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Elements of Statistical Learning, video, pdf, Hastie, Tibshirani, 2014

Cloned from R-Blogger. 

In January 2014, Stanford University professors Trevor Hastie and Rob Tibshirani (authors of the legendary Elements of Statistical Learning textbook) taught an online course based on their newest textbook, An Introduction to Statistical Learning with Applications in R (ISLR). I found it to be an excellent course in statistical learning (also known as “machine learning”), largely due to the high quality of both the textbook and the video lectures. And as an R user, it was extremely helpful that they included R code to demonstrate most of the techniques described in the book.

If you are new to machine learning (and even if you are not an R user), I highly recommend reading ISLR from cover-to-cover to gain both a theoretical and practical understanding of many important methods for regression and classification. It is available as a free PDF download from the authors’ website.

If you decide to attempt the exercises at the end of each chapter, there is a GitHub repository of solutions provided by students you can use to check your work.

As a supplement to the textbook, you may also want to watch the excellent course lecture videos (linked below), in which Dr. Hastie and Dr. Tibshirani discuss much of the material. In case you want to browse the lecture content, I’ve also linked to the PDF slides used in the videos.

Chapter 1: Introduction (slides, playlist)

• Opening Remarks and Examples (18:18)
• Supervised and Unsupervised Learning (12:12)

Chapter 2: Statistical Learning (slides, playlist)

• Statistical Learning and Regression (11:41)
• Curse of Dimensionality and Parametric Models (11:40)
• Assessing Model Accuracy and Bias-Variance Trade-off (10:04)
• Classification Problems and K-Nearest Neighbors (15:37)
• Lab: Introduction to R (14:12)

Chapter 3: Linear Regression (slides, playlist)

• Simple Linear Regression and Confidence Intervals (13:01)
• Hypothesis Testing (8:24)
• Multiple Linear Regression and Interpreting Regression Coefficients (15:38)
• Model Selection and Qualitative Predictors (14:51)
• Interactions and Nonlinearity (14:16)
• Lab: Linear Regression (22:10)

Chapter 4: Classification (slides, playlist)

• Introduction to Classification (10:25)
• Logistic Regression and Maximum Likelihood (9:07)
• Multivariate Logistic Regression and Confounding (9:53)
• Case-Control Sampling and Multiclass Logistic Regression (7:28)
• Linear Discriminant Analysis and Bayes Theorem (7:12)
• Univariate Linear Discriminant Analysis (7:37)
• Multivariate Linear Discriminant Analysis and ROC Curves (17:42)
• Quadratic Discriminant Analysis and Naive Bayes (10:07)
• Lab: Logistic Regression (10:14)
• Lab: Linear Discriminant Analysis (8:22)
• Lab: K-Nearest Neighbors (5:01)

Chapter 5: Resampling Methods (slides, playlist)

• Estimating Prediction Error and Validation Set Approach (14:01)
• K-fold Cross-Validation (13:33)
• Cross-Validation: The Right and Wrong Ways (10:07)
• The Bootstrap (11:29)
• More on the Bootstrap (14:35)
• Lab: Cross-Validation (11:21)
• Lab: The Bootstrap (7:40)

Chapter 6: Linear Model Selection and Regularization (slides, playlist)

• Linear Model Selection and Best Subset Selection (13:44)
• Forward Stepwise Selection (12:26)
• Backward Stepwise Selection (5:26)
• Estimating Test Error Using Mallow’s Cp, AIC, BIC, Adjusted R-squared (14:06)
• Estimating Test Error Using Cross-Validation (8:43)
• Shrinkage Methods and Ridge Regression (12:37)
• The Lasso (15:21)
• Tuning Parameter Selection for Ridge Regression and Lasso (5:27)
• Dimension Reduction (4:45)
• Principal Components Regression and Partial Least Squares (15:48)
• Lab: Best Subset Selection (10:36)
• Lab: Forward Stepwise Selection and Model Selection Using Validation Set (10:32)
• Lab: Model Selection Using Cross-Validation (5:32)
• Lab: Ridge Regression and Lasso (16:34)

Chapter 7: Moving Beyond Linearity (slides, playlist)

• Polynomial Regression and Step Functions (14:59)
• Piecewise Polynomials and Splines (13:13)
• Smoothing Splines (10:10)
• Local Regression and Generalized Additive Models (10:45)
• Lab: Polynomials (21:11)
• Lab: Splines and Generalized Additive Models (12:15)

Chapter 8: Tree-Based Methods (slides, playlist)

• Decision Trees (14:37)
• Pruning a Decision Tree (11:45)
• Classification Trees and Comparison with Linear Models (11:00)
• Bootstrap Aggregation (Bagging) and Random Forests (13:45)
• Boosting and Variable Importance (12:03)
• Lab: Decision Trees (10:13)
• Lab: Random Forests and Boosting (15:35)

Chapter 9: Support Vector Machines (slides, playlist)

• Maximal Margin Classifier (11:35)
• Support Vector Classifier (8:04)
• Kernels and Support Vector Machines (15:04)
• Example and Comparison with Logistic Regression (14:47)
• Lab: Support Vector Machine for Classification (10:13)
• Lab: Nonlinear Support Vector Machine (7:54)

Chapter 10: Unsupervised Learning (slides, playlist)

• Unsupervised Learning and Principal Components Analysis (12:37)
• Exploring Principal Components Analysis and Proportion of Variance Explained (17:39)
• K-means Clustering (17:17)
• Hierarchical Clustering (14:45)
• Breast Cancer Example of Hierarchical Clustering (9:24)
• Lab: Principal Components Analysis (6:28)
• Lab: K-means Clustering (6:31)
• Lab: Hierarchical Clustering (6:33)

Interviews (playlist)

• Interview with John Chambers (10:20)
• Interview with Bradley Efron (12:08)
• Interview with Jerome Friedman (10:29)
• Interviews with statistics graduate students (7:44)

Algorithms and tools for protein–protein interaction networks clustering, with a special focus on population-based stochastic methods Clara Pizzuti1,† and Simona E. Rombo2,*,†

Algorithms and tools for protein–protein interaction networks clustering, with a special focus on population-based stochastic methods

Clara Pizzuti¹,^† and 

Simona E. Rombo, Bioinformatics 2014.

http://bioinformatics.oxfordjournals.org/content/30/10/1343.short


PR14 used 3 yeast PPI data to compare MCL with others. The MCL parameter was taken from Boheree2006. PR14 used protein complex as 'golden standard'. When overlapping score > 20%, MCL is the best algorithm. Bader's MCODE is also a good method for certain parameter settings.

useful references on teaching

Active learning increases student performance in science, engineering, and mathematics
Scott Freemana,1, Sarah L. Eddya, Miles McDonougha, Michelle K. Smithb, Nnadozie Okoroafora,Hannah Jordta,and Mary Pat Wenderoth, April 15, 2014
http://www.pnas.org/content/111/23/8410.full.pdf+html

Research-Based Learning Principles

http://www.josephjaywilliams.com/education

http://www.josephjaywilliams.com/education#TOC-Comparison:-Help-learners-grasp-or-construct-new-abstract-principles-by-comparison-of-specific-examples-of-the-generalization.

E coli flow cytometer, PI staining

Hawley & Hawley
PI stain

Wang, Li, Deng, Pan, BMC review on clustering methods for protein interaction networks.

Recent advances in clustering methods for protein interaction networks

Jianxin Wang1,2*, Min Li1*, Youping Deng3, Yi Pan2

From The ISIBM International Joint Conference on Bioinformatics, Systems Biology and Intelligent

Computing (IJCBS), Shanghai, China. 3-8 August 2009

http://www.biomedcentral.com/content/pdf/1471-2164-11-S3-S10.pdf

cited by
http://scholar.google.com/scholar?cites=16432683922097612422&as_sdt=5,43&sciodt=0,43&hl=en

Reviewed 20 clustering methods, including MCL. MCL is commented as the highly successful.

10. Brohée S, van Helden J: Evaluation of clustering algorithms for proteinprotein

interaction networks. BMC Bioinformatics 2006, 7:48.

63. Vlasblom J, Wodak SJ: Markov clustering versus affinity propagation for

the partitioning of protein interaction graphs. BMC Bioinformatics 2009,10:99.

References on teaching math biology /systems biology

 Learning Biology by Recreating and Extending Mathematical Models

Hillel J. Chiel,1,2,3 Jeffrey P. Gill,1 Jeffrey M. McManus,1 Kendrick M. Shaw1 

vision and change, 

Lin C, Cho Y-R, Hwang W-C, Pei P, and Zhang A. 2007. Clustering Methods in a Protein–Protein Interaction Network. In: Hu X, and Pan Y, eds. Knowledge Discovery in Bioinformatics: John Wiley & Sons, Inc., 319-355.

CLUSTERING METHODS IN PROTEIN-PROTEIN INTERACTION NETWORK
Chuan Lin, Young-rae Cho, Woo-chang Hwang, Pengjun Pei, Aidong Zhang
Department of Computer Science and Engineering
State University of New York at Buffalo

Cite as:
Lin C, Cho Y-R, Hwang W-C, Pei P, and Zhang A. 2007. Clustering Methods in a Protein–Protein Interaction Network. In: Hu X, and Pan Y, eds. Knowledge Discovery in Bioinformatics: John Wiley & Sons, Inc., 319-355.

This review article did not provide enough details on validation and comparison of different algorithms.

minimal version, R package

For my todo list, write a personalized R package

http://hilaryparker.com/2014/04/29/writing-an-r-package-from-scratch/

MCL algorithm comparison

Brohee and van Helden 2006, BMC [BH06]

BH06 compared Markov Clustering (MCL), Restricted Neighborhood Search Clustering (RNSC), Super Paramagnetic Clustering (SPC), and Molecular Complex Detection (MCODE), using annotated protein complex as bench mark. Random noises were introduced by randomly adding and deleting edges. MCL is the most reliable and robust method. 

Atlanta-qbio google group (AQBIO)

https://groups.google.com/forum/#!forum/atlanta-qbio

http://groups.google.com/d/groupsettings/atlanta-qbio/information

atlanta-qbio@googlegroups.com

http://groups.google.com/d/managemembers/atlanta-qbio/invite

conference call on comp and systems biology

Lessons:
I cannot see all the peoples. Sometimes it is hard to know who is talking.
When people not talking, mic should be muted to decrease background noises.

bio386, proofread of CNV manuscript

The student proofread CNV manuscript.

Final take home exam was given.

bio233, epidemiology,

I used an R-based simulation in class. The pace was fast and about 2/3 students are not following. However, a few students clearly paid attention.

Problems:
1) Plot did not show in Rstudio due to screen resolution. I used PDF to circumvent the problem.
2) I did not give student enough time to run the R code themselves, partially because I only require this for bonus points.

bio233, lab, analysis of flow cytomter data, Australian rabbit virus

Went over oral presentation order, final project report
Exam schedule

30 minutes, analysis of flow cytomter data,
http://youtu.be/BN5Ldu1AFgk

30 minutes, Australian rabbit virus
http://youtu.be/FqSDxGYu3K0

30 minutes, streak single colonies. I used my streak plate as an example of "A".

bio233, phylogeny

Let bio233 worked through power point slides.

I stumbled on the comparison between the canonical endosymbiosis and hydrogen hypothesis for mitochondria, after a student asked the question.

bio386, R coursera

I led students worked on R programming offered by Coursera.

bio233, virus, problem sets

I let students worked through 2 set of MCAT-styple problems. It was the first time that we finished two reading paragraphs in a 75-minute class.

Lotus Notes, initiate a new proposal for internal routing

http://spelmanosp.wordpress.com/2014/09/22/obtaining-approval-to-submit-a-grant-proposal/

bio233, streaking for single colonies

Six-streak procedure
1: short
2: twice
3: three times
4: 4 times.
5: many times
6: many times to evenly spread out the cells.

Usually the 4th streak should be thinned out.

Many students went back to the cell cultures for every streak, and this even happened after 2-3 trials.

bio233, flow cytometer lab on DHE-labelled yeast cells.

 The class spent 2 hours on DHE staining. I could save the time by giving assignment on the protocol itself.

When I let students to re-streak their plates, some groups stopped working on their DHE staining procedure. 

At 4pm, I started the Cellquest but no signal can be read from the Calibur. This is the 3rd time that this machine malfunctioned. Really bad timing. 

Flow cytometry, flow cytometer teaching resource

YouTube Learning material on flow cytometry
Hand-drawing introduction. 
Animated introduction
UW's tutorial on flow cytometry
Hong Qin's tutorial on BD FACS Calibur usage. Useful for understanding experimental procedure.


Reading materials on flow cytometr
Flow cytmetry from Wikipedia


DHE, superoxide indicator
http://www.lifetechnologies.com/order/catalog/product/D1168

NIH, Flow Cytometry Interest Group

http://sigs.nih.gov/FCIG/Pages/default.aspx

BIO125, spring 2015 strain and data request,

AGY 75, yeast strain with pSH44 reporter plasmid
AGY125, yeast strain with the wild type pMSH2 and pSH44 (This is the wildtype MSH2 control)
AGY124, yeast strain with pRS413 and pSH44 (This is the plasmid control)

Ecoli strain with plasmid
AG372   pmsh2-H658R
AG421  pmsh2-A618V


Read Gammie's recent papers.

Small NGS data of wildtype MSH2 and mutant msh2 for students to analyze using Galaxy

bio233, guest lecture, circulating tumor DNA

bio233 guest lecture

CAPP-seq

CT, biopsy are common method for tumor diagnosis.

How did ctDNA comes from tumor?

There arre much cell-free DNA in human circulation, typically 5ng/ml of plasma in healthy adults, primarily from hemopoetic cells.  Cell-free DNA often have half-live are 0.5 ~ 2 hours.

Hybrid selection (NimbeGen), target enrichment.

10,000X sequencing is required?

bio233 phylogeny and lab, practical exam on streaking single colonies

I spent 1 hour on introduction of phylogeny using my own slides.

Many students did not bring laptops.

For the lab, MEGA6 on Mac runs very slow.

For practical exam, some students did not see the previous streaking example clearly.

SVM, reading notes

 See http://hongqinlab.blogspot.com/2014/11/elements-of-statistical-learning-video.html


SVM kernel trick

trial and error to separate data in high dimenstional space

cross validation

predict True Negative?

Mathews correlation coefficient (MCC)  (for binary classification)

In general the equation for a hyperplane has the form

SVM maximize soft margin.

Data should be standardized for SVM analysis, because SVM treats every columns the same. 

On researchgate, someone argues: Perform different normalization such as Z-Score or Min-Max before using PCA. Z-Score normalization before using PCA might be beneficial.

For principal component (PCA) and svm, 
http://www.softcomputing.net/isda2010_2.pdf
On researchGate: Principal components are linear combinations of original variables x1, x2, etc. So when you do SVM on PCA decomposition you work with these combinations instead of original variables.


Support vector classifer in the enlarged spaced solves separation problem in the lower-dimensional space.

Question: Kernel is used to computer inner products of vectors. Why are there different types of kernels for computing the same thing (inner products)? 


SVM for more than 2 classes:

MATLAB ODE solver

ode15s

fmincon

http://laser.cheng.cam.ac.uk/wiki/images/e/e5/NumMeth_Handout_7.pdf

PDE

Numerical difference, approximate PDE

polytopes and phylogeny

polytopes is the convex hull

http://en.wikipedia.org/wiki/Polytope

tree -> matrix as markov process  OR polytope

Hamiltonian system

http://en.wikipedia.org/wiki/Hamiltonian_system

funding 2014

PD 14-7513 , due Feb 4, 2014
http://www.nsf.gov/funding/pgm_summ.jsp?pims_id=504976&org=EHR&from=home#.Uo-EwOFRdH8.facebook

NIH big data
http://grants.nih.gov/grants/guide/rfa-files/RFA-HG-14-009.html

http://bd2k.nih.gov/funding_opportunities.html#sthash.hFOQ3jpE.dpbs


NIH diversity RFP
http://grants.nih.gov/grants/guide/pa-files/PAR-12-016.html  expires in Jan 2015

Tajima's D, D_non, D_syn

Tajima's D = D_non + D_syn
additive

seems to be used by Autin Hughs

Cholera

http://cph.osu.edu/people/jtien

Cholera SIWR model

Seasonal variations will be used to further improve the model, modeled directly into infection force.

19th centry sample
http://muttermuseum.org/

Haiti, no recorded history of cholera infection. no immune responses.

Cholera spatial spread, waterways, human movement, cell phone movement (Digicel, Flowminder)

Moran's I to compare cell phone movement and cholera spread. Local movement versus waterways.

Community networks with environmental pathogen movement
 patch heterogeneity
 weight directed edges in networks

When can disease invade the network? R0 of the network,

Coupled locations

Next generation matrix (second generation matrix, Diekmann, Heesterbeek, Metz 1990, van den Driessche and Watmough 2002.

Transfer matrix V = Transfer out  - Transfers in + Decay
  Laplacian matrix(from graph theory) can be used model transfer out and in.

   i.e. V = L + D

D = diag{\delta_i}

Time scale have to be right

V^-1 as a perturbation problem

 Langenhop 1971, Laurent series for perturbed singular matrices

According to Tien, lifespan of cholera were fitted with expoential model in the lab. Later, Tien explained that fresh cholera have high infectious rate, so fitness of cholera bacteria has a characteristic of aging.