Thursday, October 1, 2015

October 2015 Goals

It's the beginning of the month, so its time for GOOOOAAAAALLLLLLSSSSSSSS!!!!!

But first lets check in on last months goals.

September 2015 Goals

1.Complete Results, Chapter 2, Thesis
2. Sending Draft of thesis to committee (mid Sept)
3. Begin preparations for defense presentation
4. Find TAship/Start TAing
5. Attend PCSGA (Network, view other presentations, work on presentation)
6. Submit Chapter 1 to JSR
7. Go camping!

How I did:

1. DONE!
2. DONE!
3. DONE!
4. DONE!
5. DONE!
6. DONE! AND REJECTED!
7. DONE!

SO SUCCESSFUL!!!!


Awesome lets see if I can beat that high score with this months goals. 

October 2015 Goals

1.  Complete Defense Presentation
2. Practice Defense Presentation
3. DEFEND!
4. Work on thesis edits!
5. Work on Chapter 1 edits for resubmission!
6. Work on Chapter 2 edits for submission!
7. Turn 29!
8. Supply UW Bee Club with lots of beer!
9. Win Winter Warmer Competition!


Also if anyone is interested in watching my thesis defense it will be 

Wednesday October 14th
12:00 PM PST
FSH 203

You can view a live stream of it here:
http://eagle.fish.washington.edu/jake/


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Tuesday, September 1, 2015

9 1 2015 Mean Ct Value Stats and Graphs R script

Steven has been helping me cull and curate the Ct values from the qPCR runs over the summer. He's developed a spreadsheet of mean Ct values of the good replicate runs. I've taken those values and created delta Ct values of the target over Actin. Then I log transformed the data to produce as normal data as possible. Finally I ran ANOVA and Tukey's Honest Significant Difference Post Hoc, on the log transformed data. Then created boxplots of the delta Ct values. I will create a table of the significant differences as a clear way to present the results. Below is the annotated code for the R script which I'll post a link to in the github repository at the bottom.

#Necessary Packages to manipulate data and plot values.
require(plyr)
require(ggplot2)
require(splitstackshape)

#Read in mean Ct value table
dCt<-read.csv("CTvalues83115.csv", header=T)
#Split SAMPLE_ID column to create columns for population, treatment, and sample number
dCt<-cSplit(dCt,"SAMPLE_ID", sep= "_", drop=F)
#rename columns appropriately
dCt<-rename(dCt,replace=c("SAMPLE_ID_1"="Pop","SAMPLE_ID_2"="Treat","SAMPLE_ID_3"="Sample"))

#divide each target of interest by the mean Ct value of the Actin Normalizing gene
dCt$CARM<-(dCt$CarmmeanCt/dCt$Actinmeanct)
dCt$TLR<-(dCt$TLRaverage/dCt$Actinmeanct)
dCt$CRAF<-(dCt$CRAFctaverage/dCt$Actinmeanct)
dCt$H2AV<-(dCt$H2AVavgct/dCt$Actinmeanct)
dCt$PGRP<-(dCt$PGRPaverage/dCt$Actinmeanct)
dCt$HSP70<-(dCt$HSP70averageCt/dCt$Actinmeanct)
dCt$BMP2<-(dCt$BMP2average/dCt$Actinmeanct)
dCt$GRB2<-(dCt$GRB2average/dCt$Actinmeanct)
dCt$PGEEP4<-(dCt$PGEEP4ctav/dCt$Actinmeanct)

#log transform the data to develop normality in data
dCt$CARMlog<-log(dCt$CARM)
dCt$TLRlog<-log(dCt$TLR)
dCt$H2AVlog<-log(dCt$H2AV)
dCt$PGRPlog<-log(dCt$PGRP)
dCt$HSP70log<-log(dCt$HSP70)
dCt$BMP2log<-log(dCt$BMP2)
dCt$GRB2log<-log(dCt$GRB2)
dCt$PGEEP4log<-log(dCt$PGEEP4)
dCt$CRAFlog<-log(dCt$CRAF)

#Run ANOVA's on all log transformed data as well as Tukey's Honestly Significant Difference post hoc test
CARM<-aov(CARMlog~Pop+Treat+Pop:Treat, data=dCt)
CARM
TukeyHSD(CARM)

TLR<-aov(TLRlog~Pop+Treat+Pop:Treat, data=dCt)
TLR
TukeyHSD(TLR)

H2AV<-aov(H2AVlog~Pop+Treat+Pop:Treat, data=dCt)
H2AV
TukeyHSD(H2AV)

PGRP<-aov(PGRPlog~Pop+Treat+Pop:Treat, data=dCt)
PGRP
TukeyHSD(PGRP)

HSP70<-aov(HSP70log~Pop+Treat+Pop:Treat, data=dCt)
HSP70
TukeyHSD(HSP70)

BMP2<-aov(BMP2log~Pop+Treat+Pop:Treat, data=dCt)
BMP2
TukeyHSD(BMP2)

GRB2<-aov(GRB2log~Pop+Treat+Pop:Treat, data=dCt)
GRB2
TukeyHSD(GRB2)

PGEEP4<-aov(PGEEP4log~Pop+Treat+Pop:Treat, data=dCt)
PGEEP4
TukeyHSD(PGEEP4)

CRAF<-aov(CRAFlog~Pop+Treat+Pop:Treat, data=dCt)
CRAF
TukeyHSD(CRAF)

#graph all raw mean Ct values to produce boxplots to visualize data

ggplot(data=dCt)+geom_boxplot(aes(x=Treat, y=CARM,fill=Pop))+theme_bw()+
  theme(axis.text.x=element_text(size=20), axis.text.y=element_text(size=20),
        axis.title.x=element_text(size=25), axis.title.y=element_text(size=25),
        legend.position=c(.1,.1),panel.grid.major=element_blank())+
  labs(x="Treatment", y="target/actin delta Ct")


ggplot(data=dCt)+geom_boxplot(aes(x=Treat, y=TLR, fill=Pop))+theme_bw()+
  theme(axis.text.x=element_text(size=20), axis.text.y=element_text(size=20),
        axis.title.x=element_text(size=25), axis.title.y=element_text(size=25),
        legend.position=c(.1,.1),panel.grid.major=element_blank())+
  labs(x="Treatment", y="target/actin delta Ct")

ggplot(data=dCt)+geom_boxplot(aes(x=Treat, y=H2AV,fill=Pop))+theme_bw()+
  theme(axis.text.x=element_text(size=20), axis.text.y=element_text(size=20),
        axis.title.x=element_text(size=25), axis.title.y=element_text(size=25),
        legend.position=c(.1,.1),panel.grid.major=element_blank())+
  labs(x="Treatment", y="target/actin delta Ct")

ggplot(data=dCt)+geom_boxplot(aes(x=Treat, y=PGRP,fill=Pop))+theme_bw()+
  theme(axis.text.x=element_text(size=20), axis.text.y=element_text(size=20),
        axis.title.x=element_text(size=25), axis.title.y=element_text(size=25),
        legend.position=c(.1,.1),panel.grid.major=element_blank())+
  labs(x="Treatment", y="target/actin delta Ct")

ggplot(data=dCt)+geom_boxplot(aes(x=Treat, y=HSP70,fill=Pop))+theme_bw()+
  theme(axis.text.x=element_text(size=20), axis.text.y=element_text(size=20),
        axis.title.x=element_text(size=25), axis.title.y=element_text(size=25),
        legend.position=c(.1,.1),panel.grid.major=element_blank())+
  labs(x="Treatment", y="target/actin delta Ct")

ggplot(data=dCt)+geom_boxplot(aes(x=Treat, y=BMP2,fill=Pop))+theme_bw()+
  theme(axis.text.x=element_text(size=20), axis.text.y=element_text(size=20),
        axis.title.x=element_text(size=25), axis.title.y=element_text(size=25),
        legend.position=c(.1,.1),panel.grid.major=element_blank())+
  labs(x="Treatment", y="target/actin delta Ct")

ggplot(data=dCt)+geom_boxplot(aes(x=Treat, y=GRB2,fill=Pop))+theme_bw()+
  theme(axis.text.x=element_text(size=20), axis.text.y=element_text(size=20),
        axis.title.x=element_text(size=25), axis.title.y=element_text(size=25),
        legend.position=c(.1,.1),panel.grid.major=element_blank())+
  labs(x="Treatment", y="target/actin delta Ct")

ggplot(data=dCt)+geom_boxplot(aes(x=Treat, y=PGEEP4,fill=Pop))+theme_bw()+
  theme(axis.text.x=element_text(size=20), axis.text.y=element_text(size=20),
        axis.title.x=element_text(size=25), axis.title.y=element_text(size=25),
        legend.position=c(.1,.1),panel.grid.major=element_blank())+
  labs(x="Treatment", y="target/actin delta Ct")

ggplot(data=dCt)+geom_boxplot(aes(x=Treat, y=CRAF,fill=Pop))+theme_bw()+
  theme(axis.text.x=element_text(size=20), axis.text.y=element_text(size=20),
        axis.title.x=element_text(size=25), axis.title.y=element_text(size=25),
        legend.position=c(.1,.1),panel.grid.major=element_blank())+
  labs(x="Treatment", y="target/actin delta Ct")

Graphs produced










Significant Differences Table
CARMlogTLRlogH2AVlogPGRPlogHSP70logBMP2logGRB2logPGEEP4logCRAFlog
Overall
C:TXXX
C:MXXX
T:MXXXXX
N:HX
H:SX
S:NX
Control
N:H
H:S
S:N
Temperature
N:H
H:S
S:N
Mechanical
N:H
H:S
S:NX
Control:Temp
N
H
SX
Control:Mech
N
H
SXX
Temp:Mech
N
H
SXXX

You can find the raw mean Ct values here and the R script here.
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September 2015 Goals

Its September 1st, Time for GOALLLLLLLLSSSSSSSSS!!!!! Let's take a quick look at the goals for last month and what I accomplished of them.

August 2015 Goals

1.Complete Analysis of qPCR data
2. Write Results
3. Complete Chapter 2
4. Complete Thesis
5. Schedule New Defense Date
6. Get TA position for the fall
7. Drink some delicious Flying Bike beer. 

How I did:

1. I'm really close. We've got most if not all of the qPCR data that we're going to use for chapter 2. I have written an R script to run through the average Ct data and produce statistics and graphs for it. I'm tidying it up today so I can basically say this one is done. 

2. This will be done in the next two weeks now that the Analysis is basically done. 

3. This will be done in the next two weeks.

4. See 2 & 3.

5. New Defense date is October 14th at 11 am.

6. Still trying to find one. Waiting to hear about the Intro R TAship.

7. The brewery opened and the beer is delicious!


September 2015 Goals

1.Complete Results, Chapter 2, Thesis
2. Sending Draft of thesis to committee (mid Sept)
3. Begin preparations for defense presentation
4. Find TAship/Start TAing
5. Attend PCSGA (Network, view other presentations, work on presentation)
6. Submit Chapter 1 to JSR
7. Go camping!
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Wednesday, August 26, 2015

8 26 2015 28s CTM qPCR 2

Today I ran two reps of the 28s target, one on the opticon and one at the CFX. We may use this as a normalizing gene if the reads look consistent. This is the run on the CFX. 

Primers:
170228s_3_FWDTAAGGCCAGTGTGGGAGAGAJH8/12/2015205559.88O.lurida"28S ribosomal protein S5, mitochondrial (MRP-S5) (S5mt)"Q5REJ1
170128s_3_REVCGCCTCACTTTTTGTGCCTCJH8/12/2015205560.04O.lurida"28S ribosomal protein S5, mitochondrial (MRP-S5) (S5mt)"Q5REJ1


Reagent Table:
VolumeReactions X80
Ssofast Evagreen MM 10800
FWD Primer0.540
REV Primer0.540
PCR H2O5400
1:9 cDNA4
  1. Added reagents from greatest to least volume
  2. Vortexed
  3. Centrifuged briefly
  4. Pipetted 11 ul Master Mix to each tube using a  multichannel pipetter
  5. Pipetted 9 ul of 1:9 cDNA each column using a channel pipetter
  6. Centrifuged plate at 2000 rpm for 1 minute
  7. Ran Program Below
Program:
StepTemperatureTime
Initiation95 C10 min
Elongation95 C30 sec
60 C1 min
Read
72 C30 sec
Read
Repeat Elongation 39 times
Termination95 C1 min
55 C1 sec
Melt Curve Manual ramp 0.2C per sec Read 0.5 C55 - 95 C30 sec
21 C10 min
End

Plate Layout:
12345678910
DNased 42215 HC1DNased 42215 NC1DNased 42215 SC1DNased 42215 HT1 1DNased 42215 NT1 1DNased 42215 ST1 1DNased 42215 HM1 1DNased 42215 NM1 1DNased 42215 SM1 1NTC
DNased 42215 HC2DNased 42215 NC2DNased 42215 SC2DNased 42215 HT1 2DNased 42215 NT1 2DNased 42215 ST1 2DNased 42215 HM1 2DNased 42215 NM1 2DNased 42215 SM1 2NTC
DNased 42215 HC3DNased 42215 NC3DNased 42215 SC3DNased 42215 HT1 3DNased 42215 NT1 3DNased 42215 ST1 3DNased 42215 HM1 3DNased 42215 NM1 3DNased 42215 SM1 3NTC
DNased 42215 HC4DNased 42215 NC4DNased 42215 SC4DNased 42215 HT1 4DNased 42215 NT1 4DNased 42215 ST1 4DNased 42215 HM1 4DNased 42215 NM1 4DNased 42215 SM1 4NTC
DNased 42215 HC5DNased 42215 NC5DNased 42215 SC5DNased 42215 HT1 5DNased 42215 NT1 5DNased 42215 ST1 5DNased 42215 HM1 5DNased 42215 NM1 5DNased 42215 SM1 5
DNased 42215 HC6DNased 42215 NC6DNased 42215 SC6DNased 42215 HT1 6DNased 42215 NT1 6DNased 42215 ST1 6DNased 42215 HM1 6DNased 42215 NM1 6DNased 42215 SM1 6
DNased 42215 HC7DNased 42215 NC7DNased 42215 SC7DNased 42215 HT1 7DNased 42215 NT1 7DNased 42215 ST1 7DNased 42215 HM1 7DNased 42215 NM1 7DNased 42215 SM1 7
DNased 42215 HC8DNased 42215 NC8DNased 42215 SC8DNased 42215 HT1 8DNased 42215 NT1 8DNased 42215 ST1 8DNased 42215 HM1 8DNased 42215 NM1 8DNased 42215 SM1 8

All 
NTCs

The amplification and melt curves look good. There's some minor primer dimer in one of the NTCs but its nothing to be concerned about. 

You can find the raw ct values here and the raw data file here. 
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8 26 2015 28s CTM qPCR 1

Today I ran two reps of the 28s target, one on the opticon and one at the CFX. We may use this as a normalizing gene if the reads look consistent. This is the run on the opticon. 

 Primers:
170228s_3_FWDTAAGGCCAGTGTGGGAGAGAJH8/12/2015205559.88O.lurida"28S ribosomal protein S5, mitochondrial (MRP-S5) (S5mt)"Q5REJ1
170128s_3_REVCGCCTCACTTTTTGTGCCTCJH8/12/2015205560.04O.lurida"28S ribosomal protein S5, mitochondrial (MRP-S5) (S5mt)"Q5REJ1
Reagent Table:
    VolumeReactions X80
    Ssofast Evagreen MM 10800
    FWD Primer0.540
    REV Primer0.540
    PCR H2O5400
    1:9 cDNA4
  1. Added reagents from greatest to least volume
  2. Vortexed
  3. Centrifuged briefly
  4. Pipetted 11 ul Master Mix to each tube using a  multichannel pipetter
  5. Pipetted 9 ul of 1:9 cDNA each column using a channel pipetter
  6. Centrifuged plate at 2000 rpm for 1 minute
  7. Ran Program Below
Program:
StepTemperatureTime
Initiation95 C10 min
Elongation95 C30 sec
60 C1 min
Read
72 C30 sec
Read
Repeat Elongation 39 times
Termination95 C1 min
55 C1 sec
Melt Curve Manual ramp 0.2C per sec Read 0.5 C55 - 95 C30 sec
21 C10 min
End

Plate Layout:
12345678910
DNased 42215 HC1DNased 42215 NC1DNased 42215 SC1DNased 42215 HT1 1DNased 42215 NT1 1DNased 42215 ST1 1DNased 42215 HM1 1DNased 42215 NM1 1DNased 42215 SM1 1NTC
DNased 42215 HC2DNased 42215 NC2DNased 42215 SC2DNased 42215 HT1 2DNased 42215 NT1 2DNased 42215 ST1 2DNased 42215 HM1 2DNased 42215 NM1 2DNased 42215 SM1 2NTC
DNased 42215 HC3DNased 42215 NC3DNased 42215 SC3DNased 42215 HT1 3DNased 42215 NT1 3DNased 42215 ST1 3DNased 42215 HM1 3DNased 42215 NM1 3DNased 42215 SM1 3NTC
DNased 42215 HC4DNased 42215 NC4DNased 42215 SC4DNased 42215 HT1 4DNased 42215 NT1 4DNased 42215 ST1 4DNased 42215 HM1 4DNased 42215 NM1 4DNased 42215 SM1 4NTC
DNased 42215 HC5DNased 42215 NC5DNased 42215 SC5DNased 42215 HT1 5DNased 42215 NT1 5DNased 42215 ST1 5DNased 42215 HM1 5DNased 42215 NM1 5DNased 42215 SM1 5
DNased 42215 HC6DNased 42215 NC6DNased 42215 SC6DNased 42215 HT1 6DNased 42215 NT1 6DNased 42215 ST1 6DNased 42215 HM1 6DNased 42215 NM1 6DNased 42215 SM1 6
DNased 42215 HC7DNased 42215 NC7DNased 42215 SC7DNased 42215 HT1 7DNased 42215 NT1 7DNased 42215 ST1 7DNased 42215 HM1 7DNased 42215 NM1 7DNased 42215 SM1 7
DNased 42215 HC8DNased 42215 NC8DNased 42215 SC8DNased 42215 HT1 8DNased 42215 NT1 8DNased 42215 ST1 8DNased 42215 HM1 8DNased 42215 NM1 8DNased 42215 SM1 8
All 
Amplification
Melt Curve
NTCs
Amplification
Melt Curve

The amplification and melt curves look good. There's some minor amplification in the NTCs but it appears to be a random product larger than the target so there's no worry about it. 

You can find the raw ct value data here and the raw data here.
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