Exercise 3 — Mini Gene Expression Analysis (Vectors + Data Frames)

In real RNA-seq analysis, we rarely work with a single gene or a simple vector.

Instead, we combine:

  • multiple genes
  • multiple samples
  • expression matrices
  • sample metadata

This exercise combines everything from Exercises 1 and 2 into a small biological dataset.

Part 1 — Creating a Multi-Gene Dataset

We now extend from a single gene to multiple genes across samples.

Task 1.1

Create the following vectors:

gene <- c("TP53","EGFR","MYC","BRCA1","ACTB","GAPDH","VEGFA","CDKN1A")

sample_id <- c("S1","S2","S3","S4","S5","S6","S7","S8")

expression <- c(12.4, 15.1, 8.7, 20.3, 17.8, 5.2, 13.5, 18.1)

condition <- c("Control","Control","Control","Treatment",
               "Treatment","Treatment","Control","Treatment")

Task 1.2

Combine into a data frame called df.

Part 2 — Inspecting Structure

Task 2.1

Run:

  • str(df)
  • head(df)
  • summary(df)

Questions:

  1. What type is each column?
  2. How many observations are there?

Part 3 — Gene-Level Filtering

Task 3.1

Find all rows where:

  • expression > 15

Task 3.2

Which genes correspond to expression > 15?

Task 3.3

Find genes where:

  • expression < 10

Task 3.4

How many genes fall into:

  • high expression (>15)
  • low expression (<10)

Part 4 — Condition-Based Analysis

Task 4.1

Split data into:

  • Control group
  • Treatment group

Task 4.2

Calculate:

  • mean expression (Control)
  • mean expression (Treatment)

Task 4.3

Which condition has higher overall expression?

Part 5 — Combined Logic (VERY IMPORTANT)

This is where RNA-seq thinking starts.

Task 5.1

Find genes where:

  • condition = “Treatment” AND
  • expression > 15

Task 5.2

Find genes where:

  • condition = “Control” AND
  • expression < 10

Task 5.3

Interpret:

  • What do these groups represent biologically?

Part 6 — Adding Biological Meaning

Task 6.1

Create a new column:

expression_category

Rules:

  • “High” if expression > 15
  • “Medium” if 10–15
  • “Low” if < 10

Task 6.2

Count how many genes fall into each category.

Part 7 — Gene Expression Ranking

Task 7.1

Sort dataset by expression:

  • highest to lowest

Task 7.2

Identify:

  • top 3 highest expressed genes

Part 8 — Mini Biological Interpretation

Answer in comments:

# 1. Which gene appears most active?
# 2. Do Treatment samples show higher expression?
# 3. Why is it useful to categorize expression levels?
# 4. What would change if this was a real RNA-seq dataset?

Bonus Challenge

B1

Create a scaled expression column:

expression_scaled = expression / max(expression)

B2

Find genes that are:

  • high expression AND in Treatment group

B3

Create a summary table:

  • mean expression per condition
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