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scRNA-seq data Data were processed using cellranger v 4.0.0 with the refdata-gex-GRCh38-2020-A reference. h5files.zip: contains all h5-Files of raw feature-barcode counts (e.g. 20094_0001_A_B_raw_feature_bc_matrix.new.h5 ) raw_feature_bc_matrices.zip: contains the same data as h5files.zip, but also in mtx-format. covid_object_ncomms.RDS: contains the Seurat file with which all analyses were conducted. samples2condition.df: text file containing sample to condition information Bulk RNA-seq covid_bulk.zip contains the count matrices extracted from the zUMIs runs for the bulk cohort. nasal_swabs.zip 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. Source Code All source code 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: Pekayvaz et al., Protective immune trajectories in early viral containment of non-pneumonic SARS-CoV-2 infection, Nature Communications 2022
SARS-CoV-2, scRNA-seq
SARS-CoV-2, scRNA-seq
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
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| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
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