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https://github.com/mahshaaban/integratingdatabioc

Source code for the Bioconductor workshop "Integrating gene expression and DNA-binding data using R/Bioconductor"
https://github.com/mahshaaban/integratingdatabioc

bioconductor-packages chip-seq gene-expression rna-se transcription-factors

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Source code for the Bioconductor workshop "Integrating gene expression and DNA-binding data using R/Bioconductor"

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README 

        
![.github/workflows/basic_checks.yaml](https://github.com/MahShaaban/IntegratingDataBioc/workflows/.github/workflows/basic_checks.yaml/badge.svg)



- This workshop is scheduled for February, 27 and is hosted by

[R-Ladies Tunisia](https://rladies.org/tunisia-rladies/) 

as part of the

[R4Bioinfo](R4Bioinfo) series. [(Event Page).](https://www.meetup.com/rladies-tunis/events/276421511/)

- Part of the materials was presented at 

[EuropBioc 2021](https://github.com/MahShaaban/targetShop). 

- The code is organized in a 

[BiocWorkshop](https://github.com/seandavi/BuildABiocWorkshop) 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](https://pubmed.ncbi.nlm.nih.gov/21940749/),

[Wang et al., 2013](https://pubmed.ncbi.nlm.nih.gov/24263090/), and 

[Ahmed et al., 2020](https://pubmed.ncbi.nlm.nih.gov/32894066/)



## 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](https://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](https://github.com/MahShaaban/IntegratingDataBioc)



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



2. Use RStudio locally to install the required packages and run the code after

cloning this repo



```bash

git clone https://github.com/MahShaaban/IntegratingDataBioc

cd IntegratingDataBioc

open -a Rstudio vignette/workshop_code.Rmd 

```



3. Use the docker image 

[mahshaaban/IntegratingDataBioc](https://hub.docker.com/repository/docker/mahshaaban/IntegratingDataBioc/)

and knit the `Rmd` files in `vignettes/` from within Rstudio.

 

```bash

docker pull mahshaaban/target:latest

docker run -e PASSWORD= -p 8787:8787 mahshaaban/target:latest

```

 

An Rstudio session will be accessable at 

[https://localhost:8787/](https://localhost:8787/)

in the browser. The login username is always `rstudio` and the password is 

``.



The packaged is tested using the docker image (option 3) on GitHub 

[Actions](https://github.com/MahShaaban/IntegratingDataBioc/actions).

To make sure everything is working fine on your end, run the following in RStudio

```r

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.frame`s 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](https://github.com/MahShaaban/targetFlow)