/2021Workshop-COVID19

2021Workshop-COVID19

Single-cell COVID-19 data visualization

According to the United Nations, in the last years, the world has shown an increase in population aging and, as a consequence, chronic age-related diseases. From a national perspective, chronic diseases have severe implications in life expectancy and, as a result, generate economic and political pressures. This year with the COVID-19 pandemic, we have been trying to understand if survivors who have been seriously ill will have some sequelae such as pulmonary fibrosis, an irreversible chronic disease. High-throughput technologies have evolved too fast in the last years, and nowadays, single-cell RNA sequencing has become more popular because of the amount of information that we can obtain measuring thousands of single cells in one experiment. This project is focused on analyzing published single-cell data of COVID-19 and look if we can find some known biomarkers of pulmonary fibrosis. We will try classical visualization tools for single-cell data using R packages, but we will be creative and find new ways of visualizing data for finding patterns. Our main objective would be to guide hypothesis generation through the visualization of single-cell COVID data.

Goal: Visualization of single-cell COVID data for hypothesis generation.

Some objectives are:

a) To manipulate single cell data formats and incorporate into R

b) To learn different packages for analyzing single cell data

c) To explore different types of visualization for single cell data

d) To find cell type biomarkers

e) To compare COVID biomarkers with those from pulmonary fibrosis

f) To work as bioinformatic team 

g) To incorporate and exchange information with other teams

h) To present the results

-Slides-Yalbi

Data

Activities

Day 2

  • Import .RDS object into R

  • Explore the balf_human_Chua/covid_nbt_loc.rds and balf_human_Chua/covid_nbt_main.rds objects and decide which one to use

  • Plot a PCA, tSNE and UMAP. Discuss the results

  • Is there any better visualization for the output of VizDimLoadings(pbmc, dims = 1:2, reduction = "pca")?

  • Can you reproduce the UMAP Fig 2 from the paper?

Day3

  1. Creativity to propose different visualization plots (Leo)

  2. Pulmonary Fibrosis in COVID-19: Biomarkers of Pulmonary fibrosis (Saul)

  3. Enrchiment analysis in scRNAseq data of COVID-19 (Basal, Ciliated, and Macrophages) (Ana, Juwy)

Resources

Useful resources:

singleCell RNA-seq

Visualization

Enrichment analysis

Co-expression networks

Others

Pulmonary fibrosis

Additional data