COVID19

Commands and codes used for the RNASeq and Proteomics data analysis SARS-Cov2 infected samples

About

The project aims to identify host cellular response against SARS-CoV2 infection in Huh7 cell lines. A time-series integrative proteo-transcriptomics analysis design was employed in the project. The Huh7 cells were collected at 24, 48, 72 hours post infection and RNASeq & Proteomics data were generated. Also an uninfected sample was included which used as a control. There were three replicates in the design. Proteomics and transcriptomics data were analysed using R package LIMMA, both for pairwise and time series analysis.

Data input and output

Inputs :

Proteomics : Proteomics raw data with uniprot accession as rows and samples as columns. Annotation file with as columns 1) Uniprot Accession 2) Gene name.
Transcriptomics : Transcriptomics count data with Ensembl ID as rows and samples as columns.

Outputs :

PCA, Distribution after normalization
Up or down regulated proteins & transcripts (time series analysis)
Up or down regulated proteins & transcripts (pair-wise analysis)

Outline

Pre-processing (Proteomics)

1) Count missing values / PCA 2) Normalization 3) Plotting Distribution / Dimension reduction : PCA 2

Pre-processing (Transcriptomics)

1) Calculation of Normalization factors 2) Filtering of low abundant genes 3) Voom transformation

Expression analysis

1) LIMMA : 6 pair-wise comparisons (UNINF/T24 / T48 / T72) 2) LIMMA : time series analysis (T24, T48, T72)

Figure generation

1) Pathway enrichment heatmap (All analysis) 2) Sankey plot (top 4 pathways) 3) Scatter plots (top 4 pathways)

Installation

Clone repository

git clone https://github.com/neogilab/COVID19.git
cd COVID19/

Requirements

All the analysis were performed in Linux-ubuntu environment.

Python3.5+ (Please check https://github.com/zqfang/gseapy for GSEAPY requirements.)
R packages (Analysis and figure generation) :

Rscript source/requierements.R
# This command will install the following packages:
# NormalyzerDE==1.4.0
# ggfortify==0.4.9
# xlsx==0.6.3
# gplots==3.0.3 
# edgeR==3.28.1
# limma==3.42.2
# matrixStats==0.56.0
# reshape2==1.4.3
# ggalluvial==0.11.1
# ggplot2==3.3.0
# dplyr==0.8.5

Execution

All data should be placed in a folder called data (transcriptomics data must be in a file named Transcriptomics and proteomics data in a file named Proteomics).

Proteomics

Rscript -e "rmarkdown::render('covid-19_proteomics_preprocessing.Rmd')"
Rscript -e "rmarkdown::render('cov-19_time_series_proteomics_antiviral.Rmd')"
Rscript -e "rmarkdown::render('covid-19_transcriptomics_pairwise_comp.Rmd')"

Transcriptomics

Rscript -e "rmarkdown::render('cov-19_time_series_transcriptomics_antiviral.Rmd')"
Rscript -e "rmarkdown::render('covid-19_transcriptomics_pairwise_comp.Rmd')"

Gene set enrichment analysis

python3 GSEA.py

make figures

Rscript -e "rmarkdown::render('covid-19_figures_heatmap.Rmd')"
Rscript -e "rmarkdown::render('covid-19_figures_top_4_pathways.Rmd')"

Network Analysis

Code used for network analysis of proteomics and transcriptomics analysis is included in the folder "network_analysis"

neogilab / COVID19

readme