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   R for Bioinformatics and Biomedicine

ÀÓ»ó ÀÇ·áÁ¤º¸¿Í À¯Àüü µ¥ÀÌÅÍÀÇ ÅëÇպм®¿¡¼­ ¿Ã¹Ù¸¥ Åë°èÇÐÀû µµ±¸ »ç¿ëÀÇ Á߿伺Àº ¾Æ¹«¸® °­Á¶Çصµ Áö³ªÄ¡Áö ¾Ê½À´Ï´Ù. °ø°³ ¼ÒÇÁÆ®¿þ¾îÀÎ R statistical package´Â dzºÎÇÑ Åë°èºÐ¼® ¶óÀ̺귯¸®¿Í ÀÚ·á󸮸¦ À§ÇÑ ÄÄÇ»ÅÍ ÇÁ·Î±×·¡¹Ö ȯ°æ ¹× ¼öÁØ ³ôÀº ±×·¡ÇÁ ±×¸®±â¸¦ Áö¿øÇÕ´Ï´Ù. ¼­¿ïÀÇ´ë Á¤º¸ÀÇÇнǿ¡¼­´Â ¹ÙÀÌ¿ÀÁ¤º¸Çаú ÀÇ°úÇÐ ºÐ¾ß¿¡¼­ RÀÇ È°¹ßÇÑ º¸±ÞÀ» À§ÇÑ ½Ç½À ¿÷¼¥À» ¸¶·ÃÇß½À´Ï´Ù. º» ±³À°°úÁ¤ÀÌ ½ÇÁúÀûÀÎ »ý¸íÀÇ°úÇÐ µ¥ÀÌÅÍ¿Í À¯Àüü µ¥ÀÌÅÍÀÇ ºÐ¼®À» À§ÇÑ R È°¿ëÀÇ ±âÃÊ°¡ µÇ±â¸¦ ±â¿øÇÕ´Ï´Ù.

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ÀÏ ½Ã : 2014³â 5¿ù 15ÀÏ(¸ñ) ~ 16ÀÏ(±Ý), ¿ÀÀü 9½Ã ~ ¿ÀÈÄ 5½Ã                   

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ÁÖ ÃÖ : ¼­¿ï´ëÇб³º´¿ø À¯Àüü ÀÓ»ó Á¤º¸ºÐ¼® ÈƷü¾ÅÍ
ÁÖ °ü : ¼­¿ï´ë ½Ã½ºÅÛ¹ÙÀÌ¿ÀÁ¤º¸ÀÇÇÐ ¿¬±¸¼¾ÅÍ, ¼­¿ïÀÇ´ë Á¤º¸ÀÇÇнÇ
µî ·Ï : 50¸í Á¦ÇÑ, °­ÀDZ³Àç Á¦°ø
  *½Ä´ç °ø»ç °ü°è·Î Áß½ÄÀÌ Á¦°øµÇÁö ¾Ê»ç¿À´Ï ¾çÇعٶø´Ï´Ù. ±Ùó ½Ä´çÀ» ÀÌ¿ëÇØ ÁÖ½Ã¸é °¨»çÇÏ°Ú½À´Ï´Ù.

¡á ÇÁ·Î±×·¥ (Day 1, 15ÀÏ)

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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

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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, 16ÀÏ)

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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
À̼ö¿¬, ¹é¼ö¿¬
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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