#Análisis estadísticos de tesis de Maestría en Ciencias Bioquímicas:
#Potencial inhibición de la senescencia celular en fibroblastos de ratón con expresión heteróloga de un inhibidor de cinasa dependiente de ciclina de ratopín.
#Laura Marianna Cano Mateo
setwd("C:/Laura Cano/Maestría/Tesis/Fotos/AnalisisR")
#Librerías
library(ggplot2)
library(ggthemes)
## Warning: package 'ggthemes' was built under R version 4.1.3
library(showtext)
## Warning: package 'showtext' was built under R version 4.1.3
## Loading required package: sysfonts
## Warning: package 'sysfonts' was built under R version 4.1.3
## Loading required package: showtextdb
## Warning: package 'showtextdb' was built under R version 4.1.3
# Cargar datos MSFs y MEFs 3 y 5 DIV
Datos<-read.csv("Dano3y5DIV.csv", header=TRUE)
#Bgal
windows()
ggplot(data = Datos, aes(x = factor(Tipo_celular), y = Bgal, fill=factor(Tratamiento))) +
scale_fill_manual(values=c("#9E9E9E", "#009688")) +
geom_boxplot(outlier.color="black") +
facet_wrap(~DIV) +
geom_jitter(color="#222424", size=1, alpha=1) +
labs(title="Células con Actividad SA-Bgalactosidasa",
x="", y="% Células Bgal+") +
theme_few()+
labs(fill="Tratamiento")
#Células por cm2
#4pixeles/um 2560pixelesx1920pixeles o 640umx480um 307200um2 *325.521x(células por campo con objetivo 20x)
windows()
ggplot(data = Datos, aes(x = factor(Tipo_celular), y = Nucleoscm, fill=factor(Tratamiento))) +
scale_fill_manual(values=c("#9E9E9E", "#009688")) +
geom_boxplot(outlier.color="black") +
facet_wrap(~DIV) +
geom_jitter(color="#222424", size=1, alpha=1) +
labs(title="Densidad Celular",
x="", y=expression(paste("No. promedio de células por ", cm^{2}))) +
theme_few()+
labs(fill="Tratamiento")
#Apoptóticos
windows()
ggplot(data = Datos, aes(x = factor(Tipo_celular), y = Apoptoticos, fill=factor(Tratamiento))) +
scale_fill_manual(values=c("#9E9E9E", "#009688")) +
geom_boxplot(outlier.color="black") +
facet_wrap(~DIV) +
geom_jitter(color="#222424", size=1, alpha=1) +
labs(title="Subletalidad de tratamiento",
x="", y="% Núcleos apoptóticos") +
theme_few()+
labs(fill="Tratamiento")
#Deformes
windows()
ggplot(data = Datos, aes(x = factor(Tipo_celular), y = Deformes, fill=factor(Tratamiento))) +
scale_fill_manual(values=c("#9E9E9E", "#009688")) +
geom_boxplot(outlier.color="black") +
facet_wrap(~DIV) +
geom_jitter(color="#222424", size=1, alpha=1) +
labs(title="Anormalidades en Morfología Nuclear",
x="", y="% Núcleos deformes") +
theme_few()+
labs(fill="Tratamiento")
# Cargar datos NSFs y MSFs
DatosNM<-read.csv("NSF-MSF_2vias02_10_22.csv", header=TRUE)
#Bgal
windows()
ggplot(data = DatosNM, aes(x = factor(Plasmido), y = Bgal, fill=factor(Tratamiento))) +
scale_fill_manual(values=c("#9E9E9E", "#009688")) +
geom_boxplot(outlier.color="black") +
facet_wrap(~Tratamiento) +
geom_jitter(color="#222424", size=1, alpha=1) +
labs(title="Células con Actividad SA-Bgalactosidasa",
x="", y="% Células Bgal+") +
theme_few()+
labs(fill="Tratamiento")
#Células por cm2
#4pixeles/um 2560pixelesx1920pixeles o 640umx480um 307200um2 *325.521x(células por campo con objetivo 20x)
windows()
ggplot(data = DatosNM, aes(x = factor(Plasmido), y = Nucleoscm, fill=factor(Tratamiento))) +
scale_fill_manual(values=c("#9E9E9E", "#009688")) +
geom_boxplot(outlier.color="black") +
facet_wrap(~Tratamiento) +
geom_jitter(color="#222424", size=1, alpha=1) +
labs(title="Densidad Celular",
x="", y=expression(paste("No. promedio de células por ", cm^{2}))) +
theme_few()+
labs(fill="Tratamiento")
#Apoptóticos
windows()
ggplot(data = DatosNM, aes(x = factor(Plasmido), y = Apoptoticos, fill=factor(Tratamiento))) +
scale_fill_manual(values=c("#9E9E9E", "#009688")) +
geom_boxplot(outlier.color="black") +
facet_wrap(~Tratamiento) +
geom_jitter(color="#222424", size=1, alpha=1) +
labs(title="Subletalidad de tratamiento",
x="", y="% Núcleos apoptóticos") +
theme_few()+
labs(fill="Tratamiento")
#Bgal
#Prueba de normalidad
windows()
hist(DatosNM$Bgal, main = "Distribución de datos células con actividad SA-Bgal",
ylab = "Frecuencia", xlab = "% Células Bgal +", col = "lightblue")
shapiro.test(lm(Bgal~Muestra, DatosNM)$residuals)
##
## Shapiro-Wilk normality test
##
## data: lm(Bgal ~ Muestra, DatosNM)$residuals
## W = 0.90703, p-value = 0.1954
#Los datos se distribuyen normalmente
#Homoscedasticidad
bartlett.test(Bgal~Muestra, DatosNM)
##
## Bartlett test of homogeneity of variances
##
## data: Bgal by Muestra
## Bartlett's K-squared = 0.53266, df = 3, p-value = 0.9117
#Hay homoscedasticidad de varianza
#Dos vías con interacción
anovaNMBgal<-aov(Bgal ~ Plasmido * Tratamiento, DatosNM)
summary(anovaNMBgal)
## Df Sum Sq Mean Sq F value Pr(>F)
## Plasmido 1 862.6 862.6 309.4 1.11e-07 ***
## Tratamiento 1 1333.5 1333.5 478.3 2.02e-08 ***
## Plasmido:Tratamiento 1 532.4 532.4 190.9 7.27e-07 ***
## Residuals 8 22.3 2.8
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#Post hoc
TukeyHSD(anovaNMBgal)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Bgal ~ Plasmido * Tratamiento, data = DatosNM)
##
## $Plasmido
## diff lwr upr p adj
## NSFs-MSFs -16.95684 -19.1799 -14.73377 1e-07
##
## $Tratamiento
## diff lwr upr p adj
## Etop-Ctrl 21.08329 18.86022 23.30635 0
##
## $`Plasmido:Tratamiento`
## diff lwr upr p adj
## NSFs:Ctrl-MSFs:Ctrl -3.635718 -8.0016405 0.7302053 0.1064450
## MSFs:Etop-MSFs:Ctrl 34.404406 30.0384829 38.7703287 0.0000000
## NSFs:Etop-MSFs:Ctrl 4.126448 -0.2394745 8.4923712 0.0640523
## MSFs:Etop-NSFs:Ctrl 38.040123 33.6742005 42.4060463 0.0000000
## NSFs:Etop-NSFs:Ctrl 7.762166 3.3962430 12.1280888 0.0020465
## NSFs:Etop-MSFs:Etop -30.277957 -34.6438803 -25.9120346 0.0000001
#Nucleoscm2
#Prueba de normalidad
windows()
hist(DatosNM$Nucleoscm, main = expression(paste("Distribución de datos No. promedio de células por ", cm^{2})),
ylab = "Frecuencia", xlab = expression(paste("No. promedio de células por ", cm^{2})), col = "lightblue")
shapiro.test(lm(Nucleoscm~Muestra, DatosNM)$residuals)
##
## Shapiro-Wilk normality test
##
## data: lm(Nucleoscm ~ Muestra, DatosNM)$residuals
## W = 0.92988, p-value = 0.3788
#Los datos se distribuyen normalmente
#Homoscedasticidad
bartlett.test(Nucleoscm~Muestra, DatosNM)
##
## Bartlett test of homogeneity of variances
##
## data: Nucleoscm by Muestra
## Bartlett's K-squared = 6.9503, df = 3, p-value = 0.0735
#Hay homoscedasticidad de varianza
#Dos vías con interacción
anovaNMnucleoscm<-aov(Nucleoscm ~ Plasmido * Tratamiento, DatosNM)
summary(anovaNMnucleoscm)
## Df Sum Sq Mean Sq F value Pr(>F)
## Plasmido 1 4.035e+07 4.035e+07 2.748 0.136
## Tratamiento 1 1.904e+09 1.904e+09 129.673 3.19e-06 ***
## Plasmido:Tratamiento 1 2.977e+08 2.977e+08 20.268 0.002 **
## Residuals 8 1.175e+08 1.469e+07
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#Post hoc
TukeyHSD(anovaNMnucleoscm)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Nucleoscm ~ Plasmido * Tratamiento, data = DatosNM)
##
## $Plasmido
## diff lwr upr p adj
## NSFs-MSFs -3667.537 -8769.706 1434.633 0.1359862
##
## $Tratamiento
## diff lwr upr p adj
## Etop-Ctrl -25195.33 -30297.49 -20093.16 3.2e-06
##
## $`Plasmido:Tratamiento`
## diff lwr upr p adj
## NSFs:Ctrl-MSFs:Ctrl -13628.479 -23648.736 -3608.223 0.0104142
## MSFs:Etop-MSFs:Ctrl -35156.268 -45176.525 -25136.011 0.0000166
## NSFs:Etop-MSFs:Ctrl -28862.862 -38883.119 -18842.605 0.0000718
## MSFs:Etop-NSFs:Ctrl -21527.789 -31548.045 -11507.532 0.0005790
## NSFs:Etop-NSFs:Ctrl -15234.383 -25254.639 -5214.126 0.0054330
## NSFs:Etop-MSFs:Etop 6293.406 -3726.851 16313.663 0.2602189
#Apoptóticos
#Prueba de normalidad
windows()
hist(DatosNM$Apoptoticos, main = "Distribución de datos Porcentaje de núcleos apoptóticos",
ylab = "Frecuencia", xlab = "% Núcleos apoptóticos", col = "lightblue")
shapiro.test(lm(Apoptoticos~Muestra, DatosNM)$residuals)
##
## Shapiro-Wilk normality test
##
## data: lm(Apoptoticos ~ Muestra, DatosNM)$residuals
## W = 0.93272, p-value = 0.4099
#Los datos se distribuyen normalmente
#Homoscedasticidad
bartlett.test(Apoptoticos~Muestra, DatosNM)
##
## Bartlett test of homogeneity of variances
##
## data: Apoptoticos by Muestra
## Bartlett's K-squared = 0.21509, df = 3, p-value = 0.9751
#Hay homoscedasticidad de varianza
#Dos vías con interacción
anovaNMapop<-aov(Apoptoticos ~ Plasmido * Tratamiento, DatosNM)
summary(anovaNMapop)
## Df Sum Sq Mean Sq F value Pr(>F)
## Plasmido 1 49.63 49.63 31.65 0.000495 ***
## Tratamiento 1 67.65 67.65 43.14 0.000175 ***
## Plasmido:Tratamiento 1 1.54 1.54 0.98 0.351170
## Residuals 8 12.54 1.57
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#Post hoc
TukeyHSD(anovaNMapop)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Apoptoticos ~ Plasmido * Tratamiento, data = DatosNM)
##
## $Plasmido
## diff lwr upr p adj
## NSFs-MSFs -4.067311 -5.734442 -2.40018 0.000495
##
## $Tratamiento
## diff lwr upr p adj
## Etop-Ctrl 4.748567 3.081436 6.415697 0.0001751
##
## $`Plasmido:Tratamiento`
## diff lwr upr p adj
## NSFs:Ctrl-MSFs:Ctrl -4.7830314 -8.0571442 -1.50891869 0.0068909
## MSFs:Etop-MSFs:Ctrl 4.0328462 0.7587335 7.30695897 0.0179442
## NSFs:Etop-MSFs:Ctrl 0.6812557 -2.5928570 3.95536850 0.9068386
## MSFs:Etop-NSFs:Ctrl 8.8158777 5.5417649 12.08999041 0.0001174
## NSFs:Etop-NSFs:Ctrl 5.4642872 2.1901744 8.73839994 0.0030614
## NSFs:Etop-MSFs:Etop -3.3515905 -6.6257032 -0.07747772 0.0449493
#3N pcDNA3.1- y pALT
Datos3N<-read.csv("Danopcdna_palt_3N.csv", header=TRUE)
#Bgal
font_add_google("Josefin Sans", "Josefin")
showtext_auto()
windows()
ggplot(data = Datos3N, aes(x = factor(Nivel), y = Bgal, fill=factor(Plasmido))) +
scale_fill_manual(values=c("#009688", "#9E9E9E")) +
geom_boxplot(outlier.color="black") +
geom_jitter(color="#222424", size=1, alpha=1) +
labs(title="Células con actividad B-gal \nasociada a senescencia",
#subtitle="Porcentaje de células B-gal+",
#caption="P=0.0271369 P=0.0194539",
x="", y="% Células B-gal+") +
theme_few()+
theme(plot.title=element_text(family="Josefin", size=20),
plot.subtitle=element_text(family="Josefin"),
plot.caption =element_text(family="Josefin"),
axis.text.x = element_text(size=rel(1.35)),
axis.title.x = element_text(family="Josefin", face="bold", hjust=0.5, size=15),
axis.title.y = element_text(family="Josefin", face="bold", hjust=0.5, size=15),
legend.title=element_text(family="Josefin", face="bold", color="black", hjust=0.5))+
labs(fill="Plásmido \ntransfectado")+
scale_x_discrete(
"",
labels = c(
"a" = "pcDNA-Ctrl",
"c" = "pcDNA-Etop",
"b" = "pALT-Ctrl",
"d" = "pALT-Etop"
)
)
#Apoptóticos
windows()
ggplot(data = Datos3N, aes(x = factor(Nivel), y = Apoptoticos, fill=factor(Plasmido))) +
#facet_wrap(~Tratamiento) +
scale_fill_manual(values=c("#009688", "#9E9E9E")) +
geom_boxplot(outlier.color="black") +
geom_jitter(color="#222424", size=1, alpha=1) +
labs(title="Subletalidad de Tratamiento",
#subtitle="Porcentaje de núcleos apoptóticos",
caption="",
x="", y="% Núcleos apoptóticos") +
theme_few()+
theme(plot.title=element_text(family="Josefin", size=20),
plot.subtitle=element_text(family="Josefin"),
plot.caption =element_text(family="Josefin"),
axis.text.x = element_text(size=rel(1.35)),
axis.title.x = element_text(family="Josefin", face="bold", hjust=0.5, size=15),
axis.title.y = element_text(family="Josefin", face="bold", hjust=0.5, size=15),
legend.title=element_text(family="Josefin", face="bold", color="black", hjust=0.5))+
labs(fill="Plásmido \ntransfectado")+
scale_x_discrete(
"",
labels = c(
"a" = "pcDNA-Ctrl",
"c" = "pcDNA-Etop",
"b" = "pALT-Ctrl",
"d" = "pALT-Etop"
)
)
#Deformes
windows()
ggplot(data = Datos3N, aes(x = factor(Nivel), y = Deformes, fill=factor(Plasmido))) +
#facet_wrap(~Tratamiento) +
scale_fill_manual(values=c("#009688", "#9E9E9E")) +
geom_boxplot(outlier.color="black") +
geom_jitter(color="#222424", size=1, alpha=1) +
labs(title="Morfología nuclear alterada",
#subtitle="Porcentaje de núcleos con morfología alterada ",
caption="P=0.0408060",
x="", y="% Núcleos deformes") +
theme_few()+
theme(plot.title=element_text(family="Josefin", size=20),
plot.subtitle=element_text(family="Josefin"),
plot.caption =element_text(family="Josefin"),
axis.text.x = element_text(size=rel(1.35)),
axis.title.x = element_text(family="Josefin", face="bold", hjust=0.5, size=15),
axis.title.y = element_text(family="Josefin", face="bold", hjust=0.5, size=15),
legend.title=element_text(family="Josefin", face="bold", color="black", hjust=0.5))+
labs(fill="Plásmido \ntransfectado")+
scale_x_discrete(
"",
labels = c(
"a" = "pcDNA-Ctrl",
"c" = "pcDNA-Etop",
"b" = "pALT-Ctrl",
"d" = "pALT-Etop"
)
)
#Células por cm2
#4pixeles/um 2560pixelesx1920pixeles o 640umx480um 307200um2 *325.521x(células por campo con objetivo 20x)
windows()
ggplot(data = Datos3N, aes(x = factor(Nivel), y = Nucleoscm, fill=factor(Plasmido))) +
#facet_wrap(~Tratamiento) +
scale_fill_manual(values=c("#009688", "#9E9E9E")) +
geom_boxplot(outlier.color="black") +
geom_jitter(color="#222424", size=1, alpha=1) +
ylab( expression(paste("No. promedio de células por ", cm^{2})))+
labs(title="Densidad celular")+
#subtitle="Número promedio de células por cm^2 ",
#caption="",
#x="", y="No. promedio de células por cm^2") +
theme_few()+
theme(plot.title=element_text(family="Josefin", size=20),
plot.subtitle=element_text(family="Josefin"),
plot.caption =element_text(family="Josefin"),
axis.text.x = element_text(size=rel(1.35)),
axis.title.x = element_text(family="Josefin", face="bold", hjust=0.5, size=15),
axis.title.y = element_text(family="Josefin", face="bold", hjust=0.5, size=15),
legend.title=element_text(family="Josefin", face="bold", color="black", hjust=0.5))+
labs(fill="Plásmido \ntransfectado")+
scale_x_discrete(
"",
labels = c(
"a" = "pcDNA-Ctrl",
"c" = "pcDNA-Etop",
"b" = "pALT-Ctrl",
"d" = "pALT-Etop"
)
)
#Lamin A/C
windows()
ggplot(data = Datos3N, aes(x = factor(Nivel), y = LaminA, fill=factor(Plasmido))) +
#facet_wrap(~Tratamiento) +
scale_fill_manual(values=c("#009688", "#9E9E9E")) +
geom_boxplot(outlier.color="black") +
geom_jitter(color="#222424", size=1, alpha=1) +
labs(title="Intensidad de fluorescencia de lamin A/C",
#subtitle="Porcentaje de núcleos con morfología alterada ",
#caption="P=0.0408060",
x="", y="UA de Densidad Integrada") +
theme_few()+
theme(plot.title=element_text(family="Josefin", size=20),
plot.subtitle=element_text(family="Josefin"),
plot.caption =element_text(family="Josefin"),
axis.text.x = element_text(size=rel(1.35)),
axis.title.x = element_text(family="Josefin", face="bold", hjust=0.5, size=15),
axis.title.y = element_text(family="Josefin", face="bold", hjust=0.5, size=15),
legend.title=element_text(family="Josefin", face="bold", color="black", hjust=0.5))+
labs(fill="Plásmido \ntransfectado")+
scale_x_discrete(
"",
labels = c(
"a" = "pcDNA-Ctrl",
"c" = "pcDNA-Etop",
"b" = "pALT-Ctrl",
"d" = "pALT-Etop"
)
)
#Lamin B1
windows()
ggplot(data = Datos3N, aes(x = factor(Nivel), y = LaminB, fill=factor(Plasmido))) +
#facet_wrap(~Tratamiento) +
scale_fill_manual(values=c("#009688", "#9E9E9E")) +
geom_boxplot(outlier.color="black") +
geom_jitter(color="#222424", size=1, alpha=1) +
labs(title="Intensidad de fluorescencia de lamin B1",
#subtitle="Porcentaje de núcleos con morfología alterada ",
#caption="P=0.0408060",
x="", y="UA de Densidad Integrada") +
theme_few()+
theme(plot.title=element_text(family="Josefin", size=20),
plot.subtitle=element_text(family="Josefin"),
plot.caption =element_text(family="Josefin"),
axis.text.x = element_text(size=rel(1.35)),
axis.title.x = element_text(family="Josefin", face="bold", hjust=0.5, size=15),
axis.title.y = element_text(family="Josefin", face="bold", hjust=0.5, size=15),
legend.title=element_text(family="Josefin", face="bold", color="black", hjust=0.5))+
labs(fill="Plásmido \ntransfectado")+
scale_x_discrete(
"",
labels = c(
"a" = "pcDNA-Ctrl",
"c" = "pcDNA-Etop",
"b" = "pALT-Ctrl",
"d" = "pALT-Etop"
)
)
#Lamin A/C Deformes
windows()
ggplot(data = Datos3N, aes(x = factor(Nivel), y = LaminADef, fill=factor(Plasmido))) +
#facet_wrap(~Tratamiento) +
scale_fill_manual(values=c("#009688", "#9E9E9E")) +
geom_boxplot(outlier.color="black") +
geom_jitter(color="#222424", size=1, alpha=1) +
labs(title="Morfología Nuclear Alterada",
#subtitle="Porcentaje de núcleos con morfología alterada ",
#caption="P=0.0408060",
x="", y="% Núcleos Deformes") +
theme_few()+
theme(plot.title=element_text(family="Josefin", size=20),
plot.subtitle=element_text(family="Josefin"),
plot.caption =element_text(family="Josefin"),
axis.text.x = element_text(size=rel(1.35)),
axis.title.x = element_text(family="Josefin", face="bold", hjust=0.5, size=15),
axis.title.y = element_text(family="Josefin", face="bold", hjust=0.5, size=15),
legend.title=element_text(family="Josefin", face="bold", color="black", hjust=0.5))+
labs(fill="Plásmido \ntransfectado")+
scale_x_discrete(
"",
labels = c(
"a" = "pcDNA-Ctrl",
"c" = "pcDNA-Etop",
"b" = "pALT-Ctrl",
"d" = "pALT-Etop"
)
)
#Prueba de normalidad
#Bgal
shapiro.test(lm(Bgal~Muestra, Datos3N)$residuals)
##
## Shapiro-Wilk normality test
##
## data: lm(Bgal ~ Muestra, Datos3N)$residuals
## W = 0.92678, p-value = 0.3472
#Los datos se distribuyen normalmente
#Apoptóticos
shapiro.test(lm(Apoptoticos~Muestra, Datos3N)$residuals)
##
## Shapiro-Wilk normality test
##
## data: lm(Apoptoticos ~ Muestra, Datos3N)$residuals
## W = 0.88045, p-value = 0.08882
#Los datos se distribuyen normalmente
#Deformes
shapiro.test(lm(Deformes~Muestra, Datos3N)$residuals)
##
## Shapiro-Wilk normality test
##
## data: lm(Deformes ~ Muestra, Datos3N)$residuals
## W = 0.96721, p-value = 0.8795
#Los datos se distribuyen normalmente
#Núcleoscm
shapiro.test(lm(Nucleoscm~Muestra, Datos3N)$residuals)
##
## Shapiro-Wilk normality test
##
## data: lm(Nucleoscm ~ Muestra, Datos3N)$residuals
## W = 0.91571, p-value = 0.2524
#Los datos se distribuyen normalmente
#Lamin A/C
shapiro.test(lm(LaminA~Muestra, Datos3N)$residuals)
##
## Shapiro-Wilk normality test
##
## data: lm(LaminA ~ Muestra, Datos3N)$residuals
## W = 0.85178, p-value = 0.03862
#Los datos no se distribuyen normalmente
#Lamin B1
shapiro.test(lm(LaminB~Muestra, Datos3N)$residuals)
##
## Shapiro-Wilk normality test
##
## data: lm(LaminB ~ Muestra, Datos3N)$residuals
## W = 0.94567, p-value = 0.5748
#Los datos se distribuyen normalmente
#Lamin A/C Deformidades nucleares
shapiro.test(lm(LaminADef~Muestra, Datos3N)$residuals)
##
## Shapiro-Wilk normality test
##
## data: lm(LaminADef ~ Muestra, Datos3N)$residuals
## W = 0.93882, p-value = 0.483
#Los datos se distribuyen normalmente
#Homoscedasticidad
#Bgal
bartlett.test(Bgal~Muestra, Datos3N)
##
## Bartlett test of homogeneity of variances
##
## data: Bgal by Muestra
## Bartlett's K-squared = 0.31168, df = 3, p-value = 0.9578
#Hay homoscedasticidad de varianzas
#Apoptóticos
bartlett.test(Apoptoticos~Muestra, Datos3N)
##
## Bartlett test of homogeneity of variances
##
## data: Apoptoticos by Muestra
## Bartlett's K-squared = 8.6134, df = 3, p-value = 0.0349
#No hay homoscedasticidad de varianzas
#Deformes
bartlett.test(Deformes~Muestra, Datos3N)
##
## Bartlett test of homogeneity of variances
##
## data: Deformes by Muestra
## Bartlett's K-squared = 4.4157, df = 3, p-value = 0.2199
#Hay homoscedasticidad de varianzas
#Nucleoscm
bartlett.test(Nucleoscm~Muestra, Datos3N)
##
## Bartlett test of homogeneity of variances
##
## data: Nucleoscm by Muestra
## Bartlett's K-squared = 8.1123, df = 3, p-value = 0.04375
#No hay homoscedasticidad de varianzas
#Lamin A/C
bartlett.test(LaminA~Muestra, Datos3N)
##
## Bartlett test of homogeneity of variances
##
## data: LaminA by Muestra
## Bartlett's K-squared = 9.7648, df = 3, p-value = 0.02068
#No hay homoscedasticidad de varianzas#Nucleoscm
#Lamin B1
bartlett.test(LaminB~Muestra, Datos3N)
##
## Bartlett test of homogeneity of variances
##
## data: LaminB by Muestra
## Bartlett's K-squared = 5.8414, df = 3, p-value = 0.1196
#Hay homoscedasticidad de varianzas#Nucleoscm
#Lamin A/C Deformidades nucleares
bartlett.test(LaminADef~Muestra, Datos3N)
##
## Bartlett test of homogeneity of variances
##
## data: LaminADef by Muestra
## Bartlett's K-squared = 3.1982, df = 3, p-value = 0.3621
#Hay homoscedasticidad de varianzas
#Dos vías con interacción
Datos2vias3N<-read.csv("2vias3N.csv", header=TRUE)
#Bgal
anova3NBgal<-aov(Bgal ~ Plasmido * Tratamiento, Datos2vias3N)
summary(anova3NBgal)
## Df Sum Sq Mean Sq F value Pr(>F)
## Plasmido 1 395.3 395.3 6.116 0.038526 *
## Tratamiento 1 2131.5 2131.5 32.981 0.000432 ***
## Plasmido:Tratamiento 1 236.8 236.8 3.665 0.091917 .
## Residuals 8 517.0 64.6
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(anova3NBgal)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Bgal ~ Plasmido * Tratamiento, data = Datos2vias3N)
##
## $Plasmido
## diff lwr upr p adj
## pcDNA-pALT 11.47833 0.7753795 22.18129 0.038526
##
## $Tratamiento
## diff lwr upr p adj
## Etop-Ctrl 26.655 15.95205 37.35795 0.0004325
##
## $`Plasmido:Tratamiento`
## diff lwr upr p adj
## pcDNA:Ctrl-pALT:Ctrl 2.593333 -18.426421 23.61309 0.9776815
## pALT:Etop-pALT:Ctrl 17.770000 -3.249754 38.78975 0.1005511
## pcDNA:Etop-pALT:Ctrl 38.133333 17.113579 59.15309 0.0017957
## pALT:Etop-pcDNA:Ctrl 15.176667 -5.843088 36.19642 0.1741819
## pcDNA:Etop-pcDNA:Ctrl 35.540000 14.520246 56.55975 0.0028204
## pcDNA:Etop-pALT:Etop 20.363333 -0.656421 41.38309 0.0575654
#Apoptóticos
anova3NApop<-aov(Apoptoticos ~ Plasmido * Tratamiento, Datos2vias3N)
summary(anova3NApop)
## Df Sum Sq Mean Sq F value Pr(>F)
## Plasmido 1 24.40 24.40 18.845 0.00247 **
## Tratamiento 1 99.25 99.25 76.665 2.27e-05 ***
## Plasmido:Tratamiento 1 12.26 12.26 9.472 0.01517 *
## Residuals 8 10.36 1.29
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(anova3NApop)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Apoptoticos ~ Plasmido * Tratamiento, data = Datos2vias3N)
##
## $Plasmido
## diff lwr upr p adj
## pcDNA-pALT -2.851667 -4.366469 -1.336864 0.0024746
##
## $Tratamiento
## diff lwr upr p adj
## Etop-Ctrl 5.751667 4.236864 7.266469 2.27e-05
##
## $`Plasmido:Tratamiento`
## diff lwr upr p adj
## pcDNA:Ctrl-pALT:Ctrl -0.830000 -3.80495265 2.144953 0.8086324
## pALT:Etop-pALT:Ctrl 7.773333 4.79838068 10.748286 0.0001458
## pcDNA:Etop-pALT:Ctrl 2.900000 -0.07495265 5.874953 0.0560178
## pALT:Etop-pcDNA:Ctrl 8.603333 5.62838068 11.578286 0.0000697
## pcDNA:Etop-pcDNA:Ctrl 3.730000 0.75504735 6.704953 0.0163196
## pcDNA:Etop-pALT:Etop -4.873333 -7.84828598 -1.898381 0.0034401
#Deformes
anova3NDef<-aov(Deformes ~ Plasmido * Tratamiento, Datos2vias3N)
summary(anova3NDef)
## Df Sum Sq Mean Sq F value Pr(>F)
## Plasmido 1 0.963 0.963 1.119 0.3209
## Tratamiento 1 18.402 18.402 21.384 0.0017 **
## Plasmido:Tratamiento 1 1.178 1.178 1.369 0.2756
## Residuals 8 6.884 0.861
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(anova3NDef)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Deformes ~ Plasmido * Tratamiento, data = Datos2vias3N)
##
## $Plasmido
## diff lwr upr p adj
## pcDNA-pALT 0.5666667 -0.6683798 1.801713 0.3209334
##
## $Tratamiento
## diff lwr upr p adj
## Etop-Ctrl 2.476667 1.24162 3.711713 0.0017007
##
## $`Plasmido:Tratamiento`
## diff lwr upr p adj
## pcDNA:Ctrl-pALT:Ctrl -0.060000 -2.4855336 2.365534 0.9998059
## pALT:Etop-pALT:Ctrl 1.850000 -0.5755336 4.275534 0.1455880
## pcDNA:Etop-pALT:Ctrl 3.043333 0.6177998 5.468867 0.0162564
## pALT:Etop-pcDNA:Ctrl 1.910000 -0.5155336 4.335534 0.1304129
## pcDNA:Etop-pcDNA:Ctrl 3.103333 0.6777998 5.528867 0.0146257
## pcDNA:Etop-pALT:Etop 1.193333 -1.2322002 3.618867 0.4419206
#Nucleos por cm2
anova3Nnuc<-aov(Nucleoscm ~ Plasmido * Tratamiento, Datos2vias3N)
summary(anova3Nnuc)
## Df Sum Sq Mean Sq F value Pr(>F)
## Plasmido 1 64646423 64646423 4.759 0.060718 .
## Tratamiento 1 459115472 459115472 33.799 0.000399 ***
## Plasmido:Tratamiento 1 5137674 5137674 0.378 0.555644
## Residuals 8 108671154 13583894
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(anova3Nnuc)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Nucleoscm ~ Plasmido * Tratamiento, data = Datos2vias3N)
##
## $Plasmido
## diff lwr upr p adj
## pcDNA-pALT 4642.069 -264.8803 9549.019 0.060718
##
## $Tratamiento
## diff lwr upr p adj
## Etop-Ctrl -12370.87 -17277.82 -7463.923 0.0003989
##
## $`Plasmido:Tratamiento`
## diff lwr upr p adj
## pcDNA:Ctrl-pALT:Ctrl 5950.717 -3686.144 15587.577 0.2718477
## pALT:Etop-pALT:Ctrl -11062.225 -20699.086 -1425.365 0.0258564
## pcDNA:Etop-pALT:Ctrl -7728.803 -17365.664 1908.057 0.1222005
## pALT:Etop-pcDNA:Ctrl -17012.942 -26649.803 -7376.081 0.0021416
## pcDNA:Etop-pcDNA:Ctrl -13679.520 -23316.381 -4042.659 0.0081511
## pcDNA:Etop-pALT:Etop 3333.422 -6303.439 12970.283 0.6951794
#Intensidad Lamin B1
anova3Nlamb<-aov(LaminB ~ Plasmido * Tratamiento, Datos2vias3N)
summary(anova3Nlamb)
## Df Sum Sq Mean Sq F value Pr(>F)
## Plasmido 1 3.806e+08 3.806e+08 0.269 0.618
## Tratamiento 1 1.661e+09 1.661e+09 1.174 0.310
## Plasmido:Tratamiento 1 1.645e+09 1.645e+09 1.163 0.312
## Residuals 8 1.131e+10 1.414e+09
#No hay diferencias significativas
#Lamin A/C Deformidades nucleares
anova3NlamAD<-aov(LaminADef ~ Plasmido * Tratamiento, Datos2vias3N)
summary(anova3NlamAD)
## Df Sum Sq Mean Sq F value Pr(>F)
## Plasmido 1 343.0 343.0 6.091 0.03883 *
## Tratamiento 1 2052.2 2052.2 36.446 0.00031 ***
## Plasmido:Tratamiento 1 73.1 73.1 1.298 0.28754
## Residuals 8 450.5 56.3
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(anova3NlamAD)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = LaminADef ~ Plasmido * Tratamiento, data = Datos2vias3N)
##
## $Plasmido
## diff lwr upr p adj
## pcDNA-pALT 10.69263 0.7021289 20.68313 0.0388271
##
## $Tratamiento
## diff lwr upr p adj
## Etop-Ctrl 26.15474 16.16424 36.14524 0.0003103
##
## $`Plasmido:Tratamiento`
## diff lwr upr p adj
## pcDNA:Ctrl-pALT:Ctrl 5.756763 -13.863787 25.37731 0.7854165
## pALT:Etop-pALT:Ctrl 21.218877 1.598327 40.83943 0.0347031
## pcDNA:Etop-pALT:Ctrl 36.847370 17.226820 56.46792 0.0014321
## pALT:Etop-pcDNA:Ctrl 15.462113 -4.158437 35.08266 0.1300633
## pcDNA:Etop-pcDNA:Ctrl 31.090607 11.470057 50.71116 0.0042249
## pcDNA:Etop-pALT:Etop 15.628493 -3.992057 35.24904 0.1252272
#Kruskal Intensidad Lamin A/C
kruskal.test(LaminA~Plasmido, data=Datos2vias3N)
##
## Kruskal-Wallis rank sum test
##
## data: LaminA by Plasmido
## Kruskal-Wallis chi-squared = 0.10256, df = 1, p-value = 0.7488
#No hay diferencias significativas