• This workshop is scheduled for February, 27 and is hosted by R-Ladies Tunisia as part of the R4Bioinfo series. (Event Page).
• Part of the materials was presented at EuropBioc 2021.
• The code is organized in a BiocWorkshop format.

Integrating gene expression and DNA-binding data using R/Bioconductor

Description

Researchers use ChIP binding data to identify potential transcription factor binding sites. They use gene expression data from sequencing or microarrays to quantify the effect of the factor over-expression or knockdown on its targets. The integration of the binding and expression data therefore can be used to improve the understanding of a transcription factor function. In this workshop, I present a complete workflow for integrating the gene expression (microarray/RNA-seq) and DNA-binding data (ChIP-seq) to predict the combined function of two transcription factors using R/Bioconductor. The example we will be using in the workshop is from real datasets of two functionally and evolutionary related transcription factors YY1 and YY2 in HeLa cells. We will try to identify the factor-specific and the shared targets of the factors in this particular cell line. Then we will use a technique find out the aggregate functions of the factors on their individual (inducer or repressor) and common targets (cooperative or competitive).

Pre-requisites

• Basic knowledge of R syntax
• Familiarity with gene expression and DNA-binding data
• Readings: Tang et al., 2011, Wang et al., 2013, and Ahmed et al., 2020

Participation

Participants are expected to walk through the code (rmarkdown document). An introduction will be given at the beginning to introduce the motivation of the analysis and the relevant Bioconductor packages.

There are a few ways to run the code

1. Use RStudio Cloud to run a cloud instance of RStudio (free). Create a New Project > New Project from Git Repository and paste this URL in the pop up box MahShaaban/IntegratingDataBioc

PS: Hit or miss due to limited resources on the free tier

1. Use RStudio locally to install the required packages and run the code after cloning this repo

git clone https://github.com/MahShaaban/IntegratingDataBioc
cd IntegratingDataBioc
open -a Rstudio vignette/workshop_code.Rmd 

1. Use the docker image mahshaaban/IntegratingDataBioc and knit the Rmd files in vignettes/ from within Rstudio.

docker pull mahshaaban/target:latest
docker run -e PASSWORD=<a_password> -p 8787:8787 mahshaaban/target:latest

An Rstudio session will be accessable at https://localhost:8787/ in the browser. The login username is always rstudio and the password is <a_password>.

The packaged is tested using the docker image (option 3) on GitHub Actions. To make sure everything is working fine on your end, run the following in RStudio

rcmdcheck::rcmdcheck(args = c("--no-manual"), error_on = "warning", check_dir = "check")

There should be no errors or warnings.

R / Bioconductor packages used

• target

Data management

• GenomicRanges
• GenomicFeatures
• rtracklayer
• AnnotationDbi
• readr
• dplyr
• tidyr
• purrr

Annotation packages

• TxDb.Hsapiens.UCSC.hg19.knownGene
• org.Hs.eg.db

Time outline

Activity	Time
Bioconductor packages and classes	20m
Introduction to the target package	20m
Code-walkthrough: A use case of YY1 and YY2 in HeLa cells	20m

Workshop goals and objectives

The workshop aims to teach participants how to use R/Bioconductor packages to read in differential expression and binding peaks data, run a predictive analysis and explore its output. I hope that by providing a complete realistic example, participants would develop an understanding of the issues and the importance of integrating those two types of data. Ideally, participants would be able to adapt this code and the workflow to apply this kind of analysis to their own datasets.

Learning goals

• Learn to read differential expression and binding peaks data into the appropriate R objects
• Learn to use Bioconductor packages to extract the genomic annotation
• Learn to prepare the expression and binding data for target analysis
• Understand the target output through the package visualization and testing tools

Learning objectives

• Read data into R data.frames and Bioconductor GRanges objects
• Extract information from Bioconductor annotation packages TxDb and org.db
• Apply the target analysis using associated_peaks and direct_targets functions
• Visualize the output using the cumulative distribution functions through plot_predicitons
• Test the results using KS test through test_predicitons

This workshop is based on a workflow article: a draft