Place: Room 107, New Building of the Institute of Information Science, Academia Sinica.
Time: Thursday 9:00-12:00
Chair: Dr. Grace Hsieh
Outline: Introduction to very useful and advanced statistical methods in computational biology. The topics include: Analysis of next generation sequencing (NGS) Data (e.g., RNA-Seq and ChIP-Seq), maximum likelihood estimation, the EM algorithm, Bayesian inference, Monte Carlo methods, Resampling (Bootstrap & permutation test), Human Genetics, clustering and classification, dimension-reduction and missing data.
Textbook: N/A
Reference book: papers will be given by instructors
Lecturers: Hsuan-Yu Chen, Yen-Tsung Huang, Grace S. Shieh, Han-Ming Wu, Hsin-Chou Yang, Shinsheng Yuan
TA: N/A (Please refers to the lectures respectively shall you have any questions of each class)
E-mail:
Office hours: N/A
Grades: Midterm exam 50%. Final exam 50%.
Week Date Topics/Brief Description sub-topics/Detail Descriptions Lecturers
1 2018/02/22 Analysis of NGS data I N/A Dr. Hsuan-Yu Chen
2 2018/03/01 Analysis of NGS data II N/A Dr. Hsuan-Yu Chen
3 2018/03/8 Maximum likelihood estimates and the EM algorithm I Maximum likelihood estimates Dr. Yen-Tsung Huang
4 2018/03/15 Maximum likelihood estimates and the EM algorithm II The EM algorithm Dr. Yen-Tsung Huang
5 2018/03/22 Bayesian methods Bayesian methods Dr. Grace S. Shieh
6 2018/03/29 Monte Carlo Markov Chains Monte Carlo Markov Chains Dr. Shin-Sheng Yuan
7 2018/04/05 Holiday
8 2018/04/12 Resampling methods I Permutation test, jacknife Dr. Grace S. Shieh
9 2018/04/19 Midterm Exam
10 2018/04/26 Resampling methods II Bootstrap Dr. Grace S. Shieh
11 2018/05/03 Statistics in Human Genetics I Concept of Statistical Genetics, International Genomic Projects, Linkage Analysis Dr. Hsin-Chou Yang

12 2018/05/10 Statistics in Human Genetics II Genetic Association, Gene-Gene interaction, Rare Variant Analysis Dr. Hsin-Chou Yang

13 2018/05/17 Advanced regression and dimension reduction I N/A Dr. Shinsheng Yuan
14 2018/05/24 Advanced regression and dimension reduction II N/A Dr. Shinsheng Yuan
15 2018/05/31 N/A NA/ N/A
16 2018/06/07 Clustering, classification
and data visualization I Clustering:
(a) K-Means Clustering, Fuzzy c-Means
(b) Self-Organizing Maps (SOM)
(c) Hierarchical Clustering
(d) Ward's Method
(e) Cluster Validation (Biologically) Dr. Han-Ming Wu
17 2018/06/14 Clustering, classification
and data visualization II Classification:
(a) Performance Measures
(b) K-Nearest Neighbors (KNN)
(c) Classification Tree (Decision Tree)
(d) Linear Discriminant Analysis (LDA)
(e) Support Vector Machine (SVM) (plus Kernel Methods)
Data visualization:
(a) Statistical Graphics
(b) Visualizing High-dimensional Data
(b1) Dimension Reduction Techniques
(b2) Dimension-free Visualization
(c) Visualizing Categorical Data
(d) Visualization of Clustering Results
Lab: exercise in R Dr. Han-Ming Wu
18 2018/06/21 Final Exam