ReproCanAtlas

Table of Contents

- [Background](#Background)
- [Data](#data)
- [Usage](#usage)
    - [Installation](#installation)
    - [Requirements](#requirements) _Can be named Dependencies as well_
    - [Activate conda environment](#activate-conda-environment) _Optional_
    - [Steps to run ](#steps-to-run) _Optional depending on project_
        - [Step-1](#step-1)
        - [Step-2](#step-2)
- [Results](#results) _Optional depending on project_
- [Team Members](#team-members)

Background

We will create the start of ReproCanAtlas with Endometrial Cancer. This project will integrate single cell RNASeq, bulk transcriptomics, and genomic data of reproductive/gynecological cancers in order to develop an atlas or knowledge base of those cancers and identify reproductive cancer signatures and therapeutic targets. We will use TCGA via recount3, TissueNexus, COSMIC, DGIdb, and CellMinerCDB. Lastly, we will integrate our findings into an interactive shiny app.

Endometrial Cancer Papers for overview: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3518445/ https://cancerimagingjournal.biomedcentral.com/articles/10.1186/s40644-018-0180-6 https://www.cancer.gov/types/uterine/research

Data

Data will be accessed through TCGA for Whole Exome Sequencing and bulk RNAseq. Single Cell RNAseq (scRNAseq) will be from paper(s). These will be provided through Cheaha (UAB's HPC) or through smaller count matrices that you can run locally

Usage

The atlas can be used for preliminary analysis by noncomputational groups as well as for clinicians. The goal is to be as user friendly as possible

Future Directions

The goal is to get this atlas started so that we can add multiple female reproductive cancers to it. We will also need to do the following

• Find better single cell for Endometrial Cancer (the one we had included some peculiar FASTQs)
• Make the Shiny app interactive - Make where you can select data types and the graphs move
  - Make where you can select genes for RNAseq to view DEGs - Make an option to select cancer types and be able to select multiple Genomic options and multiple cancers.
• Include quantitative data underneath the graphs.

Installation of Packages

The following are required to do this analysis: R/ RStudio: https://www.rstudio.com/products/rstudio/download/

Need access to Cheaha for some of the larger data

Packages within R

• Shiny: https://shiny.rstudio.com
• Seurat for single cell: https://satijalab.org/seurat/
• MAFtools for WXS: https://www.bioconductor.org/packages/devel/bioc/vignettes/maftools/inst/doc/maftools.html
• DEseq2 for bulk RNAseq: https://bioconductor.org/packages/release/bioc/html/DESeq2.html

Requirements

Same as Installation

Results

We were able to finish

• a basic shiny app with some of the options present
• DESeq2 analysis for TCGA-OV and TCGA-UCEC
• MAFtools based analysis for TCGA-UCEC

Team Members

Emily Page | [email protected]| Team Leader

Ryan Strickland | [email protected] | Team Participant

Fion Chan | [email protected] | Team Participant

Ayesha Sanjana | [email protected] | Team Participant