The only secret of style

Matthew Arnold (1822 -1888):

“Have something to say, and say it as clearly as you can. That is the only secret of style.”

E.g. Bill Clinton full DNC Speech 2012
http://www.youtube.com/watch?v=uzDhk3BHi6Q

2007 Microsoft Office Add-in: Microsoft Save as PDF or XPS

http://r.office.microsoft.com/r/rlidMSAddinPDFXPS

http://office.microsoft.com/en-us/help/print-share-and-protect-files-in-the-pdf-and-xps-file-formats-HA010167527.aspx?CTT=5&origin=HA010067494

Install this plugin so that you can export / save PowerPoint slides to PDF.

yED - Graph Editor

http://www.yworks.com/en/products_yed_about.html

Workflow visualization tool.

Coursera

https://www.coursera.org/ubc

Free online course

Useful Genetics from Rosy Redfield

Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources

http://www.nature.com/nprot/journal/v4/n1/full/nprot.2008.211.html

Da Wei Huang^1,2, Brad T Sherman^1,2 & Richard A Lempicki¹

Abstract

DAVID bioinformatics resources consists of an integrated biological knowledgebase and analytic tools aimed at systematically extracting biological meaning from large gene/protein lists. This protocol explains how to use DAVID, a high-throughput and integrated data-mining environment, to analyze gene lists derived from high-throughput genomic experiments. The procedure first requires uploading a gene list containing any number of common gene identifiers followed by analysis using one or more text and pathway-mining tools such as gene functional classification, functional annotation chart or clustering and functional annotation table. By following this protocol, investigators are able to gain an in-depth understanding of the biological themes in lists of genes that are enriched in genome-scale studies.

Science and the Semantic Web

http://www.sciencemag.org/content/299/5606/520.summary

James Hendler*[HN19]

As modern science grows in complexity and scope, the need for more collaboration between scientists at different institutions, in different areas, and across scientific disciplines becomes increasingly important. An emerging generation of World Wide Web technology, known as the Semantic Web, offers tremendous potential for collaborative and interdisciplinary science. However, to realize this potential, scientists and information technologists must forge new models of cooperation, and new thinking must go into the funding and dissemination of this next generation of scientific tools on the Web.      

Science 24 January 2003:
Vol. 299 no. 5606 pp. 520-521 DOI: 10.1126/science.1078874

Force fsck on boot

Tried to run fsck but your drive is already mounted? Force run it on the next boot by typing

$ sudo touch /forcefsck

Edit the drives that you want checked by putting a 1 on the last column

$ sudo gedit /etc/fstab

First day of Teaching

http://www2.honolulu.hawaii.edu/facdev/guidebk/teachtip/dayone.htm

Don't just present the slides, also read the expression of students and see if they follow you.

http://gradschool.about.com/od/collegeteaching/p/How-To-Lecture-Teaching-Tips-For-Graduate-Students.htm

Place yourself in their shoes

http://gradschool.about.com/od/becomingaprofessional/g/impostor.htm

Definition: The impostor syndrome is feeling that one hasn't earned his or her achievements - that the achievements are the result of luck. It is very common among high achieving persons, particularly women, who worry that some day they will be "found out" - that others will discover that they really do not know what they are doing. A professor who experiences the impostor syndrome may feel that he or she is faking it and worry that others know that they are really a phony and that their achievements are simply a matter of luck, not ability.

Remind yourself that you know your stuff. Visualize yourself succeeding and be your own cheerleader.  

Miniature science: How microfluidics is powering biology

http://www.youtube.com/watch?v=66Oc8fLKXlE

... so basically, use laser tweezers to isolate an unculturable bacteria (eg. SFB segmented filamentous bacteria, found in the lining of intestines) so that you can amplify them and sequence
.... in a microfluidic environment, you can minimize the contamination that comes with amplification reagents due to smaller volume in chambers and it's a closed environment so contamination from air can't come in

Table of Contents
00:00 - Introduction
01:33 - Talk begins
03:26 - What is microfluidics?
09:24 - Microelectronics: a recent micro-tech revolution
13:12 - The complexity of biological systems
17:50 - How to unravel biological mysteries
21:24 - Is there a Moore's law for experimental biology?
23:55 - Methods and possibilities of modern microfluidics
30:23 - Studying single cells with microfluidics
34:33 - Moving cells with laser tweezers
35:18 - Preventing contamination using microfluidics
40:38 - Intestinal bacteria and their DNA
44:55 - A new revolution in life science is beginning...
46:30 - Acknowledgements
47:10 - Q&A

Miniaturized lab-on-a-chip methods are being deployed as labor-saving devices in biological research, through the advent of a suite of microfluidics technologies. Microfluidics enables large-scale studies that provide the means to better understand, prevent, and treat human disease. In his 2012 Midsummer Nights' Science lecture, Paul Blainey discusses the promise of using microfluidics to transform our industrial infrastructure to operate more efficiently, while protecting the natural environment.

Find out more about Paul Blainey's work at the Broad Institute and about our public lecture series and other educational opportunities:

-Blainey lab page (http://www.broadinstitute.org/node/4165/)
-Broad news story (http://www.broadinstitute.org/news/3068)
-Education (http://www.broadinstitute.org/node/635/)
-Broad Institute Midsummer Nights' Science series (http://www.broadinstitute.org/midsummer)

Category:

Science & Technology

License:

Standard YouTube License

Vancouver Prostate Centre Next-Gen Sequencing

slebihan@prostatecentre.com

Metagenomic research

Energy production, bioremediation

microbe, dentistry

marine biology

soil

for:
microbial and metabolic diversity
microbial abundance
gene content
metatranscriptomics
metabolic signature profiling
viral pathogen detection

16S/18S rRNA Amplicon Sequencing and Shotgun Sequencing

Free online brainstorming diagraming tool

https://bubbl.us/

brainstorming and workflow made simple

R Bootstrapping, stratification

http://www.statmethods.net/advstats/bootstrapping.html

Bootstrapping a Single Statistic (k=1)

The following example generates the bootstrapped 95% confidence interval for R-squared in the linear regression of miles per gallon (mpg) on car weight (wt) and displacement (disp). The data source is mtcars. The bootstrapped confidence interval is based on 1000 replications.
# Bootstrap 95% CI for R-Squared
library(boot)
# function to obtain R-Squared from the data
rsq <- function(formula, data, indices) {
  d <- data[indices,] # allows boot to select sample
  fit <- lm(formula, data=d)
  return(summary(fit)$r.square)
}
# bootstrapping with 1000 replications
results <- boot(data=mtcars, statistic=rsq,
   R=1000, formula=mpg~wt+disp)

# view results
results
plot(results)

# get 95% confidence interval
boot.ci(results, type="bca")




http://rss.acs.unt.edu/Rdoc/library/sampling/html/strata.html

library('sampling')



############
## Example 3
############
# Uses the 'swissmunicipalities' data for drawing a sample of units
data(swissmunicipalities)
# the variable 'REG' has 7 categories in the population; it is used as stratification variable
# Computes the population stratum sizes
table(swissmunicipalities$REG)
# do not run
#  1   2   3   4   5   6   7 
# 589 913 321 171 471 186 245 
# the sample stratum sizes are given by size=c(30,20,45,15,20,11,44)
# the method is simple random sampling without replacement (equal probability, without replacement)
st=strata(swissmunicipalities,stratanames=c("REG"),size=c(30,20,45,15,20,11,44), method="srswor")
# extracts the observed data
# the order of the columns is different from the order in the swsissmunicipalities database
table(getdata(swissmunicipalities, st)$REG)
# do not run
# 1  2  3  4  5  6  7 
# 20 15 45 30 20 11 44

z<-getdata(swissmunicipalities, st)
dim(z)
#[1] 185  25
dim(swissmunicipalities)
#[1] 2896   22




Differential gene expression

Differential Gene Expression

http://estbioinfo.stat.ub.es/docs/jlmosquera/teaching/UVic/tia/Lecture3-DifferentialGeneExpression.pdf

ENCODE Animation

http://www.nature.com/nature/videoarchive/encode_animation/index.html

ENCODE: The story of you

Ever since a monk called Mendel started breeding pea plants we've been learning about our genomes. In 1953, Watson, Crick and Franklin described the structure of the molecule that makes up our genomes: the DNA double helix. Then, in 2000, scientists wrote down the entire 3-billion letter code contained in the average human genome. Now they're trying to interpret that code; to work out how it's used to make different types of cells and different people. The ENCODE project, as it's called, is the latest chapter in the story of you. This animation, narrated by Tim Minchin, shows how ENCODE is the culmination of two centuries of learning.

To read the research papers and more, visit nature.com/ENCODE

13 September 2011

Changing privacy landscape of big data

http://www.nature.com/msb/journal/v8/n1/full/msb201247.html?WT.ec_id=MSB-v8/n1

Thirty years ago, it was relatively easy to protect one's privacy and remain anonymous. Few computerized systems existed to store our personal information, the internet was so primitive that most were not even aware it existed, and only a few thousand individuals were privileged enough to own a handheld cellular phone. Fast forward to our current day and life—everything has changed. Rapid electronic transactions among individuals and between individuals and entire communities occur on an unprecedented scale, our life stream is continuously digitized and archived—GPS positioning information, cell phone calls, text messages, credit card purchases, e-mails, online social network chatter and even our electronic medical records (Figure 1). In fact, today the marketing department of your neighborhood Target can know before you that your own daughter is pregnant, given changes in purchase patterns (Duhigg, 2012). Long gone are the days of anonymity and privacy.

Metagenomics and shotgun sequencing

[VanBUG] Thursday, Sept 13, 6pm - Katherine Pollard, Quantifying taxonomic and functional diversity of metagenomes from next-generation sequencing data

http://www.ncbi.nlm.nih.gov/pubmed/19383763
 Microbial community profiling for human microbiome projects: Tools, techniques, and challenges.

High-throughput sequencing studies and new software tools are revolutionizing microbial community analyses, yet the variety of experimental and computational methods can be daunting. In this review, we discuss some of the different approaches to community profiling, highlighting strengths and weaknesses of various experimental approaches, sequencing methodologies, and analytical methods. We also address one key question emerging from various Human Microbiome Projects: Is there a substantial core of abundant organisms or lineages that we all share? It appears that in some human body habitats, such as the hand and the gut, the diversity among individuals is so great that we can rule out the possibility that any species is at high abundance in all individuals: It is possible that the focus should instead be on higher-level taxa or on functional genes instead.

Environmental Shotgun Sequencing
http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.0050082

In this essay I focus on one particularly promising area of metagenomics—the use of shotgun genome methods to sequence random fragments of DNA from microbes in an environmental sample. The randomness and breadth of this environmental shotgun sequencing (ESS)—first used only a few years ago [10,11] and now being used to assay every microbial system imaginable from the human gut [12] to waste water sludge [13]—has the potential to reveal novel and fundamental insights into the hidden world of microbes and their impact on our world. However, the complexity of analysis required to realize this potential poses unique interdisciplinary challenges, challenges that make the approach both fascinating and frustrating in equal measure.

t-test, anova

//t-test apply(matrix, 1, function(x){t.test(x ~ factor(t(facts)))$p.value}) apply(matrix, 1, function(x){t.test(x ~ factor(t(facts)))$statistic}) //one way anova apply(matrix,1,function(x){anova(aov(x~factor))$Pr}) apply(matrix,1,function(x){anova(aov(x~factor))$F}) //two way anova without interactions apply(matrix,1,function(x){anova(aov(x~farea+ftreat))$Pr}) apply(matrix,1,function(x){anova(aov(x~farea+ftreat))$F}) //two way anova with interactions apply(matrix,1,function(x){anova(aov(x~farea+ftreat+farea*ftreat))$Pr}) apply(matrix,1,function(x){anova(aov(x~farea+ftreat+farea*ftreat))$F}) http://en.wikipedia.org/wiki/F-test#One-way_ANOVA_example

Basic epidemiology

whqlibdoc.who.int/publications/2006/9241547073_eng.pdf

http://www.fao.org/Wairdocs/ILRI/x5436E/x5436e04.htm

http://www.mha.spph.ubc.ca/program_course.html

http://www.healthinformaticsforum.com/forum/topics/health-informatics-vs

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC79037/

The 7 Biggest Beginner Tennis Mistakes

Tennis timing and accuracy
http://www.youtube.com/watch?v=KDCrKgzkCY8
- don't hit the ball too late, hit it while it's a bit front of you just below the waist
- don't rotate your head

Tennis Training: How To Hit Your Forehand With More Topspin!
http://www.youtube.com/watch?v=wa-3GStYh_I&feature=relmfu

Grip
http://www.youtube.com/watch?v=aXcsblS3Jl4&feature=related

http://www.youtube.com/watch?v=uURMnGqjZEs&feature=relmfu

http://www.articledashboard.com/Article/The-7-Biggest-Beginner-Tennis-Mistakes/469178

http://www.videojug.com/film/tennis-high-deep-spin

http://www.videojug.com/film/how-to-master-the-basic-forehand

http://www.videojug.com/film/how-to-master-the-basic-one-handed-backhand

http://www.videojug.com/film/practice-drills-for-stunning-groundstrokes

Hitting
- right position, shoulders perpendicular to the net
- use all the muscle to do a full swing (don't stop the swing because it will contract the muscles - not good)
- return to original position

http://tt.tennis-warehouse.com/showthread.php?t=119615

Too close from the Tennis Ball?
- take small short foot steps
- imagine just slightly missing the ball (so you don't move in too close, it's easier to move forward than back)
- stay a close on the ball
- practice

Strategies
http://www.youtube.com/watch?v=TKLZ9aOj2Ew&playnext=1&list=PL11888A296102F79F&feature=results_main


Roger Federer
http://www.youtube.com/watch?NR=1&feature=endscreen&v=1ImeQaAyFPc

ENCODE

http://o.canada.com/2012/09/07/huge-dna-project-offers-guidebook-to-human-genome-200-m-encode-collaboration-releases-30-new-studies/


NEW YORK — In the largest single batch of discoveries about human DNA since the completion of the human genome project in 2003, 442 scientists in labs across three continents released 30 studies jam-packed with finds this week.

The discoveries, representing what the journal Nature calls the “guidebook to the human genome,” range from the esoteric — what is a gene? — to the practical — that just 20 gene switches may underlie 17 seemingly unrelated cancers, giving companies a workable number of drug targets.



The studies come from a $196-million project called the Encyclopedia of DNA Elements, or ENCODE, whose goal is to take the babel produced by the human genome project — the sequence of 3.2 billion chemical “bases” or “letters” that constitute the human genome — and make sense of it.

Naive Bayes Classifier Example

http://www.inf.u-szeged.hu/~ormandi/ai2/06-naiveBayes-example.pdf

All models are wrong

All models are wrong, but some models are useful. — George Box (Box and Draper 1987,
p424).

Probabilistic learning

http://www.cs.cmu.edu/afs/cs/academic/class/15381-s07/www/slides/041707probabilisticLearning.pdf

http://sli.ics.uci.edu/Classes/2011S-274a

http://www.cs.ubc.ca/~murphyk/MLbook/pml-intro-22may12.pdf

Suppose instead of a yes or no  Predicting credit risk
answer want some estimate of how 
strongly we believe a loan applicant is 
a credit risk.

Bayesian classification

Linear / logistic regression

Estimating Class Membership Probabilities using Classifier Learners

http://hunch.net/~jl/projects/reductions/probing/aistat_real_final/aistat_final.pdfhttp://hunch.net/~jl/projects/reductions/probing/aistat_real_final/aistat_final.pdf

Probabilistic answers instead of discrete labels.

E.g. bagging, margin sigmoid, direct prediction

Probing Reduction

Multiple instance learning

http://www.cs.cmu.edu/~juny/MILL/review.htm

1. Introduction

Multiple Instance Learning (MIL) is proposed as a variation of supervised learning for problems with incomplete knowledge about labels of training examples. In supervised learning, every training instance is assigned with a discrete or real-valued label. In comparison, in MIL the labels are only assigned to bags of instances. In the binary case, a bag is labeled positive if at least one instance in that bag is positive, and the bag is labeled negative if all the instances in it are negative. There are no labels on the individual instances. The goal of MIL is to classify unseen bags or instances based on the labeled bags as the training data.

            The study on MIL was first motivated by the problem of predicting the drug molecule activity level. After that, many MIL methods have been proposed, such as learning axis-parallel concepts [Dietterich et al., 1997], diverse density [Maron and Lozano-Perez, 1998], extended Citation kNN [Wang and Zucker, 2000], etc. They have been applied to a wide spectrum of applications ranging from image concept learning and text categorization to stock market prediction. We review the several popular MIL methods and their applications in this document.

Alzheimer’s drugs take a new tack

http://www.nature.com/news/alzheimer-s-drugs-take-a-new-tack-1.11343

Hopes pinned on pre-emptive clinical trials after latest setbacks.

“The major conundrum in the field is: ‘are we just treating people too late?’.”

“We’ve had this concern for quite some time,” says Reiman, “that if these trials were negative we would see some major stakeholders and investors abandon amyloid-modifying treatments. We think that would be throwing the baby out with the bath water, and abandoning Alzheimer’s disease.”

Nature

 

489,

 

13–14

 

(06 September 2012)

 

doi:10.1038/489013a

 The bapineuzumab trials seem to have been more of an unqualified failure. This antibody drug targets the amyloid-β plaques, in hopes of awakening the immune system to clear them from the brain. But two trials in approximately 2,400 patients failed to show any benefit compared with a placebo, although this may have been because the drug was administered in lower doses than solanezumab, owing to its higher toxicity. Johnson & Johnson and its partner Pfizer, headquartered in New York city, say that they will vastly scale back development of bapineuzumab.

Biclustering algorithms

Iterative Signature Algorithm (ISA)

The intuition is that the genes in a bicluster are co-regulated and, thus,
for each sample the average gene expression over all the biclsuter’s genes should be surprising
(unusually high or low) and for each gene the average gene expression over all biclusters samples
should be surprising. This intuition is formalized using a simple linear model for gene expression
assuming normally distributed expression levels for each gene or sample

http://cs.fit.edu/~dmitra/Compbio/Resources/BiclusterRevw-Tanay2004.pdf

http://cran.r-project.org/web/packages/isa2/isa2.pdf