UVR consequences on polyp reproduction

Importing and tidying asexual reproduction data

  1. Importing data.
# data where all the controls are combined into one group 
df.combinedcontrol <- read.csv(here::here("Budding Analysis/polypclonedata_combinedcontrol.csv"), stringsAsFactors = F)
  1. Add column with final polyp numbers
df.combinedcontrol$FinalPolyp <- df.combinedcontrol$Day_27
  1. Calculate daily and weekly budding rates
# daily budding rate for experiment
df.combinedcontrol$DBR <- ((df.combinedcontrol$FinalPolyp - df.combinedcontrol$Day_00)/27)
# weekly budding rate for experiment
df.combinedcontrol$WBR <- (((df.combinedcontrol$FinalPolyp - df.combinedcontrol$Day_00)/27)*7)
  1. Reorganize/Reformat the data frame
# long format 
df.combinedcontrol.gather <- df.combinedcontrol %>% gather(key = "Time", value = "Polyps", Day_00:Day_27)

# separate the days of experiment out and only keep the number of the day
df <- separate(data = df.combinedcontrol.gather, col = Time, into = c(NA,'Day'), sep = '_')

# reformat number of day to integer
df$Day <- as.integer(df$Day)

knitr::kable(head(df))
Treatment JarID DishID FinalPolyp DBR WBR Day Polyps
Control C1 D1 11 0.3703704 2.5925926 0 1
Control C1 D2 4 0.1111111 0.7777778 0 1
Control C1 D3 8 0.2592593 1.8148148 0 1
Control C2 D4 9 0.2962963 2.0740741 0 1
Control C2 D5 10 0.3333333 2.3333333 0 1
Control C2 D6 7 0.2222222 1.5555556 0 1

Data analysis

We compared differences in weekly budding rate among the UVR treatment groups using a one-way ANOVA with treatment as the main effect.

wbraov <- aov(WBR~Treatment, data = df.combinedcontrol)

summary(wbraov)
##             Df Sum Sq Mean Sq F value   Pr(>F)    
## Treatment    3  23.43   7.809   10.94 1.23e-05 ***
## Residuals   50  35.69   0.714                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

A post-hoc Tukey test was used when significant differences were detected.

TukeyHSD(wbraov)
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = WBR ~ Treatment, data = df.combinedcontrol)
## 
## $Treatment
##                  diff          lwr         upr     p adj
## AB-AA      -1.8580247 -2.774636818 -0.94141256 0.0000113
## BB-AA      -0.9506173 -1.867229411 -0.03400516 0.0393725
## Control-AA -0.4609053 -1.297653914  0.37584321 0.4665907
## BB-AB       0.9074074 -0.009204719  1.82401953 0.0532755
## Control-AB  1.3971193  0.560370778  2.23386791 0.0002857
## Control-BB  0.4897119 -0.347036630  1.32646050 0.4130403

UVR consequences on polyp health

Creating table of detachment data

Polyp detachment was classified binomially as either yes (polyps in dish detached at > 1 timepoints) or no (polyps in dish detached at ≤ 1 timepoints).

D <- as.table(rbind(c(11,7), c(11,1), c(4,8), c(0,12)))

dimnames(D) <- list(treatment = c("Control", "AA", "BB", "AB"), status = c("Attached", "Detached"))

knitr::kable(D)
Attached Detached
Control 11 7
AA 11 1
BB 4 8
AB 0 12

Data analysis

Chi-squared test of independence to test whether there were non-random differences in detachment probabilities across the treatment groups.

library(chisq.posthoc.test)

chisq.test(D, correct = F)
## 
##  Pearson's Chi-squared test
## 
## data:  D
## X-squared = 22.512, df = 3, p-value = 5.103e-05

Post-Hoc Comparisons

6 comparisons so alpha is 0.05/6 = 0.0083
Comparison is bolded if p value is significant

Control vs AA

chisq.test(D[c(1,2),], correct = F)
## 
##  Pearson's Chi-squared test
## 
## data:  D[c(1, 2), ]
## X-squared = 3.4375, df = 1, p-value = 0.06373

Control vs BB

chisq.test(D[c(1,3),], correct = F)
## 
##  Pearson's Chi-squared test
## 
## data:  D[c(1, 3), ]
## X-squared = 2.2222, df = 1, p-value = 0.136

Control vs AB

chisq.test(D[c(1,4),], correct = F)
## 
##  Pearson's Chi-squared test
## 
## data:  D[c(1, 4), ]
## X-squared = 11.579, df = 1, p-value = 0.000667

AA vs BB

chisq.test(D[c(2,3),], correct = F)
## 
##  Pearson's Chi-squared test
## 
## data:  D[c(2, 3), ]
## X-squared = 8.7111, df = 1, p-value = 0.003163

AA vs AB

chisq.test(D[c(2,4),], correct = F)
## 
##  Pearson's Chi-squared test
## 
## data:  D[c(2, 4), ]
## X-squared = 20.308, df = 1, p-value = 6.593e-06

BB vs AB

chisq.test(D[c(3,4),], correct = F)
## 
##  Pearson's Chi-squared test
## 
## data:  D[c(3, 4), ]
## X-squared = 4.8, df = 1, p-value = 0.02846

```

Figures

Fig 1A. Total number of polyps observed (mean +/- s.e.) under each UV treatment over the 27-day experiment.

Fig 1B. Weekly budding rates (polyps/week) for each UV treatment. Lowercase letters (a, b, c) indicate signficant differences (p < 0.5) between groups.