Protective immune trajectories in early viral containment of non-pneumonic SARS-CoV-2 infection

<p><strong>scRNA-seq data</strong></p> <p>Data were processed using cellranger v 4.0.0 with the&nbsp;refdata-gex-GRCh38-2020-A reference.</p> <p><em>h5files.zip</em>: contains all h5-Files of raw feature-barcode counts (e.g. 20094_0001_A_B_raw_feature_bc_matrix.new.h5 )</p> <p><em>raw_feature_bc_matrices.zip</em>: contains the <em>same data</em> as h5files.zip, but also in mtx-format.</p> <p>covid_object_ncomms<em>.RDS</em>: contains the Seurat file with which all analyses were conducted.</p> <p><em>samples2condition.df</em>: text file containing sample to condition information</p> <p><strong>Bulk RNA-seq</strong></p> <p><em>covid_bulk.zip</em> contains the count matrices extracted from the zUMIs runs for the bulk cohort.</p> <p><em>nasal_swabs.zip</em> contains the count matrices extracted from the zUMIs run for the nasal swab cohort.</p> <p>The extracted count matrices were then used with the bulk analysis scripts provided with the source code.</p> <p><strong>Source Code</strong></p> <p>All <strong>source code</strong> for the publication is available from: <a href="https://github.com/mjoppich/covidSC">https://github.com/mjoppich/covidSC</a> or from tagged releases: <a href="https://github.com/mjoppich/covidSC/releases/tag/ncomms">https://github.com/mjoppich/covidSC/releases/tag/ncomms</a></p> <p>When using any of these data, please cite:<br> <br> Pekayvaz et al., Protective immune trajectories in early viral containment of non-pneumonic SARS-CoV-2 infection, Nature Communications 2022</p>