Data Analysis for Life Sciences - BENG628

This repository contains R code and workflows from the BENG628 lectures on data analysis techniques applied to life science and medicine.

Course Description

BENG628 focuses on equipping students with the skills to analyze life sciences data using R. The lectures cover a range of topics, including data wrangling, visualization, statistical analysis, and specific applications in genomics, transcriptomics, and other areas.

Repository Contents

• Lecture 1: Introduction to R and Data Exploration
  • BENG628_Lecture1.Rproj: RStudio project file.
  • BENG628_Lecture1.R: R script with code for importing data, basic data types, control structures, and initial data exploration.
  • data/: Folder containing the Clinical_Data_Discovery_Cohort.csv dataset used in the lecture.
• Lecture 2: Exploratory Data Analysis for Life Sciences
  • BENG628_Lecture2.R: R script demonstrating data wrangling with dplyr, ggplot2 visualizations, and an introduction to survival analysis.
  • data/: Folder containing the Global_Health_Statistics.csv dataset.
• Lecture 3: Advanced Data Wrangling and Visualization
  • BENG628_Lecture3.R: R script covering advanced data manipulation techniques, including reshaping, string manipulation, and handling missing data. Also includes more advanced ggplot2 visualizations.
• Lecture 4: Data Visualization and Basic Statistical Analysis
  • BENG628_Lecture4.R: R script focusing on data visualization principles and techniques using ggplot2, as well as basic statistical summaries and functions.
• Lecture 5: RNA-seq Analysis for Rare Diseases
  • BENG628_Lecture5.R: R script demonstrating RNA-seq analysis using DESeq2 for rare genetic disorders, with a case study on Facioscapulohumeral Muscular Dystrophy (FSHD). Includes differential expression analysis, volcano plot visualization, and gene search.
• Lecture 6: Working with Genomic Data
  • BENG628_Lecture6.R: R script demonstrating how to access and analyze genomic data from resources like GEO (Gene Expression Omnibus) and Ensembl.
• Lecture 7: Advanced Genomic Data Analysis
  • BENG628_Lecture7.R: R script covering more advanced genomic data analysis techniques, including differential expression analysis, methylation analysis, and integration of multiple data types.
• Lecture 8: Genome-Wide Association Studies (GWAS)
  • BENG628_Lecture8.R: R script focusing on the analysis and visualization of GWAS data, including using tools like locuszoomr.
• **output/`: Folder to store any output files generated by the scripts.

R Packages Used

• Core Tidyverse:
  • dplyr: Data manipulation
  • tidyr: Data reshaping
  • ggplot2: Data visualization
  • readr: Data import
  • purrr: Functional programming
  • tibble: Modern data frames
  • stringr: String manipulation
• Other Key Packages:
  • survival: Survival analysis
  • survminer: Visualization for survival analysis
  • Seurat: Single-cell RNA-seq analysis
  • DESeq2: RNA-seq differential expression analysis
  • ChIPseeker: ChIP-seq data annotation
  • edgeR: RNA-seq differential expression analysis
  • TCGAbiolinks: Accessing and analyzing TCGA data
  • SummarizedExperiment: Bioconductor class for genomic data
  • here: Project-relative paths
  • ggpubr: Publication-ready plots
  • rstatix: Statistical analysis
  • pheatmap: Heatmaps
  • ComplexHeatmap: Advanced heatmaps
  • biomaRt: Accessing biological databases
  • viridis: Colorblind-friendly palettes
  • factoextra: PCA visualization
  • naniar: Missing data visualization
  • mice: Imputation of missing data
  • randomForest: Random forest modeling
  • ranger: Fast random forest implementation
  • clusterProfiler: Enrichment analysis
  • org.Hs.eg.db: Human gene annotation
  • STRINGdb: Protein-protein interaction analysis
  • FactoMineR: Multivariate analysis
  • cluster: Clustering analysis
  • MVN: Multivariate normality tests
  • lubridate: Working with dates and times
  • airway: Example RNA-seq dataset
  • GEOquery: Downloading GEO data
  • ggseqlogo: Sequence logos
  • rgl: 3D visualizations
  • rentrez: Accessing NCBI databases
  • Biostrings: Biological sequence analysis
  • msa: Multiple sequence alignment
  • ape: Phylogenetic analysis
  • ggtree: Phylogenetic tree visualization
  • phangorn: Phylogenetics
  • locuszoomr: GWAS visualization
  • AnnotationHub: Accessing genomic annotations
  • ensembldb: Working with Ensembl databases
  • plotrix: 3D pie charts (use with caution!)
  • ggrepel: Text labels for plots
  • UCSCXenaTools: Accessing TCGA data from UCSC Xena
  • missMethyl: Methylation pathway analysis
  • IlluminaHumanMethylation450kanno.ilmn12.hg19: Methylation annotation
  • IlluminaHumanMethylation450kmanifest: Methylation manifest
  • ChAMP: Additional methylation analysis tools
  • data.table: Fast data manipulation

How to Run the Code

1. Clone the repository: git clone https://github.com/oktaykaplan/Data-Analysis-for-Life-Sciences.git
2. Open the RStudio project files: For each lecture, open the corresponding .Rproj file in RStudio. This will set the correct working directory.
3. Install required packages: Make sure you have all the necessary R packages installed. You can install them using install.packages() or BiocManager::install() for Bioconductor packages.
4. Run the R scripts: Execute the code in the .R files sequentially, following the instructions and comments provided.

Data Sources

• Lecture 1:

  • Clinical Data Discovery Cohort: https://www.kaggle.com/datasets/imtkaggleteam/clinical-dataset

• Lecture 2:

  • Global Health Statistics Dataset: https://www.kaggle.com/datasets/malaiarasugraj/global-health-statistics

• Lecture 5:

  • data/extracted_files/: Folder for GSE174301 RNA-seq data (to be downloaded manually from https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE174301).

  • output/Lecture5_VolcanoPlot.png: Generated volcano plot highlighting key FSHD-related genes.

    

    

• Lecture 6:

  • GSE37745 (Kidney diseases): https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE37745

• Lecture 7:

  • GSE11121 (Breast cancer cell lines): https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE11121
  • GSE183879 (Obesity and diabetes): https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE183879
  • GSE42865 (Cancer vs. normal methylation): https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE42865
  • TCGA Prostate Adenocarcinoma (PRAD): Accessed via UCSC Xena

• Lecture 8:

  • GWAS Catalog data (GPR75): https://www.ebi.ac.uk/gwas/publications/34210852

Notes

• The code in this repository is provided as-is, for educational purposes.
• It is recommended to run the code in the order of the lectures.
• Some scripts may require additional setup or data download.
• Please refer to the comments within the scripts for specific instructions.

Contributing

If you find any errors or have suggestions for improvement, feel free to submit an issue or a pull request.