Instructions

Submission Format [3 marks]

This is assignment is due by 5pm, Thursday 9th April.

  • Submissions must be made as a zip archive containing 2 files:
    1. Your source R Markdown Document (with an Rmd suffix)
    2. A compiled pdf, showing all code
  • All file names within the zip archive must start with your student number. However the name of the zip archive is not important as myUni will likely modify this during submission. See here for help creating a zip archive

All questions are to be answered on the same R Markdown / PDF, regardless of if they require a plain text answer, or require execution of code.

Marks directly correspond to the amount of time and effort we expect for each question, so please answer with this is mind.

We strongly advise working in the folder ~/transcriptomics/assignment2 on your virtual machine. Using an R Project for each individual assignment is also strongly advised.

Creating a zip archive

On Your VM

If all files required for submission are contained on your VM:

  1. Select both files using the Files pane in R Studio
  2. Click export
  3. They will automatically be placed into a single zip archive. Please name this in whatever informative name you decide is suitable, but it should contain the suffix .zip

Windows

If all files are on your on your local Windows machine:

  1. Using File Explorer, enter the folder containing both files
  2. Select all files simultaneously by using Ctrl + Click
  3. Right click on one of the files and select Send to > Compressed (zipped) folder
  4. Rename as appropriate, ensuring the archive ends with the suffix .zip

Mac OS

If all the files are on your *local macOS machine`:

  1. Locate the items to zip in the Mac Finder (file system)
  2. Right-click on a file, folder, or files you want to zip
  3. Select “Compress Items”
  4. Find the newly created .zip archive in the same directory and name as appropriate

Questions

Question 1 [3 marks]

Many early technologies still have a relevant place in modern transcriptomics. Of the technologies covered in Lecture 2: Early Transcriptomic Strategies, which technology might be suitable for analysis of pri-miRNAs? Explain why in one or two brief sentences.

Question 2 [8 marks]

Briefly contrast two of the technologies presented in Lecture 2: Early Transcriptomic Strategies and Lecture 3: Microarrays. Discuss their strengths and limitations, paying particular attention to how each represented a breakthrough at the time they gained prominence.

Question 3 [3 marks]

When performing a statistical test, we usually frame our analysis in terms of a Null Hypothesis and an Alternate Hypothesis, eventually returning a \(p\)-value. Explain why we do this, including clear description of what a \(p\)-value represents?

Question 4 [3 marks]

When conducting a \(T\)-test, we estimate two population-level parameters. Describe both of these, using the context of comparing gene expression patterns across two treatment groups.

Question 5 [10 marks]

To obtain your own set of qPCR data, please execute the following lines of code, using your own student number instead of the example given ("a1234567"). This will create an object called qPCR in your R Environment. This object will contain \(C_t\) values for a gene of interest and a housekeeper gene, across two cell types. Each experiment is run as a series of pairs so that you will have 4 values for each pair (2X Cell Types + 2X Genes).

source("https://uofabioinformaticshub.github.io/transcriptomics_applications/assignments/A2Funs.R")
makeRT("a1234567")

For this question, please perform the following tasks, showing all code. Where suitable, use pander() to display the results.

  1. Generate a clearly labelled boxplot for each gene and cell type.
  2. After generation of the boxplot, comment on the suitability of the housekeeper, paying particular note to the stability of it’s expression between your cell types.
  3. Calculate the \(\Delta C_t\) values for your gene of interest by normalising to the housekeeper
  4. Calculate the \(\Delta \Delta C_t\) values for the comparison between cell types
  5. Using the above \(\Delta \Delta C_t\) values, perform a \(T\)-test using the R function t.test() and interpret the output

Question 6 [10 marks]

For this question, you will need the objects cpm.tsv, topTable.csv and de.tsv. You will be assigned a gene set using the following command, again remembering to use your own student ID number.

source("https://uofabioinformaticshub.github.io/transcriptomics_applications/assignments/A2Funs.R")
chooseGeneSet("a1234567")

For this question, your task is to:

  1. Find which differentially expressed genes belong to this gene-set. These are provided in the object de.tsv, and these should be formed into a character vector.
  2. Restrict this character vector so that it only contains genes within cpm.tsv. (Hint: You can use topTable.csv to map from gene names to gene IDs)
  3. Using pheatmap(), create a heatmap of these genes using the expression values contained in cpm.tsv. For reference, these values are provided as logCPM values, which are suitable for plotting directly. Include an annotation for each sample, indicating which genotype it represents.

Total: 40 marks