CellSizes
Load ’em up.
library(dplyr)##
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## filter, lag## The following objects are masked from 'package:base':
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## intersect, setdiff, setequal, unionlibrary(tidyr)## Warning: package 'tidyr' was built under R version 3.2.3library(cowplot)## Warning: package 'cowplot' was built under R version 3.2.4## Loading required package: ggplot2## Warning: package 'ggplot2' was built under R version 3.2.4##
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## ggsavelibrary(lme4)## Warning: package 'lme4' was built under R version 3.2.3## Loading required package: Matrix## Warning: package 'Matrix' was built under R version 3.2.4##
## Attaching package: 'Matrix'## The following object is masked from 'package:tidyr':
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## expandlibrary(MCMCglmm)## Warning: package 'MCMCglmm' was built under R version 3.2.3## Loading required package: coda## Loading required package: ape## Warning: package 'ape' was built under R version 3.2.3cbPalette <- c("#999999", "#E69F00", "#56B4E9", "#009E73", "#F0E442", "#0072B2", "#D55E00", "#CC79A7")
setwd("~/Downloads")Get Data
Get cell size data, visually inspect
#get data on cell size, calculate means, make new table with just means
cellmeasures <- read.csv("fixingmeasures-updated.csv",header=TRUE)
cellmeans<-mutate(cellmeasures,mean_size=rowMeans(select(cellmeasures,6:185),na.rm=TRUE)) %>%
select(c(1:5,mean_size))Cell size differs between leaves and between adjacent, mid.
ggplot(cellmeans,aes(y=mean_size,x=interaction(pos,leaf),fill=as.factor(leaf)))+
geom_boxplot()+
scale_fill_manual("leaf",values=cbPalette[1:2], labels=c("3","4"))
#show ANOVA
summary(lmer(data=cellmeans,mean_size~leaf+pos+leaf:pos+(1|pop)))## Linear mixed model fit by REML ['lmerMod']
## Formula: mean_size ~ leaf + pos + leaf:pos + (1 | pop)
## Data: cellmeans
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## REML criterion at convergence: 7297.7
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## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.3468 -0.6223 0.0061 0.5936 3.2958
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## Random effects:
## Groups Name Variance Std.Dev.
## pop (Intercept) 93.62 9.676
## Residual 522.22 22.852
## Number of obs: 800, groups: pop, 19
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## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 198.258 8.391 23.626
## leaf -9.341 2.285 -4.088
## posmiddle 33.401 11.426 2.923
## leaf:posmiddle -1.373 3.232 -0.425
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## Correlation of Fixed Effects:
## (Intr) leaf psmddl
## leaf -0.953
## posmiddle -0.681 0.700
## leaf:psmddl 0.674 -0.707 -0.990Get growth rate data, merge with cell size data, calculate cell production
#get data on growth rates, using for now mean rate
cellget <- read.csv("CellProduction_All_crap.csv") %>%
filter(Ind != "NA")
cellprod<-mutate(cellget,mid3=rowMeans(select(cellget,7:10),na.rm=T),mid4=rowMeans(select(cellget,11:14),na.rm=T)) %>%
select(c(1:6,dailyavg3,dailyavg4)) %>%
gather(leaf,growth,c(dailyavg3,dailyavg4)) %>%
mutate(leaf=substr(leaf,9,9))
#merge two datasets and get production
master<-merge(cellprod,cellmeans,by=c("fullid","leaf")) %>% mutate(cell_production=growth/mean_size) %>% select(leaf,Popsep,Pop,genomesize,growth,pos,mean_size,cell_production)How’s the data look? Pretty normalish.
a=ggplot(data=master)+geom_histogram(aes(genomesize))
b=ggplot(data=master)+geom_histogram(aes(mean_size))
c=ggplot(data=master)+geom_histogram(aes(growth))
d=ggplot(data=master)+geom_histogram(aes(cell_production))
plot_grid(a,b,c,d)## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Genome size correlates
Growth decreases with increasing genome size, probably not significant, but varies a lot within families!
a=ggplot(master,aes(y=growth,x=genomesize,color=Pop))+
geom_point()+
geom_smooth(method="lm",se=FALSE)+
xlab("2C Genome Size")+
ylab("Mean Leaf Elongation")+
theme(axis.text=element_text(size=12),
axis.title=element_text(size=18,face="bold"))
b=ggplot(master,aes(y=growth,x=genomesize,color=leaf,linetype=pos))+
geom_point()+
geom_smooth(method="lm",se=FALSE)+
xlab("2C Genome Size")+
ylab("Mean Leaf Elongation")+
theme(axis.text=element_text(size=12),
axis.title=element_text(size=18,face="bold")) +
scale_color_manual("leaf",values=cbPalette[1:2])
plot_grid(a,b,nrow=2)
summary(lmer(data=master,growth~genomesize+leaf+pos+(1|Popsep:genomesize)+(1|Popsep)))## Linear mixed model fit by REML ['lmerMod']
## Formula: growth ~ genomesize + leaf + pos + (1 | Popsep:genomesize) +
## (1 | Popsep)
## Data: master
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## REML criterion at convergence: 2240.3
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## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.9540 -0.5221 -0.0549 0.5367 3.6580
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## Random effects:
## Groups Name Variance Std.Dev.
## Popsep:genomesize (Intercept) 0.50168 0.7083
## Popsep (Intercept) 0.09243 0.3040
## Residual 0.74099 0.8608
## Number of obs: 784, groups: Popsep:genomesize, 160; Popsep, 51
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## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 5.974e+00 2.081e+00 2.871
## genomesize -2.448e-01 3.478e-01 -0.704
## leaf4 1.275e+00 6.149e-02 20.735
## posmiddle -3.263e-14 6.149e-02 0.000
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## Correlation of Fixed Effects:
## (Intr) genmsz leaf4
## genomesize -0.999
## leaf4 -0.015 0.000
## posmiddle -0.015 0.000 0.000Cell size decreases with increasing genome size, probably not significant.
ggplot(master,aes(y=mean_size,x=genomesize,color=leaf,linetype=pos))+
geom_point()+
geom_smooth(method="lm")+
xlab("2C Genome Size")+
ylab("Mean Size")+
theme(axis.text=element_text(size=12),
axis.title=element_text(size=18,face="bold"))+
scale_color_manual("leaf",values=cbPalette[1:2])
summary(lmer(data=master,mean_size~genomesize+leaf+pos+(1|Popsep:genomesize)+(1|Popsep)))## Linear mixed model fit by REML ['lmerMod']
## Formula: mean_size ~ genomesize + leaf + pos + (1 | Popsep:genomesize) +
## (1 | Popsep)
## Data: master
##
## REML criterion at convergence: 7066.3
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## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9386 -0.5724 -0.0092 0.5262 3.2584
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## Random effects:
## Groups Name Variance Std.Dev.
## Popsep:genomesize (Intercept) 168.61 12.985
## Popsep (Intercept) 64.79 8.049
## Residual 378.58 19.457
## Number of obs: 784, groups: Popsep:genomesize, 160; Popsep, 51
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 208.103 42.232 4.928
## genomesize -6.083 7.054 -0.862
## leaf4 -9.894 1.390 -7.119
## posmiddle 28.702 1.390 20.652
##
## Correlation of Fixed Effects:
## (Intr) genmsz leaf4
## genomesize -0.999
## leaf4 -0.016 0.000
## posmiddle -0.016 0.000 0.000No effect of genome size on cell production.
ggplot(master,aes(y=cell_production,x=genomesize,color=leaf,linetype=pos))+
geom_point()+
geom_smooth(method="lm")+
xlab("2C Genome Size")+
ylab("Cell Production")+
theme(axis.text=element_text(size=12),
axis.title=element_text(size=18,face="bold"))+
scale_color_manual("leaf",values=cbPalette[1:2])
summary(lmer(data=master,cell_production~genomesize+leaf+pos+(1|Popsep:genomesize)+(1|Popsep)))## Linear mixed model fit by REML ['lmerMod']
## Formula:
## cell_production ~ genomesize + leaf + pos + (1 | Popsep:genomesize) +
## (1 | Popsep)
## Data: master
##
## REML criterion at convergence: -5631.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6867 -0.5390 -0.0246 0.4801 4.4324
##
## Random effects:
## Groups Name Variance Std.Dev.
## Popsep:genomesize (Intercept) 1.947e-05 0.004413
## Popsep (Intercept) 5.802e-06 0.002409
## Residual 3.074e-05 0.005544
## Number of obs: 784, groups: Popsep:genomesize, 160; Popsep, 51
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 0.0246327 0.0134659 1.829
## genomesize 0.0004104 0.0022496 0.182
## leaf4 0.0087092 0.0003960 21.991
## posmiddle -0.0045016 0.0003960 -11.367
##
## Correlation of Fixed Effects:
## (Intr) genmsz leaf4
## genomesize -0.999
## leaf4 -0.015 0.000
## posmiddle -0.015 0.000 0.000