Anova( Tukey HSD)
Anika
2023-01-24
#set working directory
getwd()## [1] "C:/Users/ASUS/Downloads"setwd("C:/Users/ASUS/Downloads")#library call
library(readr)
library(ggplot2)## Warning: package 'ggplot2' was built under R version 4.2.2library(multcompView)## Warning: package 'multcompView' was built under R version 4.2.2library(rlang)
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## intersect, setdiff, setequal, unionlibrary(readxl)
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data<- read.csv("qPCR_data.csv")
data## Condition SOS1 MYB32 OsIAA4 OsIAA11
## 1 paired salt 6.6500415 2.2338509 4.2057982 15.2615752
## 2 paired salt 1.3188965 3.8550564 7.3496872 40.9389073
## 3 paired salt 3.9844690 0.6126454 1.0619092 15.2615752
## 4 paired_control 2.2419187 0.8729655 0.4223344 0.7044219
## 5 paired_control 2.1355190 0.3819304 0.2468449 1.0212733
## 6 paired_control 2.1887188 1.3640006 0.5978240 0.3875704
## 7 unpair_control 1.0491444 1.2028127 1.9065266 1.1369593
## 8 unpair_control 0.9541699 0.5364505 0.2925789 0.5965396
## 9 unpair_control 1.0016572 1.8691749 3.5204743 1.6773790
## 10 unpaired_salt 1.9990537 1.4142991 1.4588538 1.0998857
## 11 unpaired_salt 3.0000000 2.4707663 2.5180016 0.6443767
## 12 unpaired_salt 0.9981075 0.3578319 0.3997061 1.5553947#anova test
fit <- aov(SOS1 ~ Condition, data=data)
summary(fit)## Df Sum Sq Mean Sq F value Pr(>F)
## Condition 3 13.88 4.626 2.281 0.156
## Residuals 8 16.23 2.028#install.packages("agricolae")
#library(agricolae)
#duncan.test(fit, data$Condition, alpha = 1)#tukey
tukey<-TukeyHSD(fit, conf.level = 0.95)
tukey## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = SOS1 ~ Condition, data = data)
##
## $Condition
## diff lwr upr p adj
## paired_control-paired salt -1.7957502 -5.519361 1.9278604 0.4574449
## unpair_control-paired salt -2.9828118 -6.706422 0.7407987 0.1227217
## unpaired_salt-paired salt -1.9854153 -5.709026 1.7381953 0.3796718
## unpair_control-paired_control -1.1870617 -4.910672 2.5365489 0.7426660
## unpaired_salt-paired_control -0.1896651 -3.913276 3.5339454 0.9983233
## unpaired_salt-unpair_control 0.9973966 -2.726214 4.7210071 0.8259458# compact letter display
cld <- multcompLetters4(fit, tukey)
print(cld)## $Condition
## $Condition$Letters
## paired salt paired_control unpaired_salt unpair_control
## "a" "a" "a" "a"
##
## $Condition$LetterMatrix
## a
## paired salt TRUE
## paired_control TRUE
## unpaired_salt TRUE
## unpair_control TRUEdata_summary <- group_by(data, Condition) %>%
summarise(mean=mean(SOS1), quant = quantile(SOS1, probs = 0.75)) %>%
arrange(desc(mean))
print(data_summary)## # A tibble: 4 × 3
## Condition mean quant
## <chr> <dbl> <dbl>
## 1 paired salt 3.98 5.32
## 2 paired_control 2.19 2.22
## 3 unpaired_salt 2.00 2.50
## 4 unpair_control 1.00 1.03add_cld <- as.data.frame.list(cld$Condition)
data_summary$Tukey <- add_cld$Letters
print(data_summary)## # A tibble: 4 × 4
## Condition mean quant Tukey
## <chr> <dbl> <dbl> <chr>
## 1 paired salt 3.98 5.32 a
## 2 paired_control 2.19 2.22 a
## 3 unpaired_salt 2.00 2.50 a
## 4 unpair_control 1.00 1.03 apersonal_theme = theme(plot.title =
element_text(hjust = 0.5))# boxplot
ggplot(data,aes(Condition, SOS1, fill = Condition))+
geom_boxplot()+
labs(title= "Relative expression of SOS1",x="Condition", y="SOS1 Expression") +
theme_bw() +
theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank()) +
geom_text(data = data_summary, aes(y=quant, label = Tukey), position = position_dodge(0.90), size = 3, vjust=-1.6, hjust =-0.7)
fit <- aov(MYB32
~ Condition, data=data)
summary(fit)## Df Sum Sq Mean Sq F value Pr(>F)
## Condition 3 3.025 1.008 0.911 0.478
## Residuals 8 8.859 1.107tukey<-TukeyHSD(fit, conf.level = 0.95)
tukey## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = MYB32 ~ Condition, data = data)
##
## $Condition
## diff lwr upr p adj
## paired_control-paired salt -1.3608854 -4.112420 1.390649 0.4378168
## unpair_control-paired salt -1.0310382 -3.782572 1.720496 0.6436550
## unpaired_salt-paired salt -0.8195518 -3.571086 1.931983 0.7780828
## unpair_control-paired_control 0.3298472 -2.421687 3.081381 0.9794375
## unpaired_salt-paired_control 0.5413336 -2.210201 3.292868 0.9194852
## unpaired_salt-unpair_control 0.2114864 -2.540048 2.963021 0.9943379#installed.packages("multcompView")
library(multcompView)cld <- multcompLetters4(fit, tukey)
print(cld)## $Condition
## $Condition$Letters
## paired salt unpaired_salt unpair_control paired_control
## "a" "a" "a" "a"
##
## $Condition$LetterMatrix
## a
## paired salt TRUE
## unpaired_salt TRUE
## unpair_control TRUE
## paired_control TRUEdata_summary <- group_by(data, Condition) %>%
summarise(mean=mean(MYB32
), quant = quantile(MYB32
, probs = 0.75)) %>%
arrange(desc(mean))
print(data_summary)## # A tibble: 4 × 3
## Condition mean quant
## <chr> <dbl> <dbl>
## 1 paired salt 2.23 3.04
## 2 unpaired_salt 1.41 1.94
## 3 unpair_control 1.20 1.54
## 4 paired_control 0.873 1.12add_cld <- as.data.frame.list(cld$Condition)
data_summary$Tukey <- add_cld$Letters
print(data_summary)## # A tibble: 4 × 4
## Condition mean quant Tukey
## <chr> <dbl> <dbl> <chr>
## 1 paired salt 2.23 3.04 a
## 2 unpaired_salt 1.41 1.94 a
## 3 unpair_control 1.20 1.54 a
## 4 paired_control 0.873 1.12 apersonal_theme = theme(plot.title =
element_text(hjust = 0.5))# boxplot
ggplot(data,aes(Condition, MYB32
, fill = Condition))+
geom_boxplot()+
labs(title= "Relative expression of MYB32
",x="Condition", y="MYB32
Expression") +
theme_bw() +
theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank()) +
geom_text(data = data_summary, aes(y=quant, label = Tukey), position = position_dodge(0.90), size = 3, vjust=-1.6, hjust =-0.7)
fit <- aov(OsIAA4
~ Condition, data=data)
summary(fit)## Df Sum Sq Mean Sq F value Pr(>F)
## Condition 3 22.97 7.656 2.245 0.16
## Residuals 8 27.28 3.410tukey<-TukeyHSD(fit, conf.level = 0.95)
tukey## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = OsIAA4 ~ Condition, data = data)
##
## $Condition
## diff lwr upr p adj
## paired_control-paired salt -3.7834637 -8.612092 1.045165 0.1327063
## unpair_control-paired salt -2.2992716 -7.127900 2.529357 0.4673053
## unpaired_salt-paired salt -2.7469443 -7.575573 2.081684 0.3307328
## unpair_control-paired_control 1.4841922 -3.344436 6.312821 0.7621502
## unpaired_salt-paired_control 1.0365194 -3.792109 5.865148 0.8990620
## unpaired_salt-unpair_control -0.4476728 -5.276301 4.380956 0.9902008#installed.packages("multcompView")
library(multcompView)cld <- multcompLetters4(fit, tukey)
print(cld)## $Condition
## $Condition$Letters
## paired salt unpair_control unpaired_salt paired_control
## "a" "a" "a" "a"
##
## $Condition$LetterMatrix
## a
## paired salt TRUE
## unpair_control TRUE
## unpaired_salt TRUE
## paired_control TRUEdata_summary <- group_by(data, Condition) %>%
summarise(mean=mean(OsIAA4
), quant = quantile(OsIAA4
, probs = 0.75)) %>%
arrange(desc(mean))
print(data_summary)## # A tibble: 4 × 3
## Condition mean quant
## <chr> <dbl> <dbl>
## 1 paired salt 4.21 5.78
## 2 unpair_control 1.91 2.71
## 3 unpaired_salt 1.46 1.99
## 4 paired_control 0.422 0.510add_cld <- as.data.frame.list(cld$Condition)
data_summary$Tukey <- add_cld$Letters
print(data_summary)## # A tibble: 4 × 4
## Condition mean quant Tukey
## <chr> <dbl> <dbl> <chr>
## 1 paired salt 4.21 5.78 a
## 2 unpair_control 1.91 2.71 a
## 3 unpaired_salt 1.46 1.99 a
## 4 paired_control 0.422 0.510 apersonal_theme = theme(plot.title =
element_text(hjust = 0.5))# boxplot of OsIAA4 gene
ggplot(data,aes(Condition, OsIAA4
, fill = Condition))+
geom_boxplot()+
labs(title= "Relative expression of OsIAA4
",x="Condition", y="OsIAA4
Expression") +
theme_bw() +
theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank()) +
geom_text(data = data_summary, aes(y=quant, label = Tukey), position = position_dodge(0.90), size = 3, vjust=-1.6, hjust =-0.7)
fit <- aov(OsIAA11
~ Condition, data=data)
summary(fit)## Df Sum Sq Mean Sq F value Pr(>F)
## Condition 3 1174.1 391.4 7.104 0.0121 *
## Residuals 8 440.8 55.1
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1tukey<-TukeyHSD(fit, conf.level = 0.95)
tukey## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = OsIAA11 ~ Condition, data = data)
##
## $Condition
## diff lwr upr p adj
## paired_control-paired salt -23.11626403 -42.52399 -3.708537 0.0214024
## unpair_control-paired salt -22.68372657 -42.09145 -3.276000 0.0235908
## unpaired_salt-paired salt -22.72080022 -42.12853 -3.313073 0.0233943
## unpair_control-paired_control 0.43253746 -18.97519 19.840264 0.9998580
## unpaired_salt-paired_control 0.39546381 -19.01226 19.803191 0.9998914
## unpaired_salt-unpair_control -0.03707364 -19.44480 19.370653 0.9999999#installed.packages("multcompView")
library(multcompView)cld <- multcompLetters4(fit, tukey)
print(cld)## $Condition
## paired salt unpair_control unpaired_salt paired_control
## "a" "b" "b" "b"data_summary <- group_by(data, Condition) %>%
summarise(mean=mean(OsIAA11
), quant = quantile(OsIAA11
, probs = 0.75)) %>%
arrange(desc(mean))
print(data_summary)## # A tibble: 4 × 3
## Condition mean quant
## <chr> <dbl> <dbl>
## 1 paired salt 23.8 28.1
## 2 unpair_control 1.14 1.41
## 3 unpaired_salt 1.10 1.33
## 4 paired_control 0.704 0.863add_cld <- as.data.frame.list(cld$Condition)
data_summary$Tukey <- add_cld$Letters
print(data_summary)## # A tibble: 4 × 4
## Condition mean quant Tukey
## <chr> <dbl> <dbl> <chr>
## 1 paired salt 23.8 28.1 a
## 2 unpair_control 1.14 1.41 b
## 3 unpaired_salt 1.10 1.33 b
## 4 paired_control 0.704 0.863 bpersonal_theme = theme(plot.title =
element_text(hjust = 0.5))# boxplot
ggplot(data,aes(Condition, OsIAA11
, fill = Condition))+
geom_boxplot()+
labs(title= "Relative expression of OsIAA11
",x="Condition", y="OsIAA11
Expression") +
theme_bw() +
theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank()) +
geom_text(data = data_summary, aes(y=quant, label = Tukey), position = position_dodge(0.90), size = 3, vjust=-1.6, hjust =-0.7)
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