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Latest revision as of 10:06, 8 August 2017

Welcome to the U-M Big Data Summer Institute 2017 Wiki!

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Reading Material

Data Mining / Machine Learning Group

• Project Outline

EHR Group

• Project Outline

Papers

• Bush et al. (2016) Unravelling the Human Genome
• AAndreu-Perez et al. (2015) Big Data for Health
• Madigan et al. (2014) A Systematic Approach to Evaluating Evidence from Observational Studies
• Collins et al. (2015) A New Initiative on Precision Medicine

Genomics Group

• Project Outline

Papers

Methods for genome-wide association studies (GWAS)

• Skol AD et al. (2006) "Joint analysis is more efficient than replication-based analysis for two-stage genome-wide association studies" Nat. Genet
  - Useful to understand basic methods for GWAS and study design
• Willer CJ et al. (2010) "METAL: fast and efficient meta-analysis of genomewide association scans." Nat Genet
  - Software tool for meta-analysis

DNA sequencing and De-novo assembly

• The 1000 Genomes Project Consortium (2010) A map of human genome variation from population-scale sequencing Nature
  First 1000 genomes paper
• The 1000 Genomes Project Consortium (2015) A global reference for human genetic variation Nature
  Final release of the 1000 Genomes Project
• Iqbal Z. et al (2012) De novo assembly and genotyping of variants using colored de Bruijn graphs. Nature
  Variant caller using de-novo assembly graphs
• Li et al (2009) Fast and accurate short read alignment with Burrows-Wheeler transform.
  Sequence alignment algorithm using BWT

Single Cell RNA Sequencing

• Macosko E et al (2015) Highly Parallel Genome-wide Expression Profiling of Individual Cells Using Nanoliter Droplets. Cell'
  Landmark paper for DropSeq method
• Zheng G et al (2017) Massively parallel digital transcriptional profiling of single cells. Nat Comm
  Paper from 10x genomics
• van der Maaten LJP and Hinton GE (2008) Visualizing Data using t-SNE J Machine Learning Research
  First paper of t-SNE method

Prediction of Gene Expression and/or Complex Phenotypes

• Gamazon et al (2015) A gene-based association method for mapping traits using reference transcriptome data Nat Genet PrediXcan paper for elasticNet-based prediction of expression
• Lappalainen T et al (2013) Transcriptome and genome sequencing uncovers functional variation in humans. Nat Genet Paper describing GEUVADIS data
• Yang J et al (2011) GCTA: a tool for genome-wide complex trait analysis Am J Hum Genet GCTA paper that has BLUP method
• Zhou X et al (2013) Polygenic modeling with bayesian sparse linear mixed models BSLMM method as a more accurate alternatives to BLUP

Online videos to better understand genetics and genomics

Genetics

• Introduction to Genetics by 23andMe (5 videos)
• TED-Ed : How Mendel's pea plants helped us understand genetics - Hortensia Jiménez Díaz
• Genetic Recombination and Gene Mapping by Bozeman Science
• Useful Genetics : A college-level comprehensive genetics course with 292 lectures offered by Rosie Redfield at UBC

Useful 3D Animations

• From DNA to protein - 3D Animation
• DNA Transcription - 3D Animation
• DNA splicing - 3D Animation
• mRNA Translation - 3D Animation
• How DNA is packaged - 3D Animation
• The Central Dogma - 3D Animation

Gene Regulation and Epigenetics

• Epigenetics Lecture by SciShow
• Hi-C Technique : A 3D map of the Human Genome
• The ENCODE Project
• RNAi by Nature Video

Sequencing Technologies

• TED-Ed : The race to sequence the human genome - Tien Nguyen
• DropSeq - Droplet-based Single Cell Sequencing by McCarroll Lab

Imaging Group

• Project Outline

2017 Presentations

Week 1

Day 1: June 6

• Orientation 2017 (Slides) - Bhramar Mukherjee, PhD
• Coordinator Presentation (Slides) - Mitch Sevingy
• Life in Ann Arbor (Slides) - Mitch Sevigny
• On Being a Scientist (Slides & Audio) - Bhramar Mukherjee, PhD
• Ethics Review (Slides) - Bhramar Mukherjee, PhD
• Basic Probability (Audio) - Robert Klemmer

Day 2: June 7

• Data Processing (Slides & Audio) - Jed Carlson
• Study Design and Inference (Slides & Audio) - Rod Little, PhD
• Basic Unix (Slides) - Hyun Min Kang, PhD

Day 3: June 8

• R 101 (Slides & Audio) - Matthew Flickinger, PhD
• Observational Data and Bias (Slides & Audio) - Rod Little, PhD
• Linear Algebra (Audio) - Robert Klemmer

Day 4: June 9

• R 102 (Slides & Audio) - Matthew Flickinger, PhD
• Matrix Computation (Slides & Audio) - Shawn Lee, PhD
• Sebastian Zoellner Journey (Slides) - Sebastian Zoellner, PhD
• R 103 (Slides & Audio) - Matthew Flickinger, PhD

Week 2

Day 5: June 12

• Python 101 (Slides & Audio) - Jonathon Stroud
• Parameter Estimation and Likelihood (Slides & Audio) - Rod Little, PhD
• EHR Project Description (Slides) - Phil Boonstra, PhD; Matt Zawistowski, PhD; Zhenke Wu, PhD

Day 6: June 13

• Python 102 (Audio) - Jonathon Stroud
• Linear Regression (Slides & Audio) - Matt Zawistowski, PhD
• Genomics Project Description (Slides) - Hyun Min Kang, PhD

Day 7: June 14

• Machine Learning 1 (Slides & Audio) - Hui Jiang, PhD
• Logistic Regression (Slides & Audio) - Matt Zawistowski, PhD
• Alfred Hero's Journey Lecture (Slides & Audio) - Alfred Hero, PhD
• Imaging Project Description (Slides) - Tim Johnson, PhD
• Neuroimaging Data Analysis (Slides) - Eunjee Lee, PhD

Day 8: June 15

• Machine Learning 2 (Slides & Audio) - Hui Jiang, PhD
• Reproducible Research (Slides & Audio) - Jed Carlson
• Data Mining/ Machine Learning (Slides) - Johann Gagnon-Bartsch, PhD

Day 9: June 16

• Reading like a Scientific Writer (Slides & Audio) - Brett Griffiths, PhD
• Bhramar Mukherjee Journey Lecture (Slides & Audio) - Bhramar Mukherjee, PhD
• Jeremy Taylor Journey Lecture (Slides & Audio) - Jeremy Taylor, PhD

Week 3

Day 10: June 19

• Unsupervised Machine Learning 1 (Audio) - Jenna Wiens, PhD
• Casual Inference 1 (Audio) - Lu Wang, PhD

Day 11: June 20

• Unsupervised Machine Learning 2 (Audio) - Jenna Wiens, PhD
• Casual Inference 2 (Slides & Audio) - Lu Wang, PhD

Day 12: June 21

• Academic Presentations (Slides & Audio) - Sebastian Zoellner, PhD
• Programming Workshop (Slides & Audio) - Hyun Min Kang, PhD
• Goncalo Abecasis Journey (Slides & Audio) - Goncalo Abecasis, PhD

Day 13: June 22

• Network Models (Slides & Audio) - Zhenke Wu, PhD
• Distributed Computing (Slides & Audio) - Harsha Madhyastha, PhD

Day 14: June 23

• Preparing for Graduate School (Slides & Audio) - Kelley Kidwell, PhD
• Michael Boehnke Journey (Slides & Audio) - Michael Boehnke, PhD

Week 4

Day 15: June 26

• Visualization 1 (Audio) - Matthew Kay, PhD
• Visualization 2 (Audio) - Matthew Kay, PhD

Day 16: June 27

• Data Visualization in R (Slides & Audio) - Matthew Flickinger, PhD
• Introduction to Bayes (Slides & Audio) - Bhramar Mukherjee, PhD

Day 17: June 28

• Leveraging Skills and Deficits in Application Essays (Slides & Audio) - Brett Griffiths, PhD
• Programming Workshop (Audio) - Hyun Min Kang, PhD

Day 18: June 29

• Optimization (Slides & Audio) - Ambuj Tewari, PhD
• Bayes Computation (Slides & Audio) - Veronica Berrocal, PhD

Day 19: June 30

• Writing from Point A to Point D: Simple Strategies for Conveying Complex Ideas (Slides & Audio) - Brett Griffiths, PhD

Week 5

Day 20: July 3

• Bayes Computation 2 (Slides & Audio) - Jian Kang, PhD
• Data Mining 1 (Slides & Audio) - Kayvan Najarian, PhD

Day 22: July 5

• Large Scale Optimization (Slides & Audio) - Ambuj Tewari, PhD
• Data Mining 2 (Slides & Audio) - Kayvan Najarian, PhD

Day 23: July 6

• Python Workshop (Audio) - Arya Farahi
• Learning Health Systems (Audio) - Karandeep Singh, MD

Day 24: July 7

• CV/ Resume Workshop (Slides & Audio) - Tara Allendorfer
• Brisa Sanchez Journey (Slides & Audio) - Brisa Sanchez, PhD

Week 6

Day 25: July 10

• Case Study: Estimating AutoAntibody Signatures to Detect Autoimmune Disease Patient Subsets (Slides & Audio) - Zhenke Wu, PhD

Day 26: July 11

• Case Study: Mixture Models for Sequence Contamination and Single Cell Transcriptions (Slides & Audio) - Hyun Min Kang, PhD
• Case Studies: "Posterior Mean Screening for Scalar-on-Image Regression"; "Bayesian Computation for Log Gaussian Cox Processes with Application to Neuroimaging" (Slides & Audio) - Jian Kang, PhD; Tim Johnson, PhD

Symposium

Student Group Presentations

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

Student Poster Presentations

• A Time-to-Event Analysis of Heart Failure via Electronic Health Records
• Melanoma Detection by Classifying Skin Lesion Images
• Classifying Skin Lesions Images Using Adaptive Boosting
• Machine Learning Classification of Skin Lesion Images
• Genomics: Genome Storage and Assembly
• Predicting the Transcriptome from the Genome
• Classification of Cell Types from Peripheral Mononuclear Blood Cells
• EHR-Based Study of Long-Term Infectious Diseases
• Visualizing Lab and Phenotype Associations Using PheWAS and Electronic Health Records
• Data Mining: Microenvironment Microarray Spot Based Approach for Cell Prediction
• Estimating Cell Growth with Machine Learning and Data Mining

Additional Resources

• DataCamp Resources