ABSTRACT
Recent advancements in the use of single-cell technologies in large cohort studies enable the investigation of cellular response and mechanisms associated with disease outcome, including COVID-19. Several efforts have been made using single-cell RNA-sequencing to better understand the immune response to COVID-19 virus infection. Nonetheless, it is often difficult to compare or integrate data from multiple data sets due to challenges in data normalisation, metadata harmonisation, and having a common interface to quickly query and access this vast amount of data. Here we present Covidscope (http://covidsc.d24h.hk/), a well-curated open web resource that currently contains single-cell gene expression data and associated metadata of almost 5 million blood and immune cells extracted from almost 1,000 COVID-19 patients across 20 studies around the world. Our collection contains the integrated data with harmonised metadata and multi-level cell type annotations. By combining NoSQL and optimised index, our Covidscope achieves rapid subsetting of high-dimensional gene expression data based on both data set level, donor-level (e.g., age and sex of patients) and cell-level (e.g., expression of specific gene markers) metadata, enabling multiple efficient downstream single-cell meta-analysis.