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

<strong>scRNA-seq data</strong> Data were processed using cellranger v 4.0.0 with the refdata-gex-GRCh38-2020-A reference. <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 ) <em>raw_feature_bc_matrices.zip</em>: contains the <em>same data</em> as h5files.zip, but also in mtx-format. covid_object_ncomms<em>.RDS</em>: contains the Seurat file with which all analyses were conducted. <em>samples2condition.df</em>: text file containing sample to condition information <strong>Bulk RNA-seq</strong> <em>covid_bulk.zip</em> contains the count matrices extracted from the zUMIs runs for the bulk cohort. <em>nasal_swabs.zip</em> contains the count matrices extracted from the zUMIs run for the nasal swab cohort. The extracted count matrices were then used with the bulk analysis scripts provided with the source code. <strong>Source Code</strong> All <strong>source code</strong> for the publication is available from: https://github.com/mjoppich/covidSC or from tagged releases: https://github.com/mjoppich/covidSC/releases/tag/ncomms 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