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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, workspace
-
Data type
-
Basic R functions
-
Data type conversion |
ÀÓÀçÇö, ¼Èñ¿ø |
10:50 ~ 12:00 |
Data
manipulation with R
- Importing/exporting data (text, SPSS, excel <-> R)
-
Missing values
-
Data management: sorting, merging, reshaping
-
Basic visualization |
Á¤Á¦±Õ,
ÀÓÀçÇö |
12:00 ~ 13:10 |
Áß ½Ä |
13:10 ~ 14:20 |
Statistical Analysis with Biomedical Data I
- Distributions
- Parametric tests
- Non-Parametric stats |
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14:30 ~ 15:40 |
Statistical Analysis with Biomedical Data II
- Correlation
- Regression
- ANOVA |
¼Õ°æ¾Æ,
À±ÁØÈñ |
15:50 ~ 17:00 |
Advanced R
graphics
- Line Plots
- Bar charts
- Histograms
- Scatter plot
- etc |
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(Day 2, 15ÀÏ)
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9:10 ~ 9:30 |
Machine
Learning Algorithms for Biomedical Informatics |
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9:30 ~ 10:40 |
Microarray Data Analysis I
-
Introduction to Microarray Data
-
Normalization methods |
À̼ö¿¬,
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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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