Análisis Genotipo de Hojas
Jayerlin Rodriguez
4/10/2021
Ejercicio- Analisis Genotipo de área foliar
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## arrange, mutate, rename, summariseLeafLength = read_excel("~/2021-II/Bioestadistica/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)Gráfico de series de tiempo por tratamiento Temperatura-CO2
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
Lo arrojado por el gráfico nos permite inferir que las plantas con el genotipo PA107 no altera el área foliar de sus hojas según las condiciones ambientales dominantes de ninguno de los escenarios, mientras que el genotipo SCA6 es si presenta alteraciones teniendo una disminución de area foliar para temperaturas más altas como de 27 a 36° C. Respecto a los niveles de Co2 no parece tener relación alguna con el crecimiento del area foliar.
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 ' ' 1Se corrobora lo dicho anteriormente pues notamos diferencias significativas para la variable de Temperatura respecto al Genotipo, la variable CO2 no muestra relación con respecto a ninguna de las variables pues esta no tiene diferencias significativas.
Post-anova CO2
bxp = ggboxplot(LeafLength, x = "Time", y = "LeafL",
color = "CO2", palette = "jco")
bxp
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))
Para confirmar la ausencia de relación de CO2 con el crecimiento del area foliar se realizó una correccion de Bonferroni con el fin de que las comparaciones multiples realizadas no interfieran con el resultado y el analisis. Aquí podemos ver que existe solo un par de valores significativos donde el nivel de CO2 de 700 tiene mayor significancia en el crecimiento foliar, sin embargo, estos datos son muy limitados, descartando la posibilidad de implicación de los niveles de CO2 realmente en el crecimiento.
Postanova Temperatura
bxp = ggboxplot(LeafLength, x = "Time", y = "LeafL",
color = "Temp", palette = "jco")
bxp
pwc = LeafLength %>%
group_by(Time) %>%
pairwise_t_test(LeafL ~ Temp, paired = TRUE,
p.adjust.method = "bonferroni")
pwc## # A tibble: 57 x 11
## Time .y. group1 group2 n1 n2 statistic df p p.adj
## * <fct> <chr> <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <dbl>
## 1 10 LeafL 31_22 33.5_24.5 24 24 -5.14 23 0.0000333 9.99e-5
## 2 10 LeafL 31_22 36_27 24 24 -2.95 23 0.007 2.2 e-2
## 3 10 LeafL 33.5_24.5 36_27 24 24 1.40 23 0.176 5.28e-1
## 4 13 LeafL 31_22 33.5_24.5 24 24 -4.00 23 0.00056 2 e-3
## 5 13 LeafL 31_22 36_27 24 24 -1.08 23 0.291 8.73e-1
## 6 13 LeafL 33.5_24.5 36_27 24 24 1.57 23 0.129 3.87e-1
## 7 16 LeafL 31_22 33.5_24.5 24 24 -1.04 23 0.307 9.21e-1
## 8 16 LeafL 31_22 36_27 24 24 1.45 23 0.161 4.83e-1
## 9 16 LeafL 33.5_24.5 36_27 24 24 3.31 23 0.003 9 e-3
## 10 19 LeafL 31_22 33.5_24.5 24 24 0.421 23 0.678 1 e+0
## # ... with 47 more rows, and 1 more variable: p.adj.signif <chr>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))
El ajuste de Bonferroni para la temperatura confirma las suposisciones realizadas sobre su influencia en el crec del area foliar, siendo el rango de temperatura más optima el de 31_22 y la menos optima la de 36_27.
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))
Para finalizar, se realizó un análisis de como la temperatura influye sobre cada genotipo, se encontró que efectivamente el genotipo PA107 no presenta cambios significativos determinados por las condiciones del ambiente; por su parte, el genotipo SCA6 tiene fluctuaciones en el crecimiento del area de sus hojas de acuerdo a la temperatura a la que sea sometido.