Data Analysis for Genomics

http://genomicsclass.github.io/book/

HarvardX: PH525x Data Analysis for Genomics

Data Analysis for Genomics

The repository of the R markdown files (.Rmd) for the labs shown here is:

http://github.com/genomicsclass/labs

Resources

• Online resources and books
• Finding more help for data analysis

Introduction (week 1)

• Introduction
• Exploratory Data Analysis
• Installing Bioconductor and finding help
• R refresher
• Robust summaries

Microarray and NGS basics (week 2)

• Installing packages from Github
• Reading microarray data
• Downloading data from GEO using GEOquery
• EDA plots for microarray
• Basic Bioconductor infrastructure
• EDA plots for next generation sequencing

Statistical inference and linear modeling (week 3)

• Inference
• Expressing design formula in R
• Linear models
• Basic inference for microarray
• Rank tests
• Monte Carlo methods

Background, modeling and normalization (week 4)

• Background
• Modeling
• Normalization

Distance and prediction (week 5)

• Distance lecture
• Distance and clustering lab
• Dimension reduction and heatmaps
• Prediction lecture
• Cross-validation

Batch effect (week 6)

• Confounding
• SVA, ComBat, PCA and SVD
• Running PCA and SVD in R

Advanced differential expression (week 7)

• Hierarchical modeling and using limma
• Mapping features to genes
• Gene set analysis lecture
• Gene set testing in R
• Multiple testing

Advanced workflows (week 8)

• Visualizing NGS data
• Counting NGS reads in features
• Methylation
• Reading 450K idat files with the minfi package
• Interactive visualization of DNA methylation data analysis
• ChIP-seq
• RNA-seq
• Genome variation

Footnotes for all lectures

• Footnotes

IEEE SPS / UBC ICICS Summer School on Signal Processing and Machine Learning for Big Data

PROGRAM

In natural language processing, latent Dirichlet allocation (LDA) is a generative model that allows sets of observations to be explained by unobserved groups that explain why some parts of the data are similar. For example, if observations are words collected into documents, it posits that each document is a mixture of a small number of topics and that each word's creation is attributable to one of the document's topics. LDA is an example of a topic model and was first presented as a graphical model for topic discovery by David Blei, Andrew Ng, and Michael Jordan in 2003.[1]

https://sites.google.com/site/s3pbigdata2014/program

IEEE SPS / UBC ICICS Summer School on Signal Processing and Machine Learning for Big Data

   

	Tuesday July 29	Wednesday July 30	Thursday July 31	Friday August 1
9:00-9:30	Opening ceremony
9:30-10:30	Konstantinos N. Plataniotis University of Toronto	Angshul Majumdar IIIT-Delhi	Ali Bashashti BC Cancer Agency	Ozgur Yilmaz University of British Columbia
10:30-11:00	Coffee break	Coffee break	Coffee break	Coffee break
11:00-12:00	Vikram Krishnamurthy University of British Columbia	Angshul Majumdar IIIT-Delhi	Tom Levi Plenty Of Fish	Michael Friedlander University of British Columbia
12:00-13:00	Lunch	Lunch	Social event with lunch	Rayan Saab University of California, San Diego
13:00-14:00	Georgios Giannakis University of Minnesota	Martin McKeown University of British Columbia
14:00-14:30	Coffee break	Coffee break
14:30-15:30	Georgios Giannakis University of Minnesota	Li Deng Microsoft Research

Bioinformatics Centre for Computational Medicine (CCM), SickKids, Toronto

Michael Brudno

http://ccm.sickkids.ca/?q=node/32

JQuery UI

http://jqueryui.com/demos/

Java generics

http://docs.oracle.com/javase/tutorial/java/generics/types.html

The most commonly used type parameter names are:

• E - Element (used extensively by the Java Collections Framework)
• K - Key
• N - Number
• T - Type
• V - Value
• S,U,V etc. - 2nd, 3rd, 4th types

public interface Pair<K, V> {
    public K getKey();
    public V getValue();
}

public class OrderedPair<K, V> implements Pair<K, V> {

    private K key;
    private V value;

    public OrderedPair(K key, V value) {
 this.key = key;
 this.value = value;
    }

    public K getKey() { return key; }
    public V getValue() { return value; }
} 

public class Box<T> {

    private T t;          

    public void set(T t) {
        this.t = t;
    }

    public T get() {
        return t;
    }

    public <U extends Number> void inspect(U u){
        System.out.println("T: " + t.getClass().getName());
        System.out.println("U: " + u.getClass().getName());
    }

    public static void main(String[] args) {
        Box<Integer> integerBox = new Box<Integer>();
        integerBox.set(new Integer(10));
        integerBox.inspect("some text"); // error: this is still String!
    }
}

A type variable with multiple bounds is a subtype of all the types listed in the bound. If one of the bounds is a class, it must be specified first. For example:

Class A { /* ... */ }
interface B { /* ... */ }
interface C { /* ... */ }

class D <T extends A & B & C> { /* ... */ }