Assignment 12

 

Assignment 12: My R Markdown Primer: Bioinformatics Workflow

Objectives

I explored R Markdown and its capabilities for creating reproducible reports, particularly in bioinformatics. R Markdown allows you to combine narrative text, code, and output in a single document, which is especially useful for RNA-Seq analyses where workflows can be complex and data-intensive.

I practiced:

  • Writing narrative text with headings, bullet points, and emphasis.

  • Using inline and displayed LaTeX equations, for example, the Hardy-Weinberg equilibrium:

Inline: $p^2 + 2pq + q^2 = 1$
Displayed:

p2+2pq+q2=1p^2 + 2pq + q^2 = 1

This helped me document mathematical models used in bioinformatics clearly.

Integration of Code and Narrative

  • I loaded the airway RNA-Seq dataset from Bioconductor.

  • Converted it into a DESeq2 object and performed variance stabilizing transformation (VST).

  • Conducted PCA analysis to visualize sample clustering.

  • All of this was embedded directly in the R Markdown document so results appear alongside the code.

This seamless integration ensures that any changes in code automatically update outputs in the final report.

Challenges

  • Installing Bioconductor packages like DESeq2 and airway on my Mac required careful version management.

  • Applying custom CSS styling was tricky; I had to specify the full path to the styles.css file for it to render correctly.

Reflection

Using R Markdown for bioinformatics has several advantages:

  • Reproducibility: All analysis steps are captured in one document.

  • Clarity: Narrative, code, and plots are linked directly.

  • Collaboration: Sharing the .Rmd file allows colleagues to rerun analyses easily.

  • Visualization: Plots such as PCA are embedded in-line, providing immediate visual feedback.

Compared to traditional static reports, this approach reduces errors and improves documentation quality.


Figure: PCA of RNA-Seq Samples

Conclusion

This assignment helped me:

  • Understand R Markdown syntax and document structure.

  • Apply bioinformatics workflows in a reproducible manner.

  • Generate HTML reports with embedded plots and styled output.

The combination of narrative text, LaTeX math, and code makes R Markdown a powerful tool for bioinformatics reporting.

Links and References

Comments

Post a Comment

Popular posts from this blog

Assignment 5

Image
Assignment 5: Matrix Algebra in R For this assignment, I explored how to create and inspect matrices in R, how to compute matrix inverses and determinants, and how to handle errors when working with non-square matrices. I also reflected on issues of numeric stability and performance that arise in matrix algebra computations. R Code # 1. Create the matrices A <- matrix(1:100, nrow = 10) B <- matrix(1:1000, nrow = 10) # 2. Inspect dimensions dimA <- dim(A) # should be 10 × 10 dimB <- dim(B) # should be 10 × 100 print("Dimensions of A:") print(dimA) print("Dimensions of B:") print(dimB) # 3. Compute inverse and determinant for A invA <- solve(A) detA <- det(A) print("Inverse of A:") print(invA) print("Determinant of A:") print(detA) # 4. Handle non-square matrix B with tryCatch invB <- tryCatch(solve(B), error = function(e) e) detB <- tryCatch(det(B), error = function(e) e) print("Inverse of B attempt:") print...
Read more

Assignment 6

Image
  Assignment 6: Matrix Operations and Construction In this assignment,  I practiced creating and manipulating matrices in R. The goal was to strengthen my understanding of how R handles matrix operations such as addition, subtraction, diagonal construction, and combining blocks of matrices to form structured arrays. R Code 🔹 Task 1: Matrix Addition & Subtraction # Task 1: define A and B A <- matrix(c(2, 0, 1, 3), ncol = 2) B <- matrix(c(5, 2, 4, -1), ncol = 2) # Addition A_plus_B <- A + B print("A + B:") print(A_plus_B) # Subtraction A_minus_B <- A - B print("A - B:") print(A_minus_B) 🔹 Task 2: Create a Diagonal Matrix # Task 2: Create a Diagonal Matrix D <- diag(c(4, 1, 2, 3)) print("Diagonal Matrix D:") print(D) 🔹 Task 3: Construct a Custom 5×5 Matrix # Task 3: Construct a Custom 5x5 Matrix # Step 1: create the first row row1 <- c(3, 1, 1, 1, 1) # Step 2: create the remaining rows: # - first column all 2’s (rows 2–5) # - diagonal...
Read more

Assignment 2

  Assignment 2 - Importing Data and Function Evaluation in R For this part of the assignment, I tested the provided myMean function in RStudio, carefully recorded the errors that appeared, explained why they happened, and then corrected the code so it would successfully return the mean of the given dataset. Original Code:  assignment2 <- c(16, 18, 14, 22, 27, 17, 19, 17, 17, 22, 20, 22) myMean <- function(assignment2) {       return(sum(assignment) / length(someData))  }  Errors from Testing Error 1: > return(sum(assignment) / length(someData))  Error: object 'assignment' not found  Inside the function body, the code used assignment instead of assignment2. Since no variable named assignment exists, R could not evaluate the expression and produced this error. Error 2: > return(sum(assignment2) / length(someData))  Error: object 'someData' not found  Here, assignment2 was corrected in the sum() part, but t...
Read more