BIOST 2055
Introductory high-throughput genomic data analysis I: data mining and
applications.
Spring/2012
Class location: Room A622 Crabtree Hall
Computer lab location: Room 3073 (3rd floor), Department of Computational Biology, BST3,
3501 Fifth Avenue
Class schedule: Wednesday, Friday 9:30-10:45AM
Course homepage: use Blackboard
Lecturer: George C. Tseng and Yan Lin
Office hour: by appointment
Office: 303 Parran Hall
Email address: ctseng@pitt.edu
Telephone number: 412-624-5318
Lecturer’s homepage: http://www.pitt.edu/~ctseng
TA: Shaowu Tang
Office hour: Monday 1:30-3:00pm and Friday: 12:30pm-2:00pm.
Office: Parran Hall 325A (or Parran 309)
Email address: biost2055pitt@gmail.com
Course Description:
This course is a graduate level aimed at introducing modern statistical methods and applications
for high-throughput genomic data. The first half of the course contains sessions to introduce
fundamental statistical and computational methods. The second half covers topics on various highthroughput genomic applications and related data analysis issues. This course is designed for graduate
students or researchers from both quantitative fields (statistics and computer science) and qualitative
biological fields. All students require basic statistical training (i.e. two elementary statistics courses,
basic calculus and linear algebra) and programming proficiency (R programming is required for
homework and final project). The visions of the course include: (1) to motivate students from
quantitative fields into genomic research (2) to familiarize students from biological fields with related
statistical methods (3) to promote inter-disciplinary collaboration habits in class.
Tentative Schedule of Sessions and Assignments:
The first 18 sessions (75 minutes each session) are designed to introduce fundamental statistical methods
used in genomic data analysis. Then another 10 sessions are devoted to selected special topics of highthroughput genomic data, some of which will be taught by invited guest speakers. The last two sessions
are for student presentations on their final projects.
Part I: Fundamental statistical methods
1/4
Introduction of the entire course and basic molecular biology and genetics.
(George & Yan)
1/6
Introduction More on molecular biology and biological database. (Yan)
1/11
Introduction microarray and next-generation sequencing (NGS) technology.
(Yan)
1/13
Data preprocessing
1/18
1/20
1/25
1/27
2/1
2/3
2/8
2/10
2/15
2/17
2/22
2/24
2/29
3/2
Data summarization, data transformation, data filtering and missing value
imputation. (Yan)
Detecting differentially expressed (DE) genes Empirical Bayes. Comparative
analysis of two or more conditions; permutation methods; SAM; control false
discovery rate (FDR). (Yan)
(Lab1) Introduction Bioconductor and NCBI database.
Up-stream analysis analysis on real Affymetrix and cDNA array data sets.
Homework 1 distributed. (Yan)
Supervised learning (classification) basic concepts in machine learning; feature
selection, overfitting and cross-validation. sensitivity and specificity. (George)
Supervised learning (classification) Bayes classifier; popular machine learning
methods: Logistic regression, LDA/QDA/Fisher’s criterion, KNN, CART,
bagging, boosting, random forest, SVM, ANN, nearest shrunken centroid.
(George)
Supervised learning (classification) cont’d (George)
(Lab2) DE analysis and classification Data analysis on detecting DE genes and
classification problem.
Homework 2 distributed. (George)
Dimension reduction data visualization; principal component analysis (PCA);
multidimensional scaling (MDS). (Yan)
Unsupervised learning (clustering) hierarchical clustering, K-means, selforganizing maps (SOM), model-based clustering; estimate number of clusters.
(Yan)
Unsupervised learning (clustering) tight clustering; penalized and weighted Kmeans; cluster stability and tightness; bi-clustering. (Yan)
(Lab3) Dimension reduction and Clustering analysis
Homework 3 distributed. (Yan)
Pathway analysis microarray and gene annotation databases (GO, KEGG and
more); enrichment analysis; motif finding. (George)
Genetic regulatory network Genomic regulatory network inference using
microarray data (Boolean network and Baysian network). (George)
Horizontal genomic meta-analysis microarray meta-analysis (random effects
model, Fisher’s method, maxP, rank-based methods etc). (George)
(Lab4) Genomic meta-analysis, gene annotation and pathway analysis
Homework 4 distributed. (George)
Spring break
3/7 &
3/9
Part II: Selected topics
3/14
Copy number variation (CNV) and loss of heterozygosity (LOH) array CGH,
SNP array (Eleanor)
3/16
Next generation sequencing I (Dr. Wei Chen)
3/21
Genome-wide association (GWAS) (Eleanor Feingold)
3/23
Next generation sequencing II (Dr. Wei Chen)
3/28
Copy number variation (CNV) and loss of heterozygosity (LOH) hidden Markov
model (HMM), change-point model (George)
3/30
Gene regulation and miRNA regulation (Dr. Takis Benos)
4/4
Gene regulation and miRNA regulation (Dr. Takis Benos)
4/6
DNA-protein interaction (ChIP-chip, ChIP-seq and ENCODE) (Dr. Xinghua
Lu)
4/11
Epigenomics methylation, histone modification, methylation array, bisulfite
sequencing (Dr. Xinghua Lu)
4/13
4/18
4/20
Vertical integrative analysis integration of multi-dimensional genomic
experimental data (transcriptome, microRNA, methylation, ChIP, SNP,
phenome) (George)
Proteomics Introduction to 2D-gel, mass spectrometry, protein array and
proteomics. (George)
Student final project presentation
Student final project presentation
Handout:
Course information and handouts will be posted to the Blackboard. You are encouraged to print out the
slides before each lecture.
Computer Lab:
There will be four lab sessions for hand-on experiences on programming and software usage during the
first half of the course. R is the major language used and ability of programming in R is a prerequisite.
Four homework sets are distributed after each computer lab.
Final project:
Final projects are conducted by groups of 3 students. We will encourage/enforce mixture of quantitative
(statisticians) and qualitative (biologists) students in the final projects. The lecturer will provide a list of
topics/references at the beginning of the semester and the major goal is to apply statistical techniques
learned in class to analyze the real data sets and solve the problems. A presentation and a final report are
expected from each group in the end of the semester.
Grade:
Homework 1~4: 52%
Final project: 48% (mid-term progress report due 3/14 for 8%; final presentation for 20%; final paper due
on 4/22 for 20%)