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Welcome to the U-M Big Data 2016 Summer Program Wiki!

Consult the User's Guide for information on using the wiki software.

Reading Material

Data Mining Group

• Automatic Construction and Natural-Language Description
• Interestingness Measures for Data Mining a Survey
• Fast Algorithms for Mining Association Rules

EHR Group

• Statistical Inference, Learning and Models in Big Data
• USRDS 2015 Annual Data Report, Volume 1: Chronic Kidney Disease
• Dialysis Facility Characteristics and Services
• Data Dictionary for Quarterly Dialysis Facility Compare
• USRDS Introduction to Volume 1: CKD in the United States

Genomics Group

• A Global Reference for Human Genetic Variation
• A Map of Human Genome Variation from Population-Scale Sequencing

2016 Presentations

Day 1: June 13

• Orientation 2016 (slides) - Bhramar Mukherjee, PhD
• Welcome Reception (slides) - Bhramar Mukherjee, PhD
• Computing Resources (slides)
• Ethics Review (slides)
• On Being A Scientist (slides)
• Life in Ann Arbor (slides) - Evan Reynolds, Biostatistics PhD Student
• Intro to Probabilities and Distributions (file) - Rebecca Rothwell, Biostatistics PhD student

Day 2: June 14

• Computing Platforms (website) - Jacob Abernethy, PhD
• Introductory Statistics (slides & audio) - Bhramar Mukherjee, PhD

Day 3: June 15

• Intro to Unix (slides) - Hyun Min Kang, PhD

Day 4: June 16

• Data Structure using Python (website) - Jacob Abernethy, PhD
• Observational Data, Bias and Confounding (slides & audio) - Roderick Little, PhD

Day 5 June 17

• Data Structure using Python (website) - Jacob Abernethy, PhD
• From Pure Mathematics to Gene Discovery: A Biostatistician's Journey (slides & audio) - Michael Boehnke, PhD

Day 6 June 20

• Statistical Modeling using R (slides) - Phil Boonstra, PhD
• Study Design and Inference (slides & audio) - Roderick Little, PhD

Day 7 June 21

• Visualization using R (slides & audio) - Phil Boonstra, PhD
• Distributed Computing (slides) - Harsha Madhyastha, PhD

Day 8 June 22

• Fundamentals of Data Processing (slides) - Jed Carlson, Biostatistics PhD student

Day 9 June 23

• Matrix Computation (audio) - Jason Estes, Research Fellow in Biostatistics

Day 10 June 24

• Career Journey and A Principal Approach to Dimensionality Reduction Part 1 (slides) - Stephen Gliske, PhD
• Career Journey and A Principal Approach to Dimensionality Reduction Part 2 (slides) - Stephen Gliske, PhD
• Career Journey and A Principal Approach to Dimensionality Reduction Part 3 (slides) - Stephen Gliske, PhD

Day 11 June 27

• Large Scale Optimization Part 1 (slides & audio) - Tewari Ambuj, PhD
• link Causal Inference (audio) - Lu Wang, PhD
• Likelihood Functions and Parameter Estimation (slides) - Matthew Zawistowski, Research Specialist at the University of Michigan

Day 12 June 28

• Large Scale Optimization Part 2 (slides & audio) - Tewari Ambuj, PhD
• Sequential Decision Making (slides & audio) - Tewari Ambuj, PhD

Day 13 June 29

• R - dplyr (slides) - Matthew Flickinger, Senior Analyst at the University of Michigan
• R - Troubleshooting (slides) - Matthew Flickinger, Senior Analyst at the University of Michigan

Day 14 June 30

• Clustering: Graphical Models and Sampling Algorithm Part 1 (slides & audio) - Long Nguyen, PhD
• Clustering: Graphical Models and Sampling Algorithm Part 2 (slides & audio) - Long Nguyen, PhD

Day 15 July 1

• My SMART Journey (slides & audio) - Kelley Kidwell, PhD
• My Spatial Journey to UM Biostatistics (slides & audio) - Veronica Berrocal, PhD

Day 17 July 5

• Supervised Machine Learning 1 (slides & audio) - Hui Jiang, PhD
• Intro to Bayes (slides & audio) - Bhramar Mukherjee, PhD

Day 18 July 6

• R Data Visualization (slides) - Matthew Flickinger, Senior Analyst at the University of Michigan

Day 19 July 7

• Bayes Computation 1 (slides & audio) - Tim Johnson, PhD
• Unsupervised Machine Learning 1 (slides) - Jenna Wiens, PhD

Day 20 July 8

• Bayes Computation 2 (slides & audio) - Tim Johnson, PhD
• The Moments and the Journey (slides & audio) - Bhramar Mukherjee, PhD

Day 21 July 11

• Visualization 1 (slides) - Eytan Adar, PhD
• Supervised Machine Learning 2 (slides & audio) - Hui Jiang, PhD

Day 22 July 12

• Visualization 2 (slides) - Eytan Adar, PhD
• Personalized Medicine (slides & audio) - Lu Wang, PhD

Day 23 July 13

• Reproducible Research (slides) - Jed Carlson, Biostatistics PhD student

Day 24 July 14

• Social Netwrok Analysis (slides) - Eytan Adar, PhD
• Unsupervised Machine Learning 2 (slides) - Jenna Wiens, PhD

Day 25 July 15

• My Journey to Bigger than Average Data (slides & audio) - Joe Messana, MD

Day 26 July 18

• Supervised Machine Learning 3 (slides & audio) - Hui Jiang, PhD
• Population Genetics (slides & audio) - Sebastian Zoellner, PhD
• Adventures in Human Genetics (slides & audio) - Goncalo Abecasis, PhD, Department of Biostatistics Chair at the University of Michigan

Day 27 July 19

• Data Privacy and Security (slides) - Jacob Abernethy, PhD
• Learning Health Systems (slides & audio) - Karandeep Singh, PhD

Day 28 July 20

• Journey Lecture (slides & audio) - Phil Boonstra, PhD

Symposium

• Symposium Welcome - Bhramar Mukherjee, PhD
• Identifying and Correcting for Contamination in DNA Sequencing Studies - Michael Boehnke, PhD
• Assessing Time-Varying Causal Effect Moderation using Intensively Collected Longitudinal Intervention Data - Susan Murphy, PhD
• Statistical Methods for Personalized Medicine - Jeremy Taylor, PhD
• Software For (and With) Big Data - Eytan Adar, PhD
• Using Doctors' Notes to Uncover Everyday Natural Experiments in Healthcare - Karandeep Singh, MD
• Data Science for the Flint Water Crisis - Jacob Abernethy, PhD and Eric Schwartz. PhD
• Doing Data Science - Rachel Schutt, Chief Data Scientist at News Corp

Student Group Presentations

• Data Mining
• Electronic Health Records (EHR)
• Genomics
• Machine Learning

Student Poster Presentations

• Using Data Mining Techniques and Classification Algorithms to Predict Impact of Academic Papers
• Clustering Gene Expression Profiles of Single Cells Using Expectation Maximization
• Data Mining: Association Rules Using the Apriori Algorithm
• Exploring U.S. Population Stratification with Genes for Good Data
• Network Structure in Offshore Leaks Data
• A Genome-Wide Association Study for Mental Illness
• Evaluation of the End Stage Renal Disease Quality Incentive Program
• Genome-Wide Association Study of Alcohol Consumption and Stress Levels
• Perservation of Semantic Parallels Through Word2Vec
• Single Cell Diffenence in Gene Expression between Macular and Peripheral Human Retinal Cells
• Twitter Sentiment Analysis & The Stock Market
• Data Curation & Wrangling
• Predicting CKD and Potential Risk Factors with Multiple Linear Regression
• The ICIJ Panama Leak: Temporal & Spatial Visualizations of the World's Hidden Wealth
• A Streamlined Approach for Comparing Two Genomic Catalogs
• From Interviews to Blood Tests: Using Statistical and Machine Learning Models to Predict Kidney Function
• Flagging Facilities: An Empirical Null Method

Getting started

• Configuration settings list
• MediaWiki FAQ
• MediaWiki release mailing list
• Localise MediaWiki for your language

Welcome!

Welcome to our wiki!

If you would like to contribute, log-in or request an account. We recommend using your e-mail address or Michigan uniqname as your user id.

For basic instructions, see the Wikipedia Tutorial. Sequence Analysis Tools

We are developing software tools for the analysis of next generation sequence data.

These tools include:

Variant Calling with GlfSingle and GlfMultiples Variant Calling and De Novo Mutation Detection in Families with Polymutt Variant Annotations using VcfCodingSnps Rare Variant Analysis using RvTests Rare Variant Association Analysis in family samples FamRvTest Quality control using FastQValidator, VerifyBamID, and BamValidator C++ APIs for sequence analsysis using C++ Library: libStatGen Meta-analysis of single variant or gene-level associations RAREMETAL-SOFTWARE Sequencing study design helper Rarefy Local ancestry inference (ancestry painting) using off-targeted sequence data SEQMIX Association Container Toolbox EPACTS Fast Genotype Imputation Tool : Minimac3

These tools and additional tools can be found on the Software page.

We are developing Genome/Sequencing Processing Pipelines for anyone to use: GotCloud High Level Tutorials

Some high-level tutorials on the analysis of next generation sequence data:

Evaluating a Read Mapper on Simulated Data SNP Call Set Properties Generic Exome Analysis Plan

Projects

NHLBI Genome Sequencing Project

SardiNIA - The SardiNIA longitudinal study of aging.

Exome Meta-analysis of Drinking and Smoking (EMADS)

The 1000 Genomes Project Learn Genetics

Faculty in the group teach in a variety of formal and informal settings. Class notes and relevant discussion are archived here. General Resources