---
title: "HW7 - RNA-Seq Analysis"
linkTitle: "HW6: RNA-Seq Analysis"
description: >
type: docs
weight: 307
---

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<div style="text-align: right"> 
Source code downloads: &nbsp; <a href="https://raw.githubusercontent.com/tgirke/GEN242//main/content/en/assignments/Homework/HW07/HW07.R" target="_blank">[ .R ]</a>
</div>
<p></p>

## A. Unstranded and strand-specific read counting 

- __Task 1__: Run the [RNA-Seq
  workflow](https://girke.bioinformatics.ucr.edu/GEN242/tutorials/sprnaseq/sprnaseq/)
  with the toy data from start to finish. Next, rerun the code of the `read_counting` step 
  [here](https://girke.bioinformatics.ucr.edu/GEN242/tutorials/sprnaseq/sprnaseq/#read-counting-with-summarizeoverlaps)
  until (including) the line that creates the `bfl` object with the `BamFileList` function
  and then continue with the code shown below. This version performs the read counting the same way as the workflow but lacks the parallelizaton 
  with `bplapply`. 

  In the read quantification step with `summarizeOverlaps` generate count
  tables for exons by genes (`eByg`) of the following three strand modes:

   1. Unstranded 
   2. Strand-specific for positive (sense) strand
   3. Strand-specific for negative (antisense) strand
   
   The solution for generating the unstranded read counts is given below. Note,
   the upstream code lines of the read counting step in the RNA-Seq workflow only 
   need to be rerun to generate the proper inputs for this taks. Thus, they are not 
   required to be included in the homework results (see `HW7.R` below).


```r
unstranded <- summarizeOverlaps(eByg, bfl, mode="Union", 
                                                ignore.strand=TRUE, 
                                                # preprocess.reads=invertStrand,
                                                inter.feature=FALSE, 
                                                singleEnd=FALSE)
unstranded <- assays(unstranded)$counts
unstranded[1:4,]
```

Before attempting to solve this homework task please read the vignette
_Counting reads with `summarizeOverlaps`_
([here](http://bioconductor.org/packages/release/bioc/html/GenomicAlignments.html))
from the `GenomicAlignments` package that defines the `summarizeOverlap`
function. In addition, the help file for `?summarizeOverlaps` provides useful information.

- __Task 2__: Provide R code that demonstrates that the two strand-specific count tables sum up to very similar values as the unstranded count table. 

- __Task 3__: Explain the utility (biological relevance) of the different strand counting modes used under Task 1. Include your explanation as comment text in your homework script (see `HW7.R` below). 

Note, for Tasks 1-3 only the code and/or text needs to be included in the homework submission (no data/result files). For details see below.

## B. Read counting for different feature types
- __Task 4__: Compute strand-specific count tables for the positive (sense) strand of the following feature types. The help files of `?exonsBy` and `?transcripts` provide useful information for solving these tasks. 

   1. Genes
   2. Exons
   3. Exons by genes 
   4. Introns by transcripts
   5. 5'-UTRs by transcripts

Note, for Tasks 4 only include the code and/or text in your homework submission (no data/result files). 

## C. DEG analysis

- __Task 5__: Perform the DEG analysis with `edgeR` as outlined under section 6 of the RNA-Seq workflow [here](https://girke.bioinformatics.ucr.edu/GEN242/tutorials/sprnaseq/sprnaseq/#run-edger). 
Use in one case for the DEG analysis the unstranded count table as input (from Task 1.1) and in another the sense strand count table (from Task 1.2). 
Compare the DEG result of the two methods in two separate 4-way Venn diagrams for the same sample comparisons used in the workflow example 
[here](https://girke.bioinformatics.ucr.edu/GEN242/tutorials/sprnaseq/sprnaseq/#venn-diagrams-of-deg-sets).

   1. 4-way Venn diagram for unstranded count table
   2. 4-way Venn diagram for sense strand count table

Note, for Tasks 5 include both the code and the resulting image files in your homework submission. 

## Homework submission

Please submit the homework results in one well structured and annotated R
script to your private GitHub repository under `Homework/HW7/HW7.R`, as well as the two Venn diagrams from Task 5. Instead of an R script the homework can be submitted in form of an R Markdown (*Rmd) file. If the latter R Markdown option is chosen, the please include there only the homework solutions, not the entire RNA-Seq workflow itself.


### Due date

This homework will be due Tue, May 20 at 6:00 PM.

## Homework Solutions

   + [hw7_solution.R](https://raw.githubusercontent.com/tgirke/GEN242/main/static/custom/hw_solutions/hw7_solution.R)
   + [Venn diagrams](http://cluster.hpcc.ucr.edu/~tgirke/Teaching/GEN242/hw7_venn/)