Course Details

Return to Classes
Course Information
SemesterCourse Unit CodeCourse Unit TitleT+P+LCreditNumber of ECTS CreditsLast Updated Date
2BENG628DATA ANALYSIS FOR LIFE SCIENCE AND MEDICINE3+0+037,515.05.2025

 
Course Details
Language of Instruction English
Level of Course Unit Doctorate's Degree
Department / Program BIOENGINEERING
Type of Program Formal Education
Type of Course Unit Elective
Course Delivery Method Face To Face
Objectives of the Course Applying statistical methods in R for RNA sequencing analysis, variant detection, and interpretation of genetic data.

Developing visualization skills in R for effectively communicating results from genomic and transcriptomic studies.

Critically evaluate and replicate bioinformatics studies by applying R for data analysis and validation.

Creating dashboards and interactive applications that integrate storytelling and exploratory data analysis using advanced R techniques.
Course Content This comprehensive graduate course is tailored for students seeking to master data analysis in life sciences and medicine using R programming. Students will delve into advanced statistical techniques, data visualization, and computational tools specifically designed for handling complex biological datasets like RNA-seq, single-cell RNA-seq, and variant analysis. The curriculum will cover data manipulation, quality control, differential expression analysis, and the integration of multi-omic data to address contemporary research questions in biology and medical sciences.
Course Methods and Techniques Programming Language: R

Data Analysis Techniques:

Differential gene expression analysis

Statistical hypothesis testing

Dimensionality reduction (PCA, t-SNE, UMAP)

Clustering and classification methods

Data Visualization Tools:

ggplot2

ComplexHeatmap

Shiny dashboards

Bioinformatics Packages:

DESeq2, edgeR, limma

Seurat for single-cell analysis

VariantAnnotation, GenomicRanges

Workflow and Documentation:

R Markdown for reproducible reports

Git for version control

Data Types Covered:

Bulk RNA-seq

Single-cell RNA-seq

Genomic variant data

Multi-omic integration
Prerequisites and co-requisities None
Course Coordinator None
Name of Lecturers Associate Prof. Oktay Kaplan . oktay.kaplan@agu.edu.tr
Assistants None
Work Placement(s) No

Recommended or Required Reading
Resources "Bioinformatics Data Skills" – Vince Buffalo Biyolojik veri analizi için temel R, shell ve veri işleme becerilerini öğretir. O’Reilly Media, 2015 ISBN: 978-1449367374 "RNA-seq Data Analysis: A Practical Approach" – Eija Korpelainen et al. RNA-seq verisi ile çalışma konusunda uygulamalı rehber. CRC Press, 2015 ISBN: 978-1482227465 "R for Data Science" – Hadley Wickham & Garrett Grolemund R dilinde veri işleme, görselleştirme ve modelleme üzerine temel kaynak. O’Reilly Media, 2016 Ücretsiz çevrim içi erişim: https://r4ds.had.co.nz
Course Notes Introduction to R and RStudio for Life Sciences

Data Import, Cleaning, and Manipulation (tidyverse, data.table)

Fundamentals of RNA-seq and single-cell RNA-seq technologies

Quality control and normalization of sequencing data

Differential expression analysis using DESeq2, edgeR, and limma

Statistical models for genomic data (linear models, GLMs, Bayesian approaches)

Variant calling and annotation (using Bioconductor packages)

Integrating and visualizing multi-omic data

Interactive visualizations with Shiny and ggplot2

Reproducibility in computational biology (R Markdown, version control)

Case studies in biomedical research

Project presentations and peer feedback
Documents Bioconductor packages
Assignments Project
Exams Nope

Course Category
Mathematics and Basic Sciences %30
Engineering %0
Engineering Design %0
Social Sciences %0
Education %0
Science %30
Health %40
Field %0

Planned Learning Activities and Teaching Methods
Activities are given in detail in the section of "Assessment Methods and Criteria" and "Workload Calculation"

Assessment Methods and Criteria
In-Term Studies Quantity Percentage
Yarıyıl İçi Çalışmalarının Başarı Notunun Katkısı 2 % 30
Proje/Çizim 1 % 70
Total
3
% 100

 
ECTS Allocated Based on Student Workload
Activities Quantity Duration Total Work Load
Proje 1 45 45
Araştırma 1 30 30
Teslim 1 12 12
Derse Devam 12 12 144
Total Work Load   Number of ECTS Credits 7,5 231

 
Course Learning Outcomes: Upon the successful completion of this course, students will be able to:
NoLearning Outcomes
1 Use R packages like DESeq2, limma, and Seurat for RNA-seq and single-cell RNA-seq analysis.
2 Proficiently manipulate, transform, and prepare large biological datasets for analysis using the R programming language and tidyverse packages, such as dplyr and tidyr.
3 Manage and analyze large genetic datasets, identifying variants and performing association studies.
4 Design and conduct bioinformatics experiments in R, from data import to complex statistical modeling.
5 Design dashboards and interactive tools using R for exploratory data analysis and storytelling.

 
Weekly Detailed Course Contents
WeekTopicsStudy MaterialsMaterials
1 Introduction to genomic data types (RNA-seq, WGS, scRNA-seq) Introduction to genomic data types (RNA-seq, WGS, scRNA-seq) Introduction to genomic data types (RNA-seq, WGS, scRNA-seq)
2 Data Import and Cleaning Reading and writing genomic data files (FASTQ, BAM, VCF, GTF) Tidyverse for data wrangling Metadata handling and sample annotation Reading and writing genomic data files (FASTQ, BAM, VCF, GTF) Tidyverse for data wrangling Metadata handling and sample annotation
3 RNA-seq Workflow Part I Overview of RNA-seq experiment design Quality control with FastQC Alignment with STAR or HISAT2 (conceptual overview) Importing count data into R Overview of RNA-seq experiment design Quality control with FastQC Alignment with STAR or HISAT2 (conceptual overview) Importing count data into R
4 RNA-seq Workflow Part II Normalization methods (TPM, RPKM, DESeq2 normalization) Exploratory data analysis (PCA, clustering) Batch effect detection and correction Normalization methods (TPM, RPKM, DESeq2 normalization) Exploratory data analysis (PCA, clustering) Batch effect detection and correction
5 Differential Gene Expression Analysis Using DESeq2 and edgeR for DEG analysis Volcano plots, MA plots Interpreting biological significance of DEGs Using DESeq2 and edgeR for DEG analysis Volcano plots, MA plots Interpreting biological significance of DEGs
6 Single-cell RNA-seq Analysis Overview of scRNA-seq technologies Using Seurat for clustering and visualization Marker gene identification Overview of scRNA-seq technologies Using Seurat for clustering and visualization Marker gene identification
7 Variant Detection and Annotation Variant calling overview (GATK, samtools bcftools) Importing and processing VCF files in R Annotation with biomaRt, VariantAnnotation Variant calling overview (GATK, samtools bcftools) Importing and processing VCF files in R Annotation with biomaRt, VariantAnnotation
8 Functional Enrichment Analysis GO and KEGG enrichment using clusterProfiler Visualizing enriched pathways Gene set enrichment analysis (GSEA) GO and KEGG enrichment using clusterProfiler Visualizing enriched pathways Gene set enrichment analysis (GSEA)
9 Data Integration and Multi-omics Introduction to integrative analysis Combining transcriptomic and genomic data Case studies in multi-omics research Introduction to integrative analysis Combining transcriptomic and genomic data Case studies in multi-omics research
10 Advanced Visualization with ggplot2 and plotly Principles of scientific visualization Customizing ggplot2 for genomics Interactive plots with plotly and heatmaps with ComplexHeatmap Principles of scientific visualization Customizing ggplot2 for genomics Interactive plots with plotly and heatmaps with ComplexHeatmap
11 Interactive Dashboards with Shiny Introduction to Shiny framework Building a simple genomic data dashboard Integrating plots and tables for exploration Introduction to Shiny framework Building a simple genomic data dashboard Integrating plots and tables for exploration
12 Critical Evaluation of Published Studies Reading and assessing bioinformatics papers Replication of selected results using shared datasets Reproducibility and transparency in computational biology Reading and assessing bioinformatics papers Replication of selected results using shared datasets Reproducibility and transparency in computational biology
13 Project Development Students propose and start developing individual or group projects Project planning, data selection, and tool selection In-class feedback and peer review Students propose and start developing individual or group projects Project planning, data selection, and tool selection In-class feedback and peer review
14 Project Presentations Final presentations of dashboards and analysis pipelines Peer and instructor feedback Course wrap-up and reflection Final presentations of dashboards and analysis pipelines Peer and instructor feedback Course wrap-up and reflection

 
Contribution of Learning Outcomes to Programme Outcomes
P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 P11 P12
All 5 5 5 5 4 5 1 4 1
C1 5 5 5 5 4 5 1 4 1
C2 5 5 5 5 4 5 4 1
C3 5 5 5 5 4 5 4
C4 5 5 5 5 4 5 4
C5 5 5 5 5 4 5 4

  Contribution: 1: Very Slight 2:Slight 3:Moderate 4:Significant 5:Very Significant

  
  https://sis.agu.edu.tr/oibs/bologna/progCourseDetails.aspx?curCourse=77844&lang=en
Return to Classes