways, for example, through deconvolution of spatial transcriptomics / PIC-seq data (getting the probabilities of finding each cell type in each spot / multiplet) ; or counting cell boundaries overlaps for each cell type pair in single cell spatial data (MERFISH , CODEX …).</p> <p>It also offers the possibility to look at biological process based differential communication among differential co-localization domains based on Ligand-Receptor pairs expression data, such as results from CrossTalkeR [ref.].</p> <p>In our first example on Differential co-localization and process based differential communication analysis, we will use data from the Myocardial Infarction atlas from Kuppe, C. et. Al., 2022 to find differential co-localization domains and underlying differential cell - cell communication networks related to ischemia:</p> <p>Dataset:</p> <p>FileName: heart_MI_ST_SC_23samples.h5mu<br>Modalities: ST+scRNAseq<br>Phenotypes: 'border', 'fibrotic', 'ischemic', 'myogenic', 'remote'<br>Filtering: minimum 1500 cells per sample</p> <p>Annotation (for scRNAseq modality, mudata['sc']):</p> <p>- mudata.obs column: "cell_type": 11 </p> <p>- mudata.obs column: "cell_subtype2" 33</p> <p>Phenotype (in scRNAseq modality, mudata['sc']):</p> <p>- mudata.obs column: "sampleType"</p> <p>Phenotype based ID (in both modalities, mudata['sc'] and mudata['visium']):</p> <ul> <li>mudata.obs column: "patient_region_id"</li> </ul> <p>Database:</p> <p>Filename: nichesphereDB_pmid.csv</p> <p>Nichesphere fibrosis related database contains ligands related to the biological processes below:</p> <div dir="ltr"> <table><colgroup><col><col><col></colgroup> <tbody> <tr> <td> <p dir="ltr">Process</p> </td> <td> <p dir="ltr">Database</p> </td> <td> <p dir="ltr">Number of ligands</p> </td> </tr> <tr> <td> <p dir="ltr">Collagens</p> </td> <td> <p dir="ltr">Matrisome (PMID:36399478)</p> </td> <td> <p dir="ltr">45</p> </td> </tr> <tr> <td> <p dir="ltr">ECM affiliated</p> </td> <td> <p dir="ltr">Matrisome (PMID:36399478)</p> </td> <td> <p dir="ltr">129</p> </td> </tr> <tr> <td> <p dir="ltr">ECM glycoproteins</p> </td> <td> <p dir="ltr">Matrisome (PMID:36399478)</p> </td> <td> <p dir="ltr">168</p> </td> </tr> <tr> <td> <p dir="ltr">ECM regulators</p> </td> <td> <p dir="ltr">Matrisome (PMID:36399478)</p> </td> <td> <p dir="ltr">204</p> </td> </tr> <tr> <td> <p dir="ltr">Proteoglycans</p> </td> <td> <p dir="ltr">Matrisome (PMID:36399478)</p> </td> <td> <p dir="ltr">36</p> </td> </tr> <tr> <td> <p dir="ltr">SecretedFactors</p> </td> <td> <p dir="ltr">Matrisome (PMID:36399478)</p> </td> <td> <p dir="ltr">265</p> </td> </tr> <tr> <td> <p dir="ltr">Chemokine</p> </td> <td> <p dir="ltr">Cytosig (PMID:34594031)</p> </td> <td> <p dir="ltr">42</p> </td> </tr> <tr> <td> <p dir="ltr">Cytokine</p> </td> <td> <p dir="ltr">Cytosig (PMID:34594031)</p> </td> <td> <p dir="ltr">86</p> </td> </tr> <tr> <td> <p dir="ltr">GrowthFactor</p> </td> <td> <p dir="ltr">Cytosig (PMID:34594031)</p> </td> <td> <p dir="ltr">130</p> </td> </tr> <tr> <td> <p dir="ltr">Inhibitory</p> </td> <td> <p dir="ltr">Cytosig (PMID:34594031)</p> </td> <td> <p dir="ltr">36</p> </td> </tr> </tbody> </table> </div> <p>Folders "coloc", "comm" and "preprocessing" contain the necessary data to run each related tutorial, including jupyter notebooks. The rest of data files are explained along the tutorials.</p> <p> </p>