Cuantificación absoluta
Montserrat Espinosa
2026-05-02
if(require("pacman"))
install.packages("pacman")## Loading required package: pacman## Installing package into '/cloud/lib/x86_64-pc-linux-gnu-library/4.5'
## (as 'lib' is unspecified)Llamar paqueterÃa
library("pacman")
p_load("vroom", "ggplot2", "dplyr", "ggrepel", "tidyverse", "scales")Llamar base de datos
Datos_PCR <- vroom("https://raw.githubusercontent.com/ManuelLaraMVZ/Metabolomica_2026_1/refs/heads/main/Cts")## Rows: 20 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: 20 × 5
## Well Grupo Practica Fluor Cq
## <chr> <chr> <chr> <chr> <dbl>
## 1 50 Curva Absoluta SYBR 5.15
## 2 10 Curva Absoluta SYBR 10.8
## 3 5 Curva Absoluta SYBR 13.9
## 4 1 Curva Absoluta SYBR 15.7
## 5 0.5 Curva Absoluta SYBR 16.5
## 6 0.1 Curva Absoluta SYBR 18.3
## 7 Muestra G1 Absoluta SYBR 11.5
## 8 Muestra G2 Absoluta SYBR 13.1
## 9 A01 G1 Relativa SYBR 28.6
## 10 A02 G1 Relativa SYBR 15.0
## 11 A03 G1 Relativa SYBR 14.0
## 12 A04 G1 Relativa SYBR 50
## 13 A05 G1 Relativa SYBR 15.5
## 14 A06 G1 Relativa SYBR 12.6
## 15 A01 G2 Relativa SYBR 12.7
## 16 A02 G2 Relativa SYBR 16.3
## 17 A03 G2 Relativa SYBR 13.1
## 18 Referencia Referencia Relativa SYBR 18.4
## 19 Profesor G1 Relativa SYBR 20.0
## 20 Profesor G2 Relativa SYBR 20.0Filtrado 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: 6 × 3
## Curva LogConc Cq
## <dbl> <dbl> <dbl>
## 1 50 1.70 5.15
## 2 10 1 10.8
## 3 5 0.699 13.9
## 4 1 0 15.7
## 5 0.5 -0.301 16.5
## 6 0.1 -1 18.3Gráfica datos
Grafica <- ggplot(Datos_curva,
aes(x = LogConc,
y = Cq))+
geom_point(color = "#900C3F", size = 4)+
theme_classic()
Grafica
Ajuste lineal
Modelo_lineal <- lm(data = Datos_curva, Cq ~LogConc)
Modelo_lineal##
## Call:
## lm(formula = Cq ~ LogConc, data = Datos_curva)
##
## Coefficients:
## (Intercept) LogConc
## 14.991 -4.655Obtención coeficientes
Coeficientes <- coef(Modelo_lineal)
Coeficientes## (Intercept) LogConc
## 14.990632 -4.654892Pendiente
m <- round(Coeficientes [2], 2)
m## LogConc
## -4.65Intersección
Y0 <- round(Coeficientes [1], 2)
Y0## (Intercept)
## 14.99Predicción
Prediccion <- data.frame(LogConc = seq(min(Datos_curva$LogConc),
max(Datos_curva$LogConc),
length.out = 100))
Prediccion$Cq <- predict(Modelo_lineal, newdata = Prediccion)
head(Prediccion)## LogConc Cq
## 1 -1.0000000 19.64552
## 2 -0.9727377 19.51862
## 3 -0.9454754 19.39172
## 4 -0.9182130 19.26481
## 5 -0.8909507 19.13791
## 6 -0.8636884 19.01101Valor de R2
Resumen_modelo <- summary(Modelo_lineal)
Resumen_modelo##
## Call:
## lm(formula = Cq ~ LogConc, data = Datos_curva)
##
## Residuals:
## 1 2 3 4 5 6
## -1.93651 0.43656 2.12380 0.69647 0.06971 -1.39002
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 14.9906 0.7233 20.725 3.2e-05 ***
## LogConc -4.6549 0.7579 -6.142 0.00356 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.649 on 4 degrees of freedom
## Multiple R-squared: 0.9041, Adjusted R-squared: 0.8802
## F-statistic: 37.73 on 1 and 4 DF, p-value: 0.003563R2 <- round(Resumen_modelo$r.squared, 4)
R2## [1] 0.9041Ecuación en gráfica
Ecuacion_recta <- paste0("y =", m, "x +", Y0, "\nR2 =", R2)
cat("y =", m, "x +", Y0, "\nR2 =", R2)## y = -4.65 x + 14.99
## R2 = 0.9041Gráfica de ajuste con ecuación
Grafica_ajuste <- Grafica+
geom_smooth(data = Prediccion,
aes(x = LogConc,
y = Cq),
color = "#141761",
size = 1.5)+
labs(title = "Curva estándar de qPCR",
subtitle = "Equipo 5",
caption = "Diseñó: SJM",
x = "Log10 (Concentración) [pg/μL]",
y = "Cycle threshold (Ct)")+
theme_classic(base_size = 15)+
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$LogConc) -1.2,
y = min(Datos_curva$Cq) + 13,
label = Ecuacion_recta,
color = "#6B2018",
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'
Datos_PCR## # A tibble: 20 × 5
## Well Grupo Practica Fluor Cq
## <chr> <chr> <chr> <chr> <dbl>
## 1 50 Curva Absoluta SYBR 5.15
## 2 10 Curva Absoluta SYBR 10.8
## 3 5 Curva Absoluta SYBR 13.9
## 4 1 Curva Absoluta SYBR 15.7
## 5 0.5 Curva Absoluta SYBR 16.5
## 6 0.1 Curva Absoluta SYBR 18.3
## 7 Muestra G1 Absoluta SYBR 11.5
## 8 Muestra G2 Absoluta SYBR 13.1
## 9 A01 G1 Relativa SYBR 28.6
## 10 A02 G1 Relativa SYBR 15.0
## 11 A03 G1 Relativa SYBR 14.0
## 12 A04 G1 Relativa SYBR 50
## 13 A05 G1 Relativa SYBR 15.5
## 14 A06 G1 Relativa SYBR 12.6
## 15 A01 G2 Relativa SYBR 12.7
## 16 A02 G2 Relativa SYBR 16.3
## 17 A03 G2 Relativa SYBR 13.1
## 18 Referencia Referencia Relativa SYBR 18.4
## 19 Profesor G1 Relativa SYBR 20.0
## 20 Profesor G2 Relativa SYBR 20.0Predicción concentración muestra
Dato_muestra1 <- Datos_PCR %>%
filter(Practica == "Absoluta",
Grupo == "G1",
Well == "Muestra") %>%
select("Well", "Cq")
Dato_muestra1## # A tibble: 1 × 2
## Well Cq
## <chr> <dbl>
## 1 Muestra 11.5Dato_muestra2 <- Datos_PCR %>%
filter(Practica == "Absoluta",
Grupo == "G2",
Well == "Muestra") %>%
select("Well", "Cq")
Dato_muestra2## # A tibble: 1 × 2
## Well Cq
## <chr> <dbl>
## 1 Muestra 13.1Valor Ct
Ct_muestra1 <- Dato_muestra1$Cq
Ct_muestra1## [1] 11.5Ct_muestra2 <- Dato_muestra2$Cq
Ct_muestra2## [1] 13.1136Valor de x (LogConc)
LogConc_calculado1 <- (Ct_muestra1-Y0)/m
LogConc_calculado1## (Intercept)
## 0.7505376LogConc_calculado2 <- (Ct_muestra2-Y0)/m
LogConc_calculado2## (Intercept)
## 0.4035269Valor real
Valor_real1 <- 10^LogConc_calculado1
cat("La concentración de la muestra 1 es:", round(Valor_real1,2), "pg/μL")## La concentración de la muestra 1 es: 5.63 pg/μLValor_real2 <- 10^LogConc_calculado2
cat("La concentración de la muestra 2 es:", round(Valor_real2,2), "pg/μL")## La concentración de la muestra 2 es: 2.53 pg/μLConstrucción dataframe
Datos_muestra1 <- data.frame(Concentracion_real = round(Valor_real1,2),
LogConc = round(LogConc_calculado1,2),
Cq = Ct_muestra1)
Datos_muestra1## Concentracion_real LogConc Cq
## (Intercept) 5.63 0.75 11.5Datos_muestra2 <- data.frame(Concentracion_real = round(Valor_real2,2),
LogConc = round(LogConc_calculado2,2),
Cq = Ct_muestra2)
Datos_muestra2## Concentracion_real LogConc Cq
## (Intercept) 2.53 0.4 13.1136Grafica muestra
Grafica_muestra <- Grafica_ajuste+
geom_point(data = Datos_muestra1,
aes(x = LogConc,
y = Cq),
color = "black",
size = 6,
shape = 20)+
geom_segment(aes(x = min(Datos_curva$LogConc), xend = LogConc_calculado1,
y = Ct_muestra1, yend = Ct_muestra1),
linetype = "dashed",
color = "#8689F7",
size = 1)+
geom_segment(aes(x = LogConc_calculado1, xend = LogConc_calculado1,
y = min(Datos_curva$Cq), yend = Ct_muestra1),
linetype = "dashed",
color = "#8689F7",
size = 1)+
annotate("text",
x = LogConc_calculado1,
y = Ct_muestra1 -0.8,
label = paste0("[Muestra 1] =", round(Valor_real1,2), " pg/μL"),
color = "black",
fontface = "bold",
hjust = -0.007,
vjust = -1.7)+
geom_point(data = Datos_muestra2,
aes(x = LogConc,
y = Cq),
color = "black",
size = 6,
shape = 20)+
geom_segment(aes(x = min(Datos_curva$LogConc), xend = LogConc_calculado2,
y = Ct_muestra2, yend = Ct_muestra2),
linetype = "dashed",
color = "#8689F7",
size = 1)+
geom_segment(aes(x = LogConc_calculado2, xend = LogConc_calculado2,
y = min(Datos_curva$Cq), yend = Ct_muestra2),
linetype = "dashed",
color = "#8689F7",
size = 1)+
annotate("text",
x = LogConc_calculado2,
y = Ct_muestra2 -0.8,
label = paste0("[Muestra 2] =", round(Valor_real2,2), " pg/μL"),
color = "black",
fontface = "bold",
hjust = -0.007,
vjust = -1.7)
Grafica_muestra## Warning: Use of `Datos_curva$LogConc` is discouraged.
## ℹ Use `LogConc` instead.## Warning in geom_segment(aes(x = min(Datos_curva$LogConc), xend = LogConc_calculado1, : All aesthetics have length 1, but the data has 6 rows.
## ℹ Please consider using `annotate()` or provide this layer with data containing
## a single row.## Warning: Use of `Datos_curva$Cq` is discouraged.
## ℹ Use `Cq` instead.## Warning in geom_segment(aes(x = LogConc_calculado1, xend = LogConc_calculado1, : All aesthetics have length 1, but the data has 6 rows.
## ℹ Please consider using `annotate()` or provide this layer with data containing
## a single row.## Warning: Use of `Datos_curva$LogConc` is discouraged.
## ℹ Use `LogConc` instead.## Warning in geom_segment(aes(x = min(Datos_curva$LogConc), xend = LogConc_calculado2, : All aesthetics have length 1, but the data has 6 rows.
## ℹ Please consider using `annotate()` or provide this layer with data containing
## a single row.## Warning: Use of `Datos_curva$Cq` is discouraged.
## ℹ Use `Cq` instead.## Warning in geom_segment(aes(x = LogConc_calculado2, xend = LogConc_calculado2, : All aesthetics have length 1, but the data has 6 rows.
## ℹ Please consider using `annotate()` or provide this layer with data containing
## a single row.## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'