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R for Bioinformatics and Biomedicine
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° »ç |
9:00 ~ 9:10 |
µî
·Ï |
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 |
ÀÓÀçÇö, ¼Èñ¿ø |
10:50 ~ 12:20 |
Data
manipulation with R
- Vector, matrix
-
Index, splice, conditional statement
-
Data management: sorting, merging, reshaping
-
Apply functions
-
User defined function |
ÀÓÀçÇö, ¹ÚÁöÇý |
12:20 ~ 13:30 |
Áß ½Ä |
13:30 ~ 14:40 |
Statistical Analysis with Biomedical Data I
- Distributions
- Parametric tests
- Non-Parametric stats |
¹ÚÂùÈñ, ÀÓ¿µ±Õ |
14:50 ~ 16:00 |
Statistical Analysis with Biomedical Data II
- Correlation
- Regression
- ANOVA |
¹ÚÂùÈñ, ¾È¼±ÁÖ |
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, 16ÀÏ)
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ÁÖ Á¦ |
° »ç |
9:10 ~ 9:30 |
Machine
Learning Algorithms for Biomedical Informatics |
±èÁÖÇÑ |
9:30 ~ 10:40 |
Microarray Data Analysis I
-
Introduction to Microarray Data
-
Normalization methods |
À̼ö¿¬,
¹é¼ö¿¬ |
10:50 ~ 12:00 |
Microarray Data Analysis II
- Identifying DEG: t-test, SAM
-
Volcano plot
-
FDR |
À̼ö¿¬,
¹é¼ö¿¬ |
12:00 ~ 13:10 |
Áß ½Ä |
13:10 ~ 14:20 |
Classification using R
-
K-Nearest Neighbor
- Support Vector Machine
- Logistic regression
- Feature selection |
¹ÚÂùÈñ,
¼Èñ¿ø |
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 |
¹ÚÂùÈñ,
ÀÓÀçÇö |
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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