Cuantificación absoluta
Ximena Sánchez
2025-05-02
if(!require("pacman"))
install.packages("pacman")## Loading required package: pacmanllamar a la paqueterÃa
library("pacman")
p_load("vroom",
"ggplot2",
"dplyr",
"ggrepel",
"tidyverse",
"scales")Datos_PCR <- vroom("https://raw.githubusercontent.com/ManuelLaraMVZ/resultados_PCR_practica/refs/heads/main/Cts1.csv")## Rows: 32 Columns: 5
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (4): Well, Grupo, Practica, Fluor
## dbl (1): Cq
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.Datos_PCR## # A tibble: 32 × 5
## Well Grupo Practica Fluor Cq
## <chr> <chr> <chr> <chr> <dbl>
## 1 A01 G1 Relativa SYBR 30.8
## 2 B01 G1 Relativa SYBR 41
## 3 C01 G1 Relativa SYBR 26.1
## 4 D01 G1 Relativa SYBR 41
## 5 E01 G1 Relativa SYBR 41
## 6 F01 G1 Relativa SYBR 22.3
## 7 Profesor G1 Relativa SYBR 30.7
## 8 Profesor G2 Relativa SYBR 30.7
## 9 B01 G2 Relativa SYBR 20.3
## 10 C01 G2 Relativa SYBR 18.7
## # ℹ 22 more rowsFiltrado de datos
Datos_curva <- Datos_PCR %>%
filter(Practica=="Absoluta",
Grupo== "Curva") %>%
select("Well", "Cq") %>%
mutate(Well= as.numeric(Well),
Curva=Well,
LogConc= log10(Curva)) %>%
select(3,4, 2)
Datos_curva## # A tibble: 7 × 3
## Curva LogConc Cq
## <dbl> <dbl> <dbl>
## 1 1000 3 12.4
## 2 100 2 15.6
## 3 10 1 18.8
## 4 1 0 22
## 5 0.1 -1 25.2
## 6 0.01 -2 28.4
## 7 0.001 -3 34.8Gráfica de datos
Grafica <- ggplot(Datos_curva,
aes(x= LogConc,
y=Cq))+
geom_point(color= "#581845",size=4)+
theme_classic()
Grafica
Realizar ajuste linear
modelo_lineal <- lm(data = Datos_curva,Cq ~ LogConc)
modelo_lineal##
## Call:
## lm(formula = Cq ~ LogConc, data = Datos_curva)
##
## Coefficients:
## (Intercept) LogConc
## 22.457 -3.543Obtención de los coeficientes
coeficientes <- coef(modelo_lineal)
coeficientes## (Intercept) LogConc
## 22.457143 -3.542857Pendiente
m <- round(coeficientes [2], 2)
m## LogConc
## -3.54Intersección
y0 <- round(coeficientes [1], 2)
y0## (Intercept)
## 22.46Predicción modelo
prediccion <- data.frame(LogConc=seq(min(Datos_curva$LogConc),
max(Datos_curva$LogConc),
length.out=100))
prediccion$Cq <- predict(modelo_lineal, newdata = prediccion)
head(prediccion)## LogConc Cq
## 1 -3.000000 33.08571
## 2 -2.939394 32.87100
## 3 -2.878788 32.65628
## 4 -2.818182 32.44156
## 5 -2.757576 32.22684
## 6 -2.696970 32.01212Obtenemos el valor de R^2
resumen_modelo <- summary(modelo_lineal)
resumen_modelo##
## Call:
## lm(formula = Cq ~ LogConc, data = Datos_curva)
##
## Residuals:
## 1 2 3 4 5 6 7
## 0.5714 0.2286 -0.1143 -0.4571 -0.8000 -1.1429 1.7143
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 22.4571 0.3959 56.73 3.22e-08 ***
## LogConc -3.5429 0.1979 -17.90 1.00e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.047 on 5 degrees of freedom
## Multiple R-squared: 0.9846, Adjusted R-squared: 0.9816
## F-statistic: 320.3 on 1 and 5 DF, p-value: 9.997e-06Obtener el valor de R^2
R2 <- round(resumen_modelo$r.squared,4)
R2## [1] 0.9846Ecuación para publicarla en la gráfica
ecuacion_recta <- paste0("y=",m,"x+",y0,"\nR^2=",R2)
cat("y=",m,"x+",y0,"\nR^2=",R2)## y= -3.54 x+ 22.46
## R^2= 0.9846Gráfica del ajuste y de la ecuación
Grafica_ajuste <- Grafica+
geom_smooth(data = prediccion,
aes(x = LogConc,
y =Cq),
color = "#E8daef",
size = 1.5)+
labs(title ="Curva estándar de RT-PCR",
subtitle = "Equipo: 2",
caption = "Diseño: Ximena Sánchez",
x="Log10(Concentración) [pg/uL)",
y="Cycle threshold (CT)")+
theme_classic(base_size = 15)+
theme(panel.grid.major = element_line(color = "grey80"),
panel.grid.minor = element_line(color = "grey90"))+
scale_y_continuous(labels = number_format(accuracy = 1))+
theme(plot.title = element_text(hjust = 0.5, face = "bold"),
axis.title.x = element_text(face = "bold"),
axis.title.y = element_text(face = "bold"))+
annotate("text",
x= max(Datos_curva$LogConc)-1.5,
y= min(Datos_curva$Cq)+13,
label= ecuacion_recta,
color="#17202a",
size=5,
fontface="bold",
hjust=0)## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.Grafica_ajuste## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
Predicción de la concentración de la muestra
Dato_muestra <- Datos_PCR %>%
filter(Practica == "Absoluta",
Grupo == "G2",
Well== "A03") %>%
select("Well","Cq")
Dato_muestra## # A tibble: 1 × 2
## Well Cq
## <chr> <dbl>
## 1 A03 15.2Obtener el valor de Ct
Ct_muestra <- round(Dato_muestra$Cq,2)
Ct_muestra## [1] 15.18Obtener el valor de x (logConc)
LogConc_calculado <- (Ct_muestra-y0)/m
LogConc_calculado## (Intercept)
## 2.056497Obtener el valor real
Valor_real <- 10^LogConc_calculado
cat("La concentración de la muestra es:", round(Valor_real,2), "pg/uL")## La concentración de la muestra es: 113.89 pg/uLConstrucción de Data.frame
Datos_muestra <- data.frame(Concentracion_real =round(Valor_real,2) ,
LogConc = round(LogConc_calculado,2),
Cq= Ct_muestra)
Datos_muestra## Concentracion_real LogConc Cq
## (Intercept) 113.89 2.06 15.18Gráfica de la muestra
Grafica_muestra <- Grafica_ajuste +
geom_point(data = Datos_muestra,
aes(x = LogConc, y = Cq),
color = "#FA8072",
size = 6,
shape = 20) +
annotate("segment",
x = min(Datos_curva$LogConc), xend = LogConc_calculado,
y = Ct_muestra, yend = Ct_muestra,
linetype = "dotted",
color = "#f06292",
size = 1.5) +
annotate("segment",
x = LogConc_calculado, xend = LogConc_calculado,
y = min(Datos_curva$Cq), yend = Ct_muestra,
linetype = "dotted",
color = "#f06292",
size = 1.5) +
annotate("text",
x = LogConc_calculado,
y = Ct_muestra - 0.7,
label = paste0("[Muestra]= ", round(Valor_real, 2), " pg/uL"),
color = "#f06292",
fontface = "bold",
hjust = -0.002,
vjust = -2)
Grafica_muestra## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'