scDiagnostics: systematic assessment of cell type annotation in single-cell transcriptomics data
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| Format: | Recurso digital |
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2026
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| _version_ | 1866901277951131648 |
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| author | Christidis, Anthony-Alexander Ghazi, Andrew Chawla, Smriti Turaga, Nitesh Geistlinger, Ludwig Gentleman, Robert |
| author_facet | Christidis, Anthony-Alexander Ghazi, Andrew Chawla, Smriti Turaga, Nitesh Geistlinger, Ludwig Gentleman, Robert |
| contents | <p>This dataset contains four processed single-cell expression objects used in the manuscript "<em>scDiagnostics: diagnostic tools for assessing cell type annotation quality in single-cell RNA-seq data</em>" (Christidis et al.).</p> <p><strong>Included files:</strong></p> <ul> <li><code>covid_data_sce.rds</code> — COVID-19 PBMC query dataset (severe infection), annotated with SingleR, Azimuth, CellTypist, and scArches</li> <li><code>normal_data_sce.rds</code> — Healthy control PBMC reference dataset</li> <li><code>dss9_data.rds</code> — DSS-induced colitis MERFISH query dataset (day 9), annotated with SingleR, Azimuth, CellTypist, and scArches</li> <li><code>healthy_data.rds</code> — Healthy mouse colon MERFISH reference dataset</li> </ul> <p>All objects are SingleCellExperiment (or SpatialExperiment for MERFISH) format with complete cell type annotations and metadata in <code>colData()</code>.</p> <p><strong>Usage:</strong> Download these files and place in your local <code>data/covid/</code> and <code>data/merfish/</code> directories. See the <a href="https://github.com/ccb-hms/scDiagnosticsManuscript" target="_new">scDiagnostics Manuscript Repository</a> for full analysis code and tutorials.</p> <p><strong>Citation:</strong> Please cite the manuscript and this dataset when using these objects in your work.</p> <p> </p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_18274942 |
| institution | Zenodo |
| language | |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | scDiagnostics: systematic assessment of cell type annotation in single-cell transcriptomics data Christidis, Anthony-Alexander Ghazi, Andrew Chawla, Smriti Turaga, Nitesh Geistlinger, Ludwig Gentleman, Robert <p>This dataset contains four processed single-cell expression objects used in the manuscript "<em>scDiagnostics: diagnostic tools for assessing cell type annotation quality in single-cell RNA-seq data</em>" (Christidis et al.).</p> <p><strong>Included files:</strong></p> <ul> <li><code>covid_data_sce.rds</code> — COVID-19 PBMC query dataset (severe infection), annotated with SingleR, Azimuth, CellTypist, and scArches</li> <li><code>normal_data_sce.rds</code> — Healthy control PBMC reference dataset</li> <li><code>dss9_data.rds</code> — DSS-induced colitis MERFISH query dataset (day 9), annotated with SingleR, Azimuth, CellTypist, and scArches</li> <li><code>healthy_data.rds</code> — Healthy mouse colon MERFISH reference dataset</li> </ul> <p>All objects are SingleCellExperiment (or SpatialExperiment for MERFISH) format with complete cell type annotations and metadata in <code>colData()</code>.</p> <p><strong>Usage:</strong> Download these files and place in your local <code>data/covid/</code> and <code>data/merfish/</code> directories. See the <a href="https://github.com/ccb-hms/scDiagnosticsManuscript" target="_new">scDiagnostics Manuscript Repository</a> for full analysis code and tutorials.</p> <p><strong>Citation:</strong> Please cite the manuscript and this dataset when using these objects in your work.</p> <p> </p> |
| title | scDiagnostics: systematic assessment of cell type annotation in single-cell transcriptomics data |
| url | https://doi.org/10.5281/zenodo.18274942 |