sepsis_and_cancer

Code and metadata associated with project:

Sepsis-associated pathways segregate cancer groups

Himanshu Tripathi, Samanwoy Mukhopadhyay & Saroj Kant Mohapatra

• (https://bmccancer.biomedcentral.com/articles/10.1186/s12885-020-06774-9)

Summary

Transcriptome-based systems biology approach segregates cancer into two groups ((termed Sepsis-Like Cancer, or SLC and Cancer Alone, or CA) based on similarity with SS. Host response to infection plays a key role in pathogenesis of SS and SLC. However, we hypothesize that some component of the host response is protective in both SS and SLC.

How to set up data and code for analysis

Step 1: Installation of the three Data Packages ssnibmg, tcnibmg and tcnibmgML

• Download the following data packages files from https://figshare.com/ using the links provided below:
• ssnibmg_1.0.tar.gz from https://doi.org/10.6084/m9.figshare.4592800.v1
• ssgeosurv_1.0.tar.gz, tcnibmg_1.0.tar.gz and tcnibmgML_1.0.tar.gz from https://doi.org/10.6084/m9.figshare.8118413.v3
• Change the directory to where you saved the files. Start R.
• At the R prompt, issue the following commands:

install.packages(pkgs="ssnibmg_1.0.tar.gz", repos=NULL) install.packages(pkgs="ssgeosurv_1.0.tar.gz", repos=NULL) install.packages(pkgs="tcnibmg_1.0.tar.gz", repos=NULL) install.packages(pkgs="tcnibmgML_1.0.tar.gz", repos=NULL)

• Now the three data packages are installed on your computer.
• Check with the following commands:

library("ssnibmg") library("ssgeosurv") library("tcnibmg") library("tcnibmgML")

Step 2: Running the analysis

• It is assumed that you have access to a folder tcnibmgdoc
• Start R and set the working directory to tcnibmgdoc
• Run the script main.R