Análisis foliar hojas (genotipo)
Diana Jurado
4/10/2021
##Just Run This Chunk
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## arrange, mutate, rename, summariseLeafLength = read_excel("C:/Users/diana/Downloads/YDRAY-LeafLength.xlsx")
LeafLength$Temp_C02=paste(LeafLength$Temp,"_and_",LeafLength$CO2,sep="")
LeafLength$id=as.factor(paste(LeafLength$Cab,"_",LeafLength$Obs,sep=""))
LeafLength$Genotype=as.factor(LeafLength$Genotype)##Time Series Plot by Temp-CO2 Treatment
gd = summarySE(LeafLength, measurevar="LeafL", groupvars=c("Temp_C02","Time","Genotype"))g1=ggplot(gd,aes(x=Time,y=LeafL,color=Temp_C02, group =Temp_C02))+ geom_line(data=gd)+geom_point(data=gd, size = 2)+ facet_grid(~Genotype)+theme_bw()+
labs(title = "Leaf Length",
x = "Time in days",
y = "Length in cm")
g1
Análisis: En el genotipo PA107 las variables de temperatura y CO2 no representan una gran variación en cuanto al crecimiento de la hoja. En SCA6 se evidencia que el crecimiento depende de las variables de rango de temperatura y CO2, en 60 días las hojas que se encontraban de 31 y 22 a 700 de concentración de CO2 tiene medidas mayores a 30cm, mientras que al mismo tiempo (60 días) las hojas que estaban expuestas de 36 a 27 a 700 de concentración de CO2 tiene un crecimiento de 18cm. Esto indica que en el rango de 31 y 22 a 700 de concentración de CO2 es más eficiete para la obtención de un crecimiento mayor en la parte foliar de las hojas.
##Anova de Medidas Repetidas
LeafLength$Time=as.factor(LeafLength$Time)
LeafLength$Temp=as.factor(LeafLength$Temp)
LeafLength$CO2=as.factor(LeafLength$CO2)
anova=aov(LeafL~Temp*CO2*Genotype+Time+Error(id/Genotype/Time),data=LeafLength)
summary(anova)##
## Error: id
## Df Sum Sq Mean Sq F value Pr(>F)
## Temp 2 6614 3307 17.188 1.06e-05 ***
## CO2 1 214 214 1.113 0.30
## Temp:CO2 2 396 198 1.028 0.37
## Residuals 30 5772 192
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Error: id:Genotype
## Df Sum Sq Mean Sq F value Pr(>F)
## Genotype 1 871 871 2.016 0.16598
## Temp:Genotype 2 7270 3635 8.409 0.00126 **
## CO2:Genotype 1 46 46 0.108 0.74522
## Temp:CO2:Genotype 2 2673 1337 3.092 0.06013 .
## Residuals 30 12968 432
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Error: id:Genotype:Time
## Df Sum Sq Mean Sq F value Pr(>F)
## Time 18 17707 983.7 140.2 <2e-16 ***
## Residuals 1278 8964 7.0
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##Postanova CO2
bxp = ggboxplot(
LeafLength, x = "Time", y = "LeafL",
color = "CO2", palette = "jco"
)pwc = LeafLength %>%
group_by(Time) %>%
pairwise_t_test(
LeafL ~ CO2, paired = TRUE,
p.adjust.method = "bonferroni"
)pwc = pwc %>% add_xy_position(x = "Time")
bxp +
stat_pvalue_manual(pwc, tip.length = 0, hide.ns = TRUE) +
labs(
subtitle = paste("Anova, ","F(1)", " = ", "1.113, ", "p", " = ", "0.30"),
caption = get_pwc_label(pwc)
)
Análisis: Respecto al crecimiento: se observa que en concetración de 700 de CO2, la planta tiene un mayor crecimiento respecto a una concentración de 400 de CO2, es decir que a mayor concentración de CO2, la hoja tiene un mayor crecimiento, debido a que es una molécula que requiere el organismo para obtener una mayor longitud en cuanto a hojas, tallo e incluso raíz. De la misma manera, el gráfico indica que los puntos de mayor crecimiento de la hoja son el los primeros días porque es donde la hoja puede tener una mayor area para recibir los nutrientes que se necesitan para funciones biológicas del organismo, posteriormente, el área foliar ya no presenta un crecimiento significativo porque ya no tiene la necesidad de crecer en tamaño porque la energía que se requiere está direccionada a otros procesos biológicos.
##POstanova Temp
bxp = ggboxplot(
LeafLength, x = "Time", y = "LeafL",
color = "Temp", palette = "jco"
)pwc = LeafLength %>%
group_by(Time) %>%
pairwise_t_test(
LeafL ~ Temp, paired = TRUE,
p.adjust.method = "bonferroni"
)pwc = pwc %>% add_xy_position(x = "Time")
bxp +
stat_pvalue_manual(pwc, tip.length = 0, hide.ns = TRUE) +
labs(
subtitle = paste("Anova, ","F(2)", " = ", "17.188 , ", "p", " = ", "<0.0001 "),
caption = get_pwc_label(pwc)
)
Análisis: Respecto a la temperatura y el crecimiento, se observa que cada punto es significativo, eso quiere decir que la variable temperatura esta muy ligada al crecimiento en el área foliar de la planta cuando esta se encuentra en un entorno de 31 a 32.
##Postanova Temp-Genotipo
bxp = ggboxplot(
LeafLength, x = "Time", y = "LeafL",
title= "Leaf Length",
xlab ="time (days)", ylab= "Length (cm)",
color = "Temp", palette = "lancet",
facet.by = "Genotype"
)
pwc = LeafLength %>%
group_by(Time, Genotype) %>%
pairwise_t_test(
LeafL ~ Temp, paired = TRUE,
p.adjust.method = "bonferroni"
)
pwc = pwc %>% add_xy_position(x = "Time")
bxp +
stat_pvalue_manual(pwc, tip.length = 0, hide.ns = TRUE) +
labs(
subtitle = paste("Anova, ","F(2)", " = ", "8.409 , ", "p", " = ", "0.00126"),
caption = get_pwc_label(pwc)
)