MCB/ABE/BIOC/ECOL/GENE 516A/416A

Statistical Bioinformatics and Genomic Analysis

Spring 2014

Time & location: MW 11:00 am - 12:15 pm, Shantz 338

Instructor: Lingling An

Office: Shantz 501

Phone: (520) 621-1248

Email: anling@email.arizona.edu

Office hours: TBA

Useful websites: R CRAN and reference card, Bioconductor.

Online p-value calculator: http://graphpad.com/quickcalcs/PValue1.cfm

Announcement:

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Homework 4: due on 4/30/14 (wed).

Project 1: due on 4/14/14 (Mon)

Project 2: instructions

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Lectures and Homework assignments

(password is required for the files)

Syllabus: here

Lecture 1: Introduction (syllabus, survey)

Lecture 2: Review (I): statistics (population & sample, parameter & statistic, normal and t- distributions,

central limit theorem, confidence intervals, two-sample comparison)

Lecture 3: Review (II): statistics (hypothesis test, one-sided, two-sided tests, p-value and interpretation, sample size, relationship between confidence interval and hypothesis test)

Lecture 4: Review (III): statistics (ANOVA analysis, mean square error, F statistic, Least Significant

Difference test, multiple comparisons and Bonferroni correction)

Lecture 5: Review: Intro to Molecular Biology

Lecture 6: Introduction to R (example data is here)

Lecture 7: Introduction to R graphics ; Lab1 ;

Lecture 8: Introduction to Microarray technology

(homework 1, and the reading material)

Lecture 9: Microarray Experiment

Lecture 10: Data Preprocessing for cDNA array

Lecture 11: Data Preprocessing for Affymetrix array

homework 2 is here , the dataset folder for the homework is here and the paper is here

Lab 2 ;

Lecture 12: Differential expression analysis

Lecture 13: Differential expression analysis (limma for affy data) (dataset for the lecture)

Lecture 14: Differential expression analysis (limma for cDNA array data)

Lecture 15: Classification analysis

Lecture 16: Classification analysis ( R codes)

(homework 3, and the data )

Lecture 17: next generation sequencing and practical skills for NSG data analysis

Lecture 18: Cluster analysis

Lecture 19: Cluster gene expression

Lecture 20: Cluster analysis – R code

Lecture 21: Gene Ontology analysis

Lecture 22: Gene Ontology analysis (R code)

Lecture 23: Bio-ontologies

Lecture 24: Pathway analysis

(homework 4 )

Lecture 25: adding and using GO (datasets)

Lecture 26: Intro to proteomics (additional)

Lecture 27: Intro to metagenomics

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