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   R for Bioinformatics and Biomedicine

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¡á ÇÁ·Î±×·¥ (Day 1, 27ÀÏ)

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9:00 ~ 9:10

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9:10 ~ 9:30 Introduction to R ±èÁÖÇÑ
9:30 ~ 10:40 Starting with R
- R Installation, R packages, workspace

- Data type and structure
- Basic R functions: built in functions
- File read and write
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10:50 ~ 12:20 Data manipulation with R
- Vector, matrix

- Index, splice, conditional statement
- Data management: sorting, merging, reshaping
- Apply functions
- User defined function
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12:20 ~ 13:30 Áß  ½Ä 
13:30 ~ 14:40

Statistical Analysis with Biomedical Data I
- Distributions

- Parametric tests

- Non-Parametric stats

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14:50 ~ 16:00

Statistical Analysis with Biomedical Data II
- Correlation
- Regression
- ANOVA

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16:10 ~ 17:20

Advanced R graphics and ggplot2
- Plot, histogram, qqplot, boxplot
- Layout, axis, legend and text
- ggplot2: scatterplot, histogram, boxplot, barplot, density plot
- Error bar, line graph

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¡á ÇÁ·Î±×·¥ (Day 2, 28ÀÏ)

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9:10 ~ 9:30 Machine Learning Algorithms for Biomedical Informatics ±èÁÖÇÑ
9:30 ~ 10:40 Microarray Data Analysis I
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Introduction to Microarray Data
- Normalization methods
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10:50 ~ 12:00 Microarray Data Analysis II
- Identifying DEG: t-test, SAM

- Volcano plot
- FDR
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12:00 ~ 13:10 Áß  ½Ä 
13:10 ~ 14:20

Classification using R
-
K-Nearest Neighbor
- Support Vector Machine

- Logistic regression
- Feature selection

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14:30~ 15:40

Evaluation and Validation
- Cross validation
- Train/validation/test set split
- Empirical p-value, permutation test
- Multiple testing
- Mean squared error rate

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15:50 ~ 17:00

Case study: association of BRCA1 and BRCA2 mutations with survival in ovarian cancer (JAMA 2011)
- DEG extraction from RNA-seq data using TRAPR
- Clustering (K-means, hierarchical)
- Correlation analysis between methylation and expression data
- Survival analysis

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