Exercise 6 — RNA-seq Visualization Basics

In RNA-seq analysis, visualization is extremely important.

Before performing advanced analysis, researchers often:

  • inspect expression distributions
  • compare samples
  • identify patterns
  • detect outliers
  • visualize biological differences

In this exercise, we will create basic RNA-seq visualizations using base R.

These concepts are foundational for:

  • exploratory data analysis (EDA)
  • quality control (QC)
  • differential expression interpretation
  • publication figures

By the end of this exercise, you should be able to:

  • Create barplots
  • Create histograms
  • Create boxplots
  • Create heatmaps
  • Visualize gene expression patterns
  • Compare Control vs Treatment samples
  • Interpret expression distributions

🧬 Biological Context

Suppose we performed RNA-seq on:

  • 4 Control samples
  • 4 Treatment samples

We now want to visualize expression patterns across genes and samples.

Part 1 — Create Expression Matrix

Task 1.1

Create the following gene vector:

genes <- c(
  "TP53","EGFR","MYC","BRCA1",
  "ACTB","GAPDH","VEGFA","CDKN1A",
  "STAT3","BCL2"
)

Task 1.2

Create the following count matrix:

counts <- matrix(c(

  120,130,125,128,220,230,240,235,
  300,310,295,305,500,520,510,530,
  90,85,88,92,70,72,68,65,
  450,470,460,455,700,720,710,730,
  1000,980,1020,1010,1050,1070,1080,1060,
  900,920,910,905,930,950,940,945,
  140,150,145,148,320,340,330,335,
  200,210,205,198,410,430,420,425,
  180,190,185,188,350,360,355,370,
  500,490,510,505,300,290,310,305

), nrow = 10, byrow = TRUE)

Task 1.3

Assign:

  • row names = genes
  • column names:
Control_1
Control_2
Control_3
Control_4
Treatment_1
Treatment_2
Treatment_3
Treatment_4

Part 2 — Basic Matrix Inspection

Task 2.1

Check:

  • dimensions
  • row names
  • column names

Task 2.2

Calculate:

  • mean expression per gene

Store as:

gene_means

Part 3 — Barplots

Barplots are useful for comparing:

  • genes
  • samples
  • conditions

Task 3.1

Create a barplot of:

  • mean expression per gene

Hint:

barplot(gene_means)

Task 3.2

Improve readability by adding:

  • gene labels
  • main title
  • axis labels

Example arguments:

names.arg
main
xlab
ylab
las

Task 3.3

Questions:

  1. Which genes appear highly expressed?
  2. Which genes appear lowly expressed?

Part 4 — Histograms

Histograms help visualize:

  • expression distributions

Task 4.1

Create a histogram of:

  • ALL expression values in the matrix

Hint:

hist(counts)

Task 4.2

Add:

  • title
  • axis labels

Task 4.3

Interpret:

  1. Are most genes lowly or highly expressed?
  2. Is the distribution symmetric or skewed?

Part 5 — Log Transformation

RNA-seq data is often log-transformed before visualization.

Task 5.1

Create:

log_counts <- log2(counts + 1)

Task 5.2

Create a histogram of:

  • log-transformed counts

Task 5.3

Compare:

  • original histogram
  • log-transformed histogram

Questions:

  1. Which distribution looks more balanced?
  2. Why is log transformation useful?

Part 6 — Boxplots

Boxplots are commonly used for:

  • sample comparison
  • QC analysis
  • identifying outliers

Task 6.1

Create a boxplot for:

  • all samples

Hint:

boxplot(counts)

Task 6.2

Add:

  • sample names
  • title
  • axis labels

Task 6.3

Interpret:

  1. Which samples appear similar?
  2. Are Treatment samples generally higher?
  3. Are there potential outliers?

Part 7 — Heatmaps

Heatmaps are one of the most important RNA-seq visualization tools.

They display:

  • genes
  • samples
  • expression intensity

Task 7.1

Create a heatmap using:

heatmap(counts)

Task 7.2

Create a heatmap using:

  • log-transformed counts

Task 7.3

Interpret:

  1. Which genes appear highly expressed?
  2. Do Treatment samples cluster together?
  3. Why are heatmaps useful?

Part 8 — Gene-Specific Visualization

Task 8.1

Create a barplot for:

  • TP53 expression across samples

Hint:

barplot(counts["TP53", ])

Task 8.2

Create a barplot for:

  • VEGFA expression across samples

Task 8.3

Interpret:

  1. Which gene changes more strongly between conditions?
  2. Which gene appears more stable?

Part 9 — Control vs Treatment Comparison

Task 9.1

Calculate:

  • average expression per sample

Hint:

colMeans(counts)

Task 9.2

Create a barplot of:

  • sample mean expression

Task 9.3

Interpret:

  1. Do Treatment samples show higher expression?
  2. Are Control samples consistent?

Part 10 — Mini Biological Interpretation

Answer in comments:

# 1. Why are visualizations important in RNA-seq analysis?
# 2. Why do we log-transform RNA-seq data?
# 3. Why are boxplots useful for QC?
# 4. Why are heatmaps widely used in transcriptomics?
# 5. Which visualization did you find most informative?

Bonus Challenge

B1

Create a heatmap for only:

  • highly expressed genes

Define highly expressed as:

  • mean expression > 400

B2

Create a boxplot using:

  • log-transformed counts

Compare with:

  • raw counts

B3

Sort genes by mean expression before plotting.

🧠 Key Concepts Learned

  • exploratory data analysis
  • RNA-seq visualization
  • distributions
  • log transformation
  • QC thinking
  • heatmaps
  • sample comparison

These concepts are essential for:

  • RNA-seq QC
  • transcriptomic interpretation
  • differential expression workflows
  • publication-quality analysis
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