Week 5 - Homework

Write your code directly in a Quarto document.

To create a Quarto document: go to File -> New File -> Quarto Document, then click Create.

Import data.

We need to use two data for this exercise:

• the bulk RNAseq gene expression data from the read-counts.csv file.
• the diffenrential expression (DE) analysis results toy_DEanalysis.

Reminder: the DE results were obtained by comparing SET1 samples to WT samples using data from read-counts.csv

1. Import DE analysis result (toy_DEanalysis.csv) and name it as de_res.

(You can either use the basic function read.csv() or the function read_csv() from the {readr} package.)

2. Import the read-counts.csv file and name it counts.

You can either use the first column (Feature) to name the rows of your data frame or keep the fisrt column as is. It will just change how you filter data later. Here I import the data without using the 1st column to name the rows of my data frame.

Find Genes of Interest

3. Find the genes which satisfy the following conditions:

• log2 fold change < -1 or > 1 (a.k.a, the absolute log2 fold change is bigger than 1)
• adjusted p-value < 0.05

Store the results in a variable target_genes.

Draw Boxplots

4. Load the {ggplot2} package.

5. Draw a boxplot for the 1st gene of the target_genes to show the expression level between SET1 and WT samples.

Hints: you need to extract the expression data for the gene from the counts and build a data frame for the boxplot.

6. Write a function that generates a boxplot for a specified gene, allowing the user to input the gene name. Use your function to create boxplots for all the remaining genes in the target_genes.

---

Click “Render” to generate your Quarto report.

The homework correction is available here: link