http://dcscience.net/Lawrence-2016.pdf
The Last 50 Years: Mismeasurement and Mismanagement Are Impeding Scientific Research
Peter A. Lawrence
Department of Zoology, University of Cambridge, Cambridge, United Kingdom
Saturday, January 30, 2016
Friday, January 29, 2016
bio125 20150129 Friday, miniprep
===============
Section 3:
Mistakes:
Many students were confused by the difference from actual protocol and vendor's version
one group added twice amount of TE
tips were not changed for new samples in one group.
one group did not put zppy column inside of collection tubes.
Neutralization buffer were stored at 4C, and given to students when needed.
Reagent tubes were color coded. A good practice that seems to lead to few student mistakes.
5ml cells were spun down and given to students.
9:45am, transfer neutralized supernatant to zppy columns.
10:30am. 3 groups started nanodrop measurement.
by 11am, students finished nanodrop readings, and submit labnotes_2.
Classed end 11:30am.
===============
Section 4:
Mistakes:
one student mistook 10ul as 100ul when pipetting.
One student poured away TE buffer. (This student did not read before lab).
Make sure three samples are done at the same time.
Kioko: from lysis to neutralization should be quick.
2:30pm, most groups have finished elusion step.
Section 3:
Mistakes:
Many students were confused by the difference from actual protocol and vendor's version
one group added twice amount of TE
tips were not changed for new samples in one group.
one group did not put zppy column inside of collection tubes.
Neutralization buffer were stored at 4C, and given to students when needed.
Reagent tubes were color coded. A good practice that seems to lead to few student mistakes.
5ml cells were spun down and given to students.
9:45am, transfer neutralized supernatant to zppy columns.
10:30am. 3 groups started nanodrop measurement.
by 11am, students finished nanodrop readings, and submit labnotes_2.
Classed end 11:30am.
===============
Section 4:
Mistakes:
one student mistook 10ul as 100ul when pipetting.
One student poured away TE buffer. (This student did not read before lab).
Make sure three samples are done at the same time.
Kioko: from lysis to neutralization should be quick.
2:30pm, most groups have finished elusion step.
Thursday, January 28, 2016
Wednesday, January 27, 2016
bio125, 20160127, Wed, central dogma, dna repair, msh2 overview
-----------------
Section 3
Go over assignment and Student presentations
problems: double-strand and single strand DNA
APE on GeneX
9:55am Replication 3
Problem: Some studens thought DNA was synthesized 3-5 on the lagging strand.
Seize this teachable moment, ask student to draw replication fork, leading and lagging strands on paper.
5 min break.
APE on GeneX
9:55am Replication 3
Problem: Some studens thought DNA was synthesized 3-5 on the lagging strand.
Seize this teachable moment, ask student to draw replication fork, leading and lagging strands on paper.
5 min break.
by 11am, Rstudio usage on standard curves. I made a mistake on the dilution.
11am-> APE on MSH2 annotation.
video on miniprep for Thu
-----------------
Section 4: (due to snow, bradford assay and serial dilution lab data were not analyzed).
Summaryize lab
1-2pm, Go over standard curve plot.
2pm-3pm APE pMSH2 annotation
KD was not practicing APE.
Went through math5 standard curve as well.
===============
pre-class: central dogma, DNA repair , MSH2 overview (abstract reading),,
In-class:
(1) Replication (in class, drawings-mutation),
(2) mutation, HSV paper
(2) Math problem 5, standard curve
(3) MCAT
Running R code on linear regression, generating plot and save figures
ApE usage
replication assignment 3
mutation&repair assignment 1
reference:
http://hongqinlab.blogspot.com/2015/01/bio125-jan-27-tue-central-dogma-dna.html
http://hongqinlab.blogspot.com/2014/03/bio125-central-dogma-review.html
Tuesday, January 26, 2016
Eventbrite help pages
Eventbrite, Export attendee information
https://www.eventbrite.com/support/articles/en_US/How_To/how-to-edit-and-export-an-attendee-summary-report?lg=en_US
https://www.eventbrite.com/support/articles/en_US/How_To/how-to-edit-and-export-an-attendee-summary-report?lg=en_US
H2O2-LOH error bars, _1a.batchPlotH2O2-LOH.2016Jan25.R
# 20160125, try to add error bars to plots
rm=(list=ls())
setwd("~/github/LOH_H2O2_2016/analysis")
debug = 0;
#all data are in 'data.H2O2-LOH'
FileList = list.files( path="../data.H2O2-LOH/"); FileList;
if( debug > 5) {FileList = FileList[1:2]}
# infile = FileList[2]
for( infile in FileList) {
# infile='M1-2,20111207.modified.csv' #debug for NA, and low lead concentration
# infile = 'M2-8,08172011.H2O2.LOH.modified.csv'; #viability plot error
# infile = 'M2-8,06062011.H2O2onLOH.csv'; #b plot error
fullFileName = paste('../data.H2O2-LOH/',infile, sep='');
mylabel = infile
tb = read.csv(fullFileName, colClasses=c("character",NA, NA, "character", rep("numeric",8 ), NA));
names(tb) = c("Strain", "OD600", "Dilution","Date","H2O2stock", "White", "Black", "halfBlack", "quarterBlack", "ThreeQBlack", "QQBlack", "Other", "Notes")
######## set zeros
mycolumns = c("White","Black","halfBlack", "quarterBlack","ThreeQBlack", "QQBlack", "Other");
for( i in 1:length(tb[,1])) {
for ( j in mycolumns) {
if( is.na(tb[i,j]) ) { tb[i,j]= 0 }
}
}
#H2O2 working stock is 2X
tb$H2O2 = tb$H2O2stock/2
tb$tot = tb$White + tb$Black + tb$halfBlack + tb$quarterBlack + tb$ThreeQBlack + tb$QQBlack + tb$Other
tb.ori = tb;
tb = tb[ ! is.na(tb$White), ]
tb$Dilution = tb$Dilution / tb$Dilution[1]
######## normalize all data
mycolumns = c("White","Black","halfBlack", "quarterBlack","ThreeQBlack", "QQBlack", "Other","tot");
for ( j in mycolumns) {
tb[,j] = tb[,j] * tb$Dilution
}
####### find out means
H2O2 = sort(unique( tb$H2O2))
#s = H2O2
tbm = data.frame(cbind(H2O2))
for ( i in 1:length(H2O2)) {
c = H2O2[i]
tmp = tb[ tb$H2O2==c, ]
tbm$tot[i] = mean(tmp$tot, na.rm=T)
tbm$tot.sd[i] = sd(tmp$tot, na.rm=T)
tbm$White[i] = mean(tmp$White, na.rm=T)
tbm$White.sd[i] = sd(tmp$White, na.rm=T)
tbm$Black[i] = mean(tmp$Black, na.rm=T)
tbm$Black.sd[i] = sd(tmp$Black, na.rm=T)
tbm$halfBlack[i] = mean(tmp$halfBlack, na.rm=T)
tbm$halfBlack.sd[i] = sd(tmp$halfBlack, na.rm=T)
tbm$quarterBlack[i] = mean(tmp$quarterBlack, na.rm=T)
tbm$ThreeQBlack[i] = mean(tmp$ThreeQBlack, na.rm=T)
tbm$QQBlack[i] = mean(tmp$QQBlack, na.rm=T);
}
tbm = tbm[tbm$tot>1, ] #remove plates with zero colonies
###### some manual curations here
#tbm$halfBlack[tbm$halfBlack==0 & tbm$H2O2>0] = NA;
###### calculate fractions
tbf = tbm;
tbf$s = tbf$tot / max(tbf$tot)
tbf$s.sd = tbm$tot.sd / max(tbm$tot)
for ( j in 3:11) {
tbf[, j] = tbf[,j] / tbf$tot
}
tbf$Black[tbf$Black<0]=NA; #remove weird experimental data, such as low-lead concentration effect
pdf(paste("sandbox/",infile, ".batch.sd.pdf", sep=''), width=5, height=5)
### full black plot
plot( tbf$s ~ tbf$H2O2, col="blue", axes=F, xlab='', ylab='', ylim=c(-0.05, 1.2), type='p');
lines( tbf$s ~ tbf$H2O2, col="blue",lty=2)
arrows( tbf$H2O2, (tbf$s - tbf$s.sd), tbf$H2O2, (tbf$s + tbf$s.sd), length=0.1, angle=90,code=3, lty=1, col="blue" );
axis( 4, at=pretty(c(0, 1.2)), col='black')
legend ( max(H2O2)*0.7, 0.9, c("viability","black"), col=c("blue","black"), lty=c(2,1), pch=c(1,16) )
par(new=T)
plot( tbf$Black ~ tbf$H2O2, pch=16, xlab='H2O2',ylab="black", ylim=c(-0.01, max(tbf$Black, na.rm = T)*3))
lines(tbf$Black ~ tbf$H2O2)
bottoms = tbf$Black -tbf$Black.sd
bottoms[bottoms<0]= 0.001
arrows( tbf$H2O2, bottoms, tbf$H2O2, (tbf$Black + tbf$Black.sd), length=0.1, angle=90,code=3, lty=1, col="gray" );
title(mylabel)
## full black -log plot
#tbf$H2O2[tbf$H2O2==0] = min(H2O2[-1])/10;
#with( tbf, plot( tot ~ log10(H2O2), col="blue"));
#par(new=T)
#with( tbf, plot( Black ~ log10(H2O2), pch=16))
#tbf$H2O2[tbf$H2O2==0] = min(H2O2[-1])/10;
#with( tbf, plot( s ~ log10(H2O2), col="blue", axes=F, xlab="H2O2", ylab='viability'), ylim=c(-0.5, 1.1) );
#xlabels = log10(c(0.001,0.001, 0.005, 0.01, 0.025, 0.05, 0.1, 0.15))
#axis(1, at = xlabels, labels= 10^xlabels)
#ylabels = c(0, 0.25, 0.5, 0.75, 1.0)
#axis(2, at=ylabels, labels=ylabels);
#par(new=T)
#with( tbf, plot( Black ~ log10(H2O2), pch=16, axes=F, xlab='', ylab=''))
#axis(4, pretty(range(tbf$Black)))
#title(mylabel)
### half black plot
plot( tbf$s ~ tbf$H2O2, col="blue", axes=F, xlab='', ylab='', ylim=c(0, 1.2), type='p', main=mylabel);
lines( tbf$s ~ tbf$H2O2, col="blue",lty=2)
axis( 4, at=pretty(c(0, 1.2)))
legend ( max(H2O2)*0.7, 0.5, c("viability","half-black"), col=c("blue","red"), lty=c(2,1), pch=c(1,16) )
par(new=T)
plot( tbf$halfBlack ~ tbf$H2O2, pch=16, xlab='H2O2',ylab="half-black", col='red', ylim=c(-0.005, max(tbf$halfBlack, na.rm=T)*2 ))
lines(tbf$halfBlack ~ tbf$H2O2, col='red')
bottoms = tbf$halfBlack - tbf$halfBlack.sd
bottoms[bottoms<0]= 0
arrows( tbf$H2O2, bottoms, tbf$H2O2, (tbf$halfBlack + tbf$halfBlack.sd), length=0.1, angle=90,code=3, lty=1, col="gray" );
title(mylabel)
### half/full plot
tbf$half.vs.full = tbf$halfBlack / tbf$Black
plot( tbf$s ~ tbf$H2O2, col="blue", axes=F, xlab='', ylab='', ylim=c(0, 1.2), type='p', main=mylabel);
lines( tbf$s ~ tbf$H2O2, col="blue",lty=2)
axis( 4, at=pretty(c(0, 1.2)))
legend ( max(H2O2)*0.7, 0.5, c("viability","half/full"), col=c("blue","green"), lty=c(2,1), pch=c(1,16) )
par(new=T)
plot( tbf$half.vs.full ~ tbf$H2O2, pch=16, xlab='H2O2',ylab="half/full", col='green')
lines(tbf$half.vs.full ~ tbf$H2O2, col='green')
bottoms = tbf$Black -tbf$Black.sd
bottoms[bottoms<0]= 0.001
title(mylabel)
dev.off() #end pdf
} #infile loop
#quit("yes")
Friday, January 22, 2016
bio125, 20160122Fri bradford assay of protein concentration, serial dilution
Snow storm is coming. Try to finish the lab quickly.
=======================
9am section 3.
AH went over lab safety.
9:07am, AH explained pipette usage and tips, two level of pressure. Properly eject the tips during exchange.
Shown recipe on the screen using student preclass submission and flow-chart.
9:28am, emphasize that bradford be added at the last step.
Students were asked to figure out way to perform experiement on their own, only asking for help when they are 'challenged'. AH mostly answered students questions.
by 9:52, most students are adding NaCl solutions to eppendorf tubes.
Student Problems:
One group add the curve in the wrong way. This is a recurring problem.
10am:
I then went over R code on standard curve preparation and analysis. I explained that sample codes will always provide to students in bio125.
R and Rstudio demo for data analysis
Review homework and assignment. No time, leave for next class.
=======================
9am section 3. 1-2pm. College closes at 2pm.
Only 10 students out of 18 students did pre-lab assignment. Among the 10 submitted, many of them are wrong.
Make sure protein are thawed.
Make sure unknown protein tube is not mistaken as BSA standard.
To save time, add solution directly to the curvettes.
Problems
Some student touch tips into working solutions and then used the same tips for stocks again.
Several groups still add wrong volumes.
references:
http://hongqinlab.blogspot.com/2015/01/bio125-thu-bradford-protein.html
http://hongqinlab.blogspot.com/2014/01/bio125-jan-21-2014-msh2-serial-dilution.html
Wednesday, January 20, 2016
tar backup byte laptop
lapphy-pc1:~ hqin$ cat backup.byte.sh
date
echo "Back up Byte laptop now"
time perl /Users/hqin/perl/backup4.pl /Volumes/bear/byte > /tmp/_backup.hqin.txt
date
lapphy-pc1:~ hqin$ sh backup.byte.sh
Wed Jan 20 20:23:37 EST 2016
Back up Byte laptop now
defined(%hash) is deprecated at /Users/hqin/lib/perl/Util.pm line 610.
(Maybe you should just omit the defined()?)
du: Library/Saved Application State/com.adobe.flashplayer.installmanager.savedState: Permission denied
tar: wdijk-112507/trash: Couldn't visit directory: Permission denied
tar: wdijk-112507/doc: Couldn't visit directory: Permission denied
tar: day2.tab: could not open file: Permission denied
tar: day3.tab: could not open file: Permission denied
tar: day4.tab: could not open file: Permission denied
tar: Error exit delayed from previous errors.
tar: Google: Cannot stat: No such file or directory
tar: Error exit delayed from previous errors.
real 82m9.817s
user 0m9.009s
sys 5m13.683s
Wed Jan 20 21:45:47 EST 2016
bio125, 20160120Wed
Section 1:
9am-10:15 _Go over student assignments
NCBI data exercise
8:20-9am, do exericse in class on nucleotide structure, chromosome, and replication.
Problems: replication origin of circular chromosome in bacteria, heavy and light chain of replicated DNA,
10:20 _ApE
Add flowchart assignment for Day 2 on Moodle.
Socrative SPD on nucleotide structure
Summarize class by calling students to answer some problems on 3'OH, cell cycle phases.
Prep next class. Explain 5X meaning.
1 part of mix of y , 5 part of water,
Section 2:
1pm
_Go over student assignments. Several groups in this section had problems with math problems.
_NCBI nucleotide database
PRoblems, 3' and 2' deoxy ribose, proofreading, genes in replication, GC content,
Not used:
_Central dogma review (concept map, group activity)
_DNA structure and replication, with MSH2's role mentioned briefly1 part of mix of y , 5 part of water,
Section 2:
1pm
_Go over student assignments. Several groups in this section had problems with math problems.
_NCBI nucleotide database
1:50pm, right hand and left hand helix.
2pm, ask students to reviews the questions.
to 3pm. APE exercise on translate MSH2 ORF
_Central dogma review (concept map, group activity)
human genome 3 billion DNAbase
yeast, how big?
Past project posteryeast, how big?
Past student presentations on MSH2 project
skipped: DNA double struck in SPDB, based bio233 materials on DNA.
Sunday, January 17, 2016
R GEO paring problem on osX
Working with R GEO on osX, somehow the GEO object cannot be parsed. I then update GEO to the most recent version. Problem persisted.
After restarting the computer, everything seems fine. Not sure what have happenned.
After restarting the computer, everything seems fine. Not sure what have happenned.
java problem for R package installed as sources.
install rJava as sources.
checking Java support in R... present:
interpreter : '/usr/bin/java'
archiver : '/usr/bin/jar'
compiler : '/usr/bin/javac'
header prep.: '/usr/bin/javah'
cpp flags : ''
java libs : ''
configure: error: One or more Java configuration variables are not set.
Make sure R is configured with full Java support (including JDK). Run
R CMD javareconf
as root to add Java support to R.
http://stackoverflow.com/questions/3311940/r-rjava-package-install-failing
This seems to be a common problem.
I went back to R and choose to install binaries. The installation passed.
Saturday, January 16, 2016
limma, model, contrast matrix, topTable
limma reference:
https://support.bioconductor.org/p/57268/
topTable output
[31] Smyth, G. (2004). Linear models and empirical bayes methods for assessing dierential
expression in microarray experiments. Statistical Applications in Genetics and Molecular
Biology 3, Article 3.
[49] Yang, Y.H. and Speed, T.P. (2003). Design and analysis of comparative microarray experiments.
In T.P. Speed, editor, Statistical Analysis of Gene Expression Microarray Data,
pages 35-91. Chapman & Hall/CRC Press.
https://support.bioconductor.org/p/57268/
topTable output
Friday, January 15, 2016
bio125 20160115 Day 2
List of things:
Overview of course
lab safety signature
Pubmed exercise
============================================
Section 1
9am Ask students to read lab safety.
PubMed search
Primary literature: original work versus reviews and commentaries.
Spelman [ad]
9:27 group work on cause of cancer
Smoking and lung cancer: fair coin and biased coin
coin toss for dish washing
10am check etext assignment. still not released
by 10:30 3 math assignment
Go over math assignment
10:30am, go over R/Rstuido.
11am. end the class.
group 1, 2: colorectal cancer, MSH2
group 3: breast cancer, BRCA1
group 4; leukemia,
group 5: oral cancer, smoking, alcohol PMID 25564114
group 6: ovarian cancer: BRCA1 and 2
group 7: prostate cancer:
group 8: stomach cancer
group 9: pancreatic cancer
group 10: liver cancer
1:14-2pm PubMed search
Group work on cause of cancer
Primary literature: original work versus reviews and commentaries.
Spelman [ad]
Morehouse
by2:40 math assignment. Ask students work on the board.
2:50pm, youtube live recording of ppt presenation.
Overview of course
lab safety signature
Pubmed exercise
Socractive
Review math assignments
eTextbook assignment and end of module questions
Review math assignments
eTextbook assignment and end of module questions
Simple R exercise
Section 1
9am Ask students to read lab safety.
PubMed search
Primary literature: original work versus reviews and commentaries.
Spelman [ad]
9:27 group work on cause of cancer
Smoking and lung cancer: fair coin and biased coin
coin toss for dish washing
10am check etext assignment. still not released
by 10:30 3 math assignment
Go over math assignment
10:30am, go over R/Rstuido.
11am. end the class.
group 1, 2: colorectal cancer, MSH2
group 3: breast cancer, BRCA1
group 4; leukemia,
group 5: oral cancer, smoking, alcohol PMID 25564114
group 6: ovarian cancer: BRCA1 and 2
group 7: prostate cancer:
group 8: stomach cancer
group 9: pancreatic cancer
group 10: liver cancer
============================================
Section 2
Ask students to read lab safety.Section 2
1:14-2pm PubMed search
Group work on cause of cancer
Primary literature: original work versus reviews and commentaries.
Spelman [ad]
Morehouse
by2:40 math assignment. Ask students work on the board.
2:50pm, youtube live recording of ppt presenation.
Summary and end the class.
Did not have time for R/Studio simple R exercise.
============================================
Reference:
Reference:
Thursday, January 14, 2016
BIO125 video lecture summary page, Spring 2016
BIO125 videl lectures recordings
2016013Wed
20160120Wed
section 3: https://youtu.be/-oUBbbMxoRY
section4: https://youtu.be/edWy81_FKEc
20160127Wed
Section 3:
https://www.youtube.com/watch?v=SC1wGg8fH1c
Section 4:
https://www.youtube.com/watch?v=b06PfqpHJwg
2016013Wed
Section 3 https://youtu.be/i-MpI3v9aQI20160115Fri
Section 4 https://www.youtube.com/watch?v=bkYOuI61VA8
section 3: https://youtu.be/PSnwJQrV0C0
section 4: https://youtu.be/3AP6KpIq8Lg
section 3: https://youtu.be/-oUBbbMxoRY
section4: https://youtu.be/edWy81_FKEc
20160127Wed
Section 3:
https://www.youtube.com/watch?v=SC1wGg8fH1c
Section 4:
https://www.youtube.com/watch?v=b06PfqpHJwg
Wednesday, January 13, 2016
Day 1, bio125 20160113Wed, section 3 and 4
List of things:
Names tags,
Syllabus, Learning objectives
Pre-survey on computing thinking:
https://docs.google.com/forms/d/1g3hrO8QtbAGcW3h2Oh-PcRr-OlmnTAoCXw71iSeiHrM/viewform
preclass knowledge assessment
APE installation
R/Rstudio installation, data analysis
Assignment due before class
============================================
Section 1
=> Dynamic books, access code problem
=> Names tags,
=>How to find upcoming assignments on Moodle?
Use a seat map to connect student names, faces and seatings.
9:25
=>Signatures, notepad and pens
=>IRB, academic integrity, photo video release form (Need to add Yes or No on the form)
14:19
=>R Rstudio. R has to be run directly on Spelman HP laptops.
=> Past exam
HP laptop log, wireless connections
Bring ethernet cables
Presentation orders. natural group order
Did Group building on cancer.
PubMed search
Primary literature: original work versus reviews and commentaries.
group 1, 2: colorectal cancer, MSH2
group 3: breast cancer, BRCA1
group 4; leukemia,
group 5: oral cancer, smoking, alcohol PMID 25564114
group 6: ovarian cancer: BRCA1 and 2
group 7: prostate cancer:
group 8: stomach cancer
group 9: pancreatic cancer
group 10: liver cancer
ToDo for Friday: R code for math assignments.
Summary: presentation groups of next class on homework assignments.
Names tags,
Syllabus, Learning objectives
Pre-survey on computing thinking:
https://docs.google.com/forms/d/1g3hrO8QtbAGcW3h2Oh-PcRr-OlmnTAoCXw71iSeiHrM/viewform
preclass knowledge assessment
APE installation
R/Rstudio installation, data analysis
Socractive
Notebook
Past student presentationsAssignment due before class
============================================
Section 1
=> Dynamic books, access code problem
=> Names tags,
=>How to find upcoming assignments on Moodle?
=>Syllabus, flipped classroom, assignments are mostly due before class,
Learning objectivesUse a seat map to connect student names, faces and seatings.
9:25
=>Signatures, notepad and pens
=>IRB, academic integrity, photo video release form (Need to add Yes or No on the form)
9:34
=> computing survey
=> computing survey
Emails the link through the commons news forum for unregistered students.
9:45=>pre-assessment
10:06
=> Download ApE. About 2 students do not have laptops.
ApE installation. On Yosemite, when the security setting is not changed, OS X gave a warning as if the downloaded software is damaged.
On some windows computer, ApE download as a ZIP file and have to be extract.
by 10:42, R Rstudio. R has to be run directly on Spelman HP laptops.
10:50am, recall names and ended the class.
============================================
Section 2
1pm
=> Dynamic books, access code problem
Problem: No assignments can be seen on your textbook.
13:16
=> Names tags,
Use a seat map to connect student names, faces and seatings.
grades, past exam, assignment due before class.
** Use the past exam average to explain assessment, and how it can be helpful to improve teaching and learning strategies.
1:30 =>Signatures, notepad and pens
=>IRB, academic integrity, photo video release form (Need to add Yes or No on the form)
=> computing survey
14:03
=> Download ApE. About 2 students do not have laptops.
Section 2
1pm
=> Dynamic books, access code problem
Problem: No assignments can be seen on your textbook.
13:16
=> Names tags,
Use a seat map to connect student names, faces and seatings.
by 1:30
=>Syllabus, flipped classroom, assignments are mostly due before class,
Learning objectives=>Syllabus, flipped classroom, assignments are mostly due before class,
grades, past exam, assignment due before class.
** Use the past exam average to explain assessment, and how it can be helpful to improve teaching and learning strategies.
1:30 =>Signatures, notepad and pens
=>IRB, academic integrity, photo video release form (Need to add Yes or No on the form)
Emails the link through the commons news forum for unregistered students.
13:55
=>pre-assessment
=>pre-assessment
14:03
14:19
=>R Rstudio. R has to be run directly on Spelman HP laptops.
=>How to find upcoming assignments on Moodle?
2:40pm. Recall names and ended the class.
============================================
Reference:
http://hongqinlab.blogspot.com/2015/01/bio125-day-1-week-1.html
http://hongqinlab.blogspot.com/2014/01/bio125-day-1-jan-16-thu-spring-2014.html
Reference:
http://hongqinlab.blogspot.com/2015/01/bio125-day-1-week-1.html
http://hongqinlab.blogspot.com/2014/01/bio125-day-1-jan-16-thu-spring-2014.html
Left for Day 2
=>lab safety form (I added the students names to the list).
Socrative login. Go over slides to test socrative. (students have trouble to generate account. Later, I figure out that students names are only available in pre-defined quizzes).
Socrative login. Go over slides to test socrative. (students have trouble to generate account. Later, I figure out that students names are only available in pre-defined quizzes).
=> Pubmed
=> SocrativeHP laptop log, wireless connections
Bring ethernet cables
Presentation orders. natural group order
Did Group building on cancer.
PubMed search
Primary literature: original work versus reviews and commentaries.
group 1, 2: colorectal cancer, MSH2
group 3: breast cancer, BRCA1
group 4; leukemia,
group 5: oral cancer, smoking, alcohol PMID 25564114
group 6: ovarian cancer: BRCA1 and 2
group 7: prostate cancer:
group 8: stomach cancer
group 9: pancreatic cancer
group 10: liver cancer
ToDo for Friday: R code for math assignments.
Tuesday, January 12, 2016
GEO, Ovarian cancer, Cisplatin
Ovarian cancer, Cisplatin
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# Differential expression analysis with limma
library(Biobase)
library(GEOquery)
library(limma)# gset <- getGEO("GSE47856", GSEMatrix =TRUE)
# save.image("GSE47856.RData")
# rm(list=ls())Study the structure of gset
# study the meta data
experimental_design = gset@phenoData@data
# gset@phenoData@varMetadata
# experimental_design[, "source_name_ch1"][1:10]
experimental_design[1:10, c("characteristics_ch1", "characteristics_ch1.2")] characteristics_ch1 characteristics_ch1.2
GSM1160723 cell line: HeyA8 treatment: none
GSM1160724 cell line: HeyA8 treatment: none
GSM1160725 cell line: HeyA8 treatment: none
GSM1160726 cell line: HeyA8 treatment: none
GSM1160727 cell line: HeyA8 treatment: none
GSM1160728 cell line: HeyA8 treatment: Cisplatin
GSM1160729 cell line: HeyA8 treatment: Cisplatin
GSM1160730 cell line: HeyC2 treatment: none
GSM1160731 cell line: HeyC2 treatment: none
GSM1160732 cell line: HeyC2 treatment: noneunique( experimental_design$characteristics_ch1 ) [1] cell line: HeyA8 cell line: HeyC2 cell line: A2780
[4] cell line: OVCA420 cell line: OVCA429 cell line: PA-1
[7] cell line: TYK-nu cell line: CH1 cell line: OV90
[10] cell line: FU-OV-1 cell line: A2008 cell line: DOV13
[13] cell line: OVCA433 cell line: OVCAR-10 cell line: DOV13B
[16] cell line: C13 cell line: IGROV-1 cell line: OVCAR-8
[19] cell line: M41 cell line: A2780cisR cell line: ovary1847
[22] cell line: Caov-2 cell line: Caov-3 cell line: Hey
[25] cell line: JHOS-2 cell line: JHOS-3 cell line: OAW28
[28] cell line: OAW42 cell line: DOV13A cell line: OV56
[31] cell line: OVCAR-3 cell line: OVCA432 cell line: OVCAR-2
[34] cell line: OVCAR-5 cell line: OVK-18 cell line: PEO1
[37] cell line: RMG-I cell line: RMG-II cell line: SKOV-4
[40] cell line: SKOV-3 cell line: SKOV-6 cell line: SKOV-8
[43] cell line: TAYA cell line: TOV-112D cell line: TOV-21G
[46] cell line: UWB1.289
46 Levels: cell line: A2008 cell line: A2780 ... cell line: UWB1.289sel = grep("HeyC2", experimental_design$characteristics_ch1)
gset = gset[ ,sel]ex <- exprs(gset)
hist(ex[,1])
hist(log2(ex[,1]))
boxplot(ex)
Log2 transform. Not sure about LogC
qx <- as.numeric(quantile(ex, c(0., 0.25, 0.5, 0.75, 0.99, 1.0), na.rm=T))
LogC <- (qx[5] > 100) ||
(qx[6]-qx[1] > 50 && qx[2] > 0) ||
(qx[2] > 0 && qx[2] < 1 && qx[4] > 1 && qx[4] < 2)
if (LogC) { ex[which(ex <= 0)] <- NaN
exprs(gset) <- log2(ex) }head(ex) GSM1160730 GSM1160731 GSM1160732 GSM1160733 GSM1160734 GSM1160735
7892501 7.34145 5.99586 6.36227 7.71416 6.96127 7.66464
7892502 4.18187 3.83990 3.27792 4.86781 3.55629 4.10709
7892503 4.13694 3.35185 3.05373 3.59022 3.33752 3.33640
7892504 9.62438 9.01348 8.63508 9.06869 9.11466 9.04583
7892505 2.30237 2.23863 2.68872 4.01024 2.68134 2.19076
7892506 4.21606 2.84229 3.93290 3.51904 3.54838 3.37970sums = apply(ex,2, sum)
sums / max(sums)GSM1160730 GSM1160731 GSM1160732 GSM1160733 GSM1160734 GSM1160735
0.9921900 0.9946481 0.9945619 1.0000000 0.9930194 0.9935355 medians = apply(ex, 2, median)
mediansGSM1160730 GSM1160731 GSM1160732 GSM1160733 GSM1160734 GSM1160735
5.76975 5.85706 5.81737 5.78378 5.85064 5.76896 means = apply(ex, 2, mean)
meansGSM1160730 GSM1160731 GSM1160732 GSM1160733 GSM1160734 GSM1160735
6.091404 6.106495 6.105966 6.139352 6.096496 6.099664 gset@phenoData@data[, c("characteristics_ch1", "characteristics_ch1.2")] characteristics_ch1 characteristics_ch1.2
GSM1160730 cell line: HeyC2 treatment: none
GSM1160731 cell line: HeyC2 treatment: none
GSM1160732 cell line: HeyC2 treatment: none
GSM1160733 cell line: HeyC2 treatment: Cisplatin
GSM1160734 cell line: HeyC2 treatment: Cisplatin
GSM1160735 cell line: HeyC2 treatment: Cisplatingset@phenoData@data$characteristics_ch1.2 V9 V10 V11
treatment: none treatment: none treatment: none
V12 V13 V14
treatment: Cisplatin treatment: Cisplatin treatment: Cisplatin
Levels: treatment: Cisplatin treatment: nonetreatments <- make.names(gset@phenoData@data$characteristics_ch1.2)
fl.names = unique(treatments)
fl = as.factor( LETTERS[1:length(fl.names)])
names(fl) = fl.names
gset$description <- fl
design <- model.matrix(~ description + 0, gset)
colnames(design) <- levels(fl)
fit <- lmFit(gset, design)
cont.matrix <- makeContrasts( A-B, levels=fl)
fit2 <- contrasts.fit(fit, cont.matrix)
fit2 <- eBayes(fit2, 0.01)
tT <- topTable(fit2, adjust="fdr", sort.by="B", number=50)
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