With only 6 strains, there is a negative correlation between mitotic asymmetry (L0) and first plasmid loss measure, pvalue = 0.04. (I missed M1-2. It seems to be labeled as M12 in the plasmid loss data set. After add M1-2, partial correlation L0.small ~ PL1 + CLS still works.
The second plasmid loss measure is not significantly correlated.
#> pTb[pTb<0.05] #PL2
#<NA> Nloh <NA> PL1 PL2 PL
#NA 0.0111638902 NA 0.0022934461 0.0000000000 0.0001032832
#> pTb[pTb<0.09] #PL1
#<NA> Nloh b.min.sd TLmax.sd <NA> L0.small PL1 PL2 PL
#NA 3.138762e-02 8.707534e-02 5.595864e-02 NA 3.957404e-02 0.000000e+00 2.293446e-03 5.375196e-05
The combined measure has a pvalue = 0.06.
#> pTb[pTb<0.09]
#<NA> Nloh b.min.sd TLmax.sd <NA> L0.small PL1 PL2 PL
#NA 1.647319e-02 5.877810e-02 7.604223e-02 NA 6.414451e-02 5.375196e-05 1.032832e-04 0.000000e+00
Partial correlation tb$L0.small ~ tb$PL1 + tb$CLS is still good. (but not with PL2)
TODO: H2O2 data, Tg/Tc
See: github/0.network.aging.prj/10.choy/_choy.plasmidLoss.20140926.R
rm(list=ls())
require(xlsx)
setwd("~/projects/0.network.aging.prj/10.choy")
list.files()
plasmidlosstb = read.xlsx("plasmid loss assays.xlsx", 1)
names(plasmidlosstb)= c("strain", "PL1", "PL2")
plasmidlosstb$strain = as.character( plasmidlosstb$strain )
str(plasmidlosstb)
LOHtb = read.table("021307.summary.by.strain.csv", sep="\t", header=T)
LOHtb$strain = as.character(LOHtb$strain)
LOHtb$PL1 = NA; LOHtb$PL2 = NA; LOHtb$PL=NA;
str(LOHtb[,1:10])
for( i in 1:length(LOHtb$strain) ){
mys = LOHtb$strain[i]
# mys ="M8"; #debug
x = grep(mys, plasmidlosstb$strain)
if( length(x)>0 ) {
tmptb = plasmidlosstb[x, ]
LOHtb$PL1[i] = mean(tmptb$PL1, na.rm=T)
LOHtb$PL2[i] = mean(tmptb$PL2, na.rm=T)
LOHtb$PL[i] = mean( c(tmptb$PL1, tmptb$PL2), na.rm=T )
}else {
print(paste(mys, " is not found in plasmid loss data"))
}
}
### all possible regression analysis for a given column
tb = LOHtb
pTb = 1: length(tb[1,])
names(pTb) = names(tb)
for( j in c(2:40) ) {
m = lm( tb[, j] ~ tb$PL)
#m = lm( tb[, j] ~ tb$PL1)
#m = lm( tb[, j] ~ tb$PL2)
sm = summary(m)
pTb[j] = 1 - pf(sm$fsta[1], sm$fsta[2], sm$fsta[3])
}
pTb[pTb<0.09]
#> pTb[pTb<0.05] #PL2
#<NA> Nloh <NA> PL1 PL2 PL
#NA 0.0111638902 NA 0.0022934461 0.0000000000 0.0001032832
#> pTb[pTb<0.09] #PL1
#<NA> Nloh b.min.sd TLmax.sd <NA> L0.small PL1 PL2 PL
#NA 3.138762e-02 8.707534e-02 5.595864e-02 NA 3.957404e-02 0.000000e+00 2.293446e-03 5.375196e-05
#> pTb[pTb<0.09]
#<NA> Nloh b.min.sd TLmax.sd <NA> L0.small PL1 PL2 PL
#NA 1.647319e-02 5.877810e-02 7.604223e-02 NA 6.414451e-02 5.375196e-05 1.032832e-04 0.000000e+00
m = lm(tb$L0.small ~ tb$PL1)
summary(m)
plot( tb$L0.small ~ tb$PL1, pch=18, xlim=c(2,7), ylim=c(0.05, 0.20))
abline(m, col='red')
text( tb$PL1, tb$L0.small, tb$strain)
text( 3, 0.1, "R2=0.69, p=0.04")
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