Setup and explore the data.

The column “OD” is the OD of the culture at the time when Biofilm was measured. This data serves to verify that low biofilm formation is not due to inhibited growth of the strain.

#IMPORT DATA
biofilm_full <- read_excel("data/Biofilm-longlist_allreplicates.xlsx") 
biofilm_full%>%
  group_by(Stamm, CC, Temperatur, Zeit, Durchgang ) %>%
  summarize(Biofilm=mean(Biofilm), OD=mean(OD)) -> biofilm_1

biofilm_1 %>%
  kable("html") %>%
  kable_styling(bootstrap_options=c("striped",
                                    "hover",
                                    "condensed",
                                    "responsive"))

#EXPLORE THE DATA
hist(biofilm_1$Biofilm)

hist(biofilm_1$OD)

plot(biofilm_1$Biofilm, as.factor(biofilm_1$CC))

plot(biofilm_1$Biofilm, biofilm_1$OD)  #es scheint dass die mit viel Biofilm wenig OD haben -> die Zellen sind im Biofilm

Visualize the data

ggplot(biofilm_1, aes(x=CC, y=Biofilm)) + 
  geom_boxplot()+
  xlab("CC")+
  ylab("Biofilm")+
  theme(axis.title.x = element_text(),
        axis.text.x  = element_text(angle=90, vjust=0.5, size=12))+
  scale_y_continuous()+ 
  facet_grid(Temperatur~.)

  ggplot(biofilm_1, aes(x=Stamm, y=Biofilm)) + 
    geom_boxplot()+
    xlab("CC")+
    ylab("Biofilm")+
    theme(axis.title.x = element_text(),
          axis.text.x  = element_text(angle=90, vjust=0.5, size=12))+
    scale_y_continuous()+ 
    facet_grid(Temperatur~.)

ggplot(biofilm_1, aes(x=CC, y=OD)) + 
  geom_boxplot()+
  xlab("CC")+
  ylab("OD")+
  theme(axis.title.x = element_text(),
        axis.text.x  = element_text(angle=90, vjust=0.5, size=12))+
  scale_y_continuous()+ 
  facet_grid(Temperatur~.)

ggplot(biofilm_1, aes(x=Stamm, y=OD)) + 
  geom_boxplot()+
  xlab("Stamm")+
  ylab("OD")+
  theme(axis.title.x = element_text(),
        axis.text.x  = element_text(angle=90, vjust=0.5, size=12))+
  scale_y_continuous()

Make a model for biofilm as a function of clonal complex

##BIOFILM AS A FUNCTION OF CC
#MAKE A MODEL: 
m_biofilm <- lmer(Biofilm ~ OD+Temperatur*CC + (1|Durchgang),
                  data=biofilm_1)

summary(m_biofilm)

## Linear mixed model fit by REML ['lmerMod']
## Formula: Biofilm ~ OD + Temperatur * CC + (1 | Durchgang)
##    Data: biofilm_1
## 
## REML criterion at convergence: -238.3
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.2808 -0.4844 -0.0225  0.4250  3.1608 
## 
## Random effects:
##  Groups    Name        Variance  Std.Dev. 
##  Durchgang (Intercept) 9.887e-20 3.144e-10
##  Residual              2.972e-03 5.452e-02
## Number of obs: 100, groups:  Durchgang, 3
## 
## Fixed effects:
##                            Estimate Std. Error t value
## (Intercept)               0.3096982  0.0241832  12.806
## OD                        0.0732746  0.0745073   0.983
## Temperatur8C             -0.1850841  0.0335879  -5.510
## CCCC204                   0.0001061  0.0315834   0.003
## CCCC6                    -0.0357083  0.0316813  -1.127
## CCCC9                     0.0228163  0.0246944   0.924
## CCHBF CC412              -0.0372934  0.0385531  -0.967
## CCLBF CC217              -0.1755851  0.0386771  -4.540
## Temperatur8C:CCCC204     -0.0027594  0.0447331  -0.062
## Temperatur8C:CCCC6        0.0223833  0.0446028   0.502
## Temperatur8C:CCCC9       -0.0214649  0.0349412  -0.614
## Temperatur8C:CCHBF CC412  0.0090646  0.0546345   0.166
## Temperatur8C:CCLBF CC217  0.1372317  0.0549389   2.498

## 
## Correlation matrix not shown by default, as p = 13 > 12.
## Use print(x, correlation=TRUE)  or
##   vcov(x)     if you need it

anova(m_biofilm)

## Analysis of Variance Table
##               Df  Sum Sq Mean Sq  F value
## OD             1 0.27114 0.27114  91.2252
## Temperatur     1 0.49838 0.49838 167.6805
## CC             5 0.08709 0.01742   5.8601
## Temperatur:CC  5 0.03469 0.00694   2.3341

#MODEL CHECKS
# Check normality of the residuals
hist(resid(m_biofilm))

qqnorm(resid(m_biofilm))
qqline(resid(m_biofilm))

plot(m_biofilm)

#DO CONTRASTS
lsm_biofilm <- lsmeans(m_biofilm,pairwise~CC|Temperatur,adjust="none")

## Loading required namespace: lmerTest

lsm_biofilm$contrasts %>%
  summary() %>%
  data.frame() %>%
  mutate(p.adj=p.adjust(p.value,method="holm")) -> lsm_df_biofilm_CC

lsm_biofilm <- lsmeans(m_biofilm,pairwise~Temperatur|CC,adjust="none")
lsm_biofilm$contrasts %>%
  summary() %>%
  data.frame() %>%
  mutate(p.adj=p.adjust(p.value,method="holm")) -> lsm_df_biofilm_Temp


#simple t-test to verify things
t.test(Biofilm ~ LBF, data = biofilm_full)

## 
##  Welch Two Sample t-test
## 
## data:  Biofilm by LBF
## t = 9.0488, df = 33.143, p-value = 1.787e-10
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  0.08915615 0.14086512
## sample estimates:
##  mean in group no mean in group yes 
##         0.2338440         0.1188333

t.test(Biofilm ~ LBF, data = subset(biofilm_full, biofilm_full$Temperatur== c("8C")))

## 
##  Welch Two Sample t-test
## 
## data:  Biofilm by LBF
## t = 3.8591, df = 9.182, p-value = 0.00371
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  0.01874544 0.07146733
## sample estimates:
##  mean in group no mean in group yes 
##        0.14243972        0.09733333

lsm_df_biofilm_CC %>%
  kable("html") %>%
  kable_styling(bootstrap_options=c("striped",
                                    "hover",
                                    "condensed",
                                    "responsive"))

lsm_df_biofilm_Temp %>%
  kable("html") %>%
  kable_styling(bootstrap_options=c("striped",
                                    "hover",
                                    "condensed",
                                    "responsive"))

make a model for biofilm as a function of “strain”

#-------------------------------------------------------------------
##BIOFILM AS A FUNCTION OF STRAIN
#MAKE A MODEL: 
m_biofilm_s <- lmer(Biofilm ~ OD+Temperatur*Stamm + (1|Durchgang),
                    data=biofilm_1)

summary(m_biofilm_s)

## Linear mixed model fit by REML ['lmerMod']
## Formula: Biofilm ~ OD + Temperatur * Stamm + (1 | Durchgang)
##    Data: biofilm_1
## 
## REML criterion at convergence: -161
## 
## Scaled residuals: 
##      Min       1Q   Median       3Q      Max 
## -2.48219 -0.40658 -0.08188  0.39509  2.72772 
## 
## Random effects:
##  Groups    Name        Variance  Std.Dev.
##  Durchgang (Intercept) 4.685e-05 0.006845
##  Residual              2.807e-03 0.052985
## Number of obs: 100, groups:  Durchgang, 3
## 
## Fixed effects:
##                                 Estimate Std. Error t value
## (Intercept)                     0.268195   0.032522   8.246
## OD                              0.108263   0.085670   1.264
## Temperatur8C                   -0.179849   0.044267  -4.063
## StammILS AS-0015                0.012105   0.043405   0.279
## StammILS AS-0016                0.024569   0.043268   0.568
## StammILS AS-0018               -0.033640   0.043305  -0.777
## StammILS AS-0021                0.028559   0.043291   0.660
## StammILS AS-0054                0.043332   0.043616   0.994
## StammILS AS-0079                0.116530   0.043269   2.693
## StammILS AS-0080               -0.042406   0.043304  -0.979
## StammILS AS-0101                0.106545   0.048423   2.200
## StammILS AS-0105                0.095146   0.043270   2.199
## StammILS AS-0108                0.032965   0.043908   0.751
## StammILS AS1-0010               0.078528   0.043279   1.814
## StammILS AS1-0011               0.078720   0.043265   1.819
## StammILS AS1-0012               0.043698   0.043272   1.010
## StammILS AS1-0013               0.053911   0.043275   1.246
## StammILS AS1-0014               0.063531   0.043262   1.469
## StammLBF                       -0.137052   0.043369  -3.160
## Temperatur8C:StammILS AS-0015   0.007752   0.061187   0.127
## Temperatur8C:StammILS AS-0016  -0.018979   0.062038  -0.306
## Temperatur8C:StammILS AS-0018   0.044099   0.061274   0.720
## Temperatur8C:StammILS AS-0021  -0.011453   0.061574  -0.186
## Temperatur8C:StammILS AS-0054  -0.011390   0.061384  -0.186
## Temperatur8C:StammILS AS-0079  -0.094421   0.061583  -1.533
## Temperatur8C:StammILS AS-0080   0.072941   0.061194   1.192
## Temperatur8C:StammILS AS-0101  -0.034084   0.068666  -0.496
## Temperatur8C:StammILS AS-0105  -0.073660   0.061490  -1.198
## Temperatur8C:StammILS AS-0108  -0.018165   0.061204  -0.297
## Temperatur8C:StammILS AS1-0010 -0.046551   0.061605  -0.756
## Temperatur8C:StammILS AS1-0011 -0.032488   0.061244  -0.530
## Temperatur8C:StammILS AS1-0012 -0.016862   0.061783  -0.273
## Temperatur8C:StammILS AS1-0013 -0.030352   0.061284  -0.495
## Temperatur8C:StammILS AS1-0014 -0.050266   0.061486  -0.818
## Temperatur8C:StammLBF           0.129679   0.061294   2.116

## 
## Correlation matrix not shown by default, as p = 35 > 12.
## Use print(x, correlation=TRUE)  or
##   vcov(x)     if you need it

anova(m_biofilm_s)

## Analysis of Variance Table
##                  Df  Sum Sq Mean Sq  F value
## OD                1 0.28308 0.28308 100.8327
## Temperatur        1 0.48876 0.48876 174.0935
## Stamm            16 0.13012 0.00813   2.8967
## Temperatur:Stamm 16 0.06509 0.00407   1.4491

#MODEL CHECKS
# Check normality of the residuals
hist(resid(m_biofilm_s))

qqnorm(resid(m_biofilm_s))
qqline(resid(m_biofilm_s))

plot(m_biofilm_s)

#DO CONTRASTS
lsm_biofilm_s <- lsmeans(m_biofilm_s,pairwise~Stamm|Temperatur,adjust="none")
lsm_biofilm_s$contrasts %>%
  summary() %>%
  data.frame() %>%
  group_by(Temperatur) %>%
  mutate(p.adj=p.adjust(p.value,method="holm")) -> lsm_df_biofilm_s

lsm_df_biofilm_s %>%
  kable("html") %>%
  kable_styling(bootstrap_options=c("striped",
                                    "hover",
                                    "condensed",
                                    "responsive"))

Setup and explore the data

#BC data 
bc <- read_excel("data/171018BC_longlist_ohnejule.xlsx")
# Make readout an ordered factor
bc$readout <- factor(bc$readout, ordered=T)
bc$clonal.complex<-factor(bc$clonal.complex)
bc$serotype <- factor(bc$serotype)

bc %>%
  kable("html") %>%
  kable_styling(bootstrap_options=c("striped",
                                    "hover",
                                    "condensed",
                                    "responsive"))