| No |
R for Bioinformatics and Biomedicine |
| 1 |
Starting with R
- Introduction to R
- R installation, workspace, R package installation
- Basic R functions
|
| 2 |
Data manipulation with R
- Data type / - Importing and exporting data
- Data management / - Missing values
|
| 3 |
Review of Basic Statistics
- Probability distributions / - Summary statistics
- Hypothesis testing, confidence intervals, bootstrap
- Exploratory data analysis using R: basic graphics, correlation coefficients, contingency table
|
| 4 |
Statistical Analysis I: Analysis of means
- Distributions
- Parametric tests - Non-Parametric tests
|
| 5 |
Statistical Analysis II: Analysis of proportions and relations
- Correlation / - Regression / - ANOVA
|
| 6 |
Survival Analysis
- Censoring, Survival data, Survival function
- Cox proportional-hazards regression
- Kaplan-Meier survival curve
- Statistical comparison of survival curves: log-rank, Wilcoxon test
|
| 7 |
Machine learning algorithms for Biomedical Informatics
- Supervised analysis / - Unsupervised analysis
|
| 8 |
Classification using R
- Feature selection - K-Nearest Neighbor
- Support Vector Machine
- Linear Discriminant Analysis
|
| 9 |
Evaluation and Validation
- Over-fitting
- Cross validation: train/validation/test set split
- Empirical p-value, permutation test - Multiple testing correction
|
| 10 |
Advanced R graphics
- Line Plots, Bar charts, Histograms, Scatter plot
- Labels and legends / - Lattice package
|
| 11 |
Microarray Data Analysis I
- Bioconductor packages / - Introduction to Microarray Data
- Normalization methods
|
| 12 |
Microarray Data Analysis II
- Identifying DEG: t-test, SAM
- Volcano plot - FDR / - Probe annotation
|
| 13 |
Gene ontology & Pathway analysis
- Gene clustering / - GO and Pathway enrichment analysis
- R packages for semantic similarity
|
| 14 |
Case study: association of BRCA1 and BRCA2 mutations with survival in ovarian cancer (JAMA 2011)
- DEG extraction from RNA-seq data
- Clustering (K-means, hierarchical)
- Correlation analysis between methylation and expression data - Survival analysis
|
| No |
Python 프 로그래밍 및 바이오 유전체 정보의학 알고리즘 |
| 1 |
입학식 |
| 2 |
파이썬 설치와 실행, 자료형, 연산자의 이해, 문자열 다루기 (1장), |
| 3 |
리스트, 튜플, for/while/if 문 (2장) |
| 4 |
사전, 집합, 파일 다루기 (3장) |
| 5 |
에디터 사용, 함수 |
| 6 |
정규표현식의 이해, 텍스트 파일 다루기: 파싱과 변환 |
| 7 |
유용한 모듈들
자료분석 실습 (1): MeSH(Medical Subject Headings)의 활용 |
| 8 |
자료분석 실습 (2): ICD (International Classification of Diseases) / OMIM (Online Mendelian Inheritance in Man |
| 9 |
공공 데이터를 활용한 분석 (1): SEER (The Cancer Surveillance, Epidemiology and End Results Program) |
| 10 |
공공 데이터를 활용한 분석 (2): PubMed and Taxonomy |
| 11 |
공공 데이터를 활용한 분석 (3): US Census file & CDC Preventive mortality files, 심 사평가원 자료 |
| 12 |
공공 데이터를 활용한 분석 (4) :CDC Mortality Files |
| 13 |
Web과 CGI (Common Gateway Interface) 프로그래밍 |
| 14 |
File Handling Tips & Exel File reading with Python |
