Instalar y llamar paquetes

if (!require("pacman"))
  install.packages("pacman")
## Loading required package: pacman
library("pacman")
p_load("vroom",
       "ggplot2",
       "dplyr",
       "tidyverse",
       "scales")

llamar Base de datos

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.
head(Datos_PCR)
## # A tibble: 6 × 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

Datos Curva

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  34.8

Grafica de datos

Grafica <-  ggplot(Datos_Curva,
                   aes(x = LogConc,
                       y = Cq))+
  geom_point(color = "#0505ff", 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  
##      22.457       -3.543
coefficientes <- coef(modelo_lineal)

m <- round(coefficientes [2], 2)
m
## LogConc 
##   -3.54
y0 <-  round(coefficientes[1], 2)
y0
## (Intercept) 
##       22.46

predicción y r2

prediccion <-  data.frame(LogConc = seq(min(Datos_Curva$LogConc), max(Datos_Curva$LogConc), length.out = 100))
prediccion$Cq <- predict(modelo_lineal, newdata = prediccion)
head(prediccion)
##     LogConc       Cq
## 1 -3.000000 33.08571
## 2 -2.939394 32.87100
## 3 -2.878788 32.65628
## 4 -2.818182 32.44156
## 5 -2.757576 32.22684
## 6 -2.696970 32.01212
resum_modelo <- summary(modelo_lineal)
resum_modelo
## 
## Call:
## lm(formula = Cq ~ LogConc, data = Datos_Curva)
## 
## Residuals:
##       1       2       3       4       5       6       7 
##  0.5714  0.2286 -0.1143 -0.4571 -0.8000 -1.1429  1.7143 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  22.4571     0.3959   56.73 3.22e-08 ***
## LogConc      -3.5429     0.1979  -17.90 1.00e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.047 on 5 degrees of freedom
## Multiple R-squared:  0.9846, Adjusted R-squared:  0.9816 
## F-statistic: 320.3 on 1 and 5 DF,  p-value: 9.997e-06
r2 <- round(resum_modelo$r.squared, 4)
r2
## [1] 0.9846
ecuacion_recta <- paste0("y = ", m, "x + ", y0, "\n r2 = ", r2)
cat("y = ", m, "x + ", y0, "\n r2 = ", r2)
## y =  -3.54 x +  22.46 
##  r2 =  0.9846

Grafica de ajuste

Grafica_ajuste <- Grafica+
  geom_smooth(data=prediccion, 
              aes(x = LogConc,
                  y = Cq),
              color= "#ff00ff",
              size = 1.5)+
  labs(title = "Curva estandar de RT-PCR", 
       subtitle = "Equipo 2",
       caption = "Diseño RDR",
       x = "log10 (concentracion) [pg/uL)",
       y = "Cycle Thershold (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$LogConc) -1.5,
           y = min(Datos_Curva$Cq) +13,
           label = ecuacion_recta,
           color = "#4455ff",
           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'

preducción de concntración de la muestra

head(Datos_PCR)
## # A tibble: 6 × 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
Dato_muestra <- Datos_PCR %>% 
  filter(Practica == "Absoluta",
         Grupo == "G2",
         Well == "A02") %>% 
  select( "Well", "Cq")

Dato_muestra
## # A tibble: 1 × 2
##   Well     Cq
##   <chr> <dbl>
## 1 A02    12.1

Valor Ct

Ct_muestra <-  round(Dato_muestra$Cq, 2)
Ct_muestra
## [1] 12.1

Ecuacion

LogCont_calculado <- (Ct_muestra-y0)/m
LogCont_calculado
## (Intercept) 
##    2.926554

Valor real

Valor_real <- 10^LogCont_calculado
Valor_real
## (Intercept) 
##    844.4106
dm <-  data.frame(Concentración_real = Valor_real, LogConc = LogCont_calculado, Cq = Ct_muestra)
Grafica_Muestra <- Grafica_ajuste +
  geom_point(data = dm,
             aes(x = LogConc, y = Cq),
             color = "red", size = 6, shape = 20) +
  geom_segment(aes(x = min(Datos_Curva$LogConc), xend = LogCont_calculado, 
                   y = Ct_muestra, yend = Ct_muestra), 
               linetype = "dotted", color = "grey", size = 1.5) +  # Move linetype, color, size outside of aes()
  geom_segment(aes(x = LogCont_calculado, xend = LogCont_calculado, 
                   y = min(Datos_Curva$Cq), yend = Ct_muestra), 
               linetype = "dotted", color = "grey", size = 1.5)+
  annotate("text",
           x = LogCont_calculado,
           y = Ct_muestra,
           label = paste0("[Muestra] = ", round(Valor_real, 2), "pg/uL"),
           color = "#000000",
           fontface = "bold",
           hjust = 2,
           vjust = -2)

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 = LogCont_calculado, : All aesthetics have length 1, but the data has 7 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 = LogCont_calculado, xend = LogCont_calculado, : All aesthetics have length 1, but the data has 7 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'