# COVID-Datathon: Biomarker identification for COVID-19 severity based on BALF scRNA-seq data

arXiv: https://arxiv.org/abs/2110.04986 · HTML: https://mlsanity.com/paper/2110.04986/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 12 references that are in the map.
- First posted on arXiv 11 Oct 2021.

## Builds on (most cited first, 1 of 1)

- [SimCD: Simultaneous Clustering and Differential expression analysis for single-cell transcriptomic data](https://mlsanity.com/paper/2104.01512/) (2021)

## Abstract (arXiv)

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emergence began in late 2019 and has since spread rapidly worldwide. The characteristics of respiratory immune response to this emerging virus is not clear. Recently, Single-cell RNA sequencing (scRNA-seq) transcriptome profiling of Bronchoalveolar lavage fluid (BALF) cells has been done to elucidate the potential mechanisms underlying in COVID-19. With the aim of better utilizing this atlas of BALF cells in response to the virus, here we propose a bioinformatics pipeline to identify candidate biomarkers of COVID-19 severity, which may help characterize BALF cells to have better mechanistic understanding of SARS-CoV-2 infection. The proposed pipeline is implemented in R and is available at https://github.com/namini94/scBALF_Hackathon.