if(!require("pacman"))
install.packages("pacman")
## Cargando paquete requerido: pacman
## Warning: package 'pacman' was built under R version 4.5.2
if(!require("tidyr"))
install.packages("tidyr")
## Cargando paquete requerido: tidyr
library("pacman")
p_load("ggplot2",
"dplyr",
"vroom")
Datos_PCRa <- vroom(file="https://raw.githubusercontent.com/ManuelLaraMVZ/Metabolomica_2026_1/refs/heads/main/Amplificacion_ambos%20grupos.csv")
## Rows: 51 Columns: 9
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## dbl (9): Cycle, 50 ng, 10 ng, 5 ng, 1 ng, 0.5 ng, 0.1 ng, G1-M, G2-M
##
## 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_PCRa
## # A tibble: 51 × 9
## Cycle `50 ng` `10 ng` `5 ng` `1 ng` `0.5 ng` `0.1 ng` `G1-M` `G2-M`
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 21.3 0.124 -2.25 2.06 -1.78 -2.32 22.1 -0.184
## 2 2 27.9 1.39 2.61 8.66 5.96 2.18 92.0 8.29
## 3 3 34.5 -0.660 11.4 12.2 5.94 2.34 132. 3.73
## 4 4 56.6 1.000 1.87 6.49 5.20 1.05 164. 3.20
## 5 5 65.2 -0.484 -1.18 1.77 -5.23 0.606 184. 0.557
## 6 6 72.3 1.44 -0.477 2.35 -0.958 4.81 214. 0.0300
## 7 7 89.1 -5.10 -3.87 -4.93 -3.60 -4.87 249. -7.37
## 8 8 101. -1.67 -3.52 -3.31 -2.34 0.403 277. -6.98
## 9 9 114. 2.66 5.35 -2.11 -1.40 -0.659 302. -3.63
## 10 10 123. 13.6 10.4 -5.98 2.27 1.13 332. -1.16
## # ℹ 41 more rows
Curvas_PCRa <- Datos_PCRa %>%
rename_with(~ sub("^(\\d+)$", "var\\1", .x))
Curvas_PCRa
## # A tibble: 51 × 9
## Cycle `50 ng` `10 ng` `5 ng` `1 ng` `0.5 ng` `0.1 ng` `G1-M` `G2-M`
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 21.3 0.124 -2.25 2.06 -1.78 -2.32 22.1 -0.184
## 2 2 27.9 1.39 2.61 8.66 5.96 2.18 92.0 8.29
## 3 3 34.5 -0.660 11.4 12.2 5.94 2.34 132. 3.73
## 4 4 56.6 1.000 1.87 6.49 5.20 1.05 164. 3.20
## 5 5 65.2 -0.484 -1.18 1.77 -5.23 0.606 184. 0.557
## 6 6 72.3 1.44 -0.477 2.35 -0.958 4.81 214. 0.0300
## 7 7 89.1 -5.10 -3.87 -4.93 -3.60 -4.87 249. -7.37
## 8 8 101. -1.67 -3.52 -3.31 -2.34 0.403 277. -6.98
## 9 9 114. 2.66 5.35 -2.11 -1.40 -0.659 302. -3.63
## 10 10 123. 13.6 10.4 -5.98 2.27 1.13 332. -1.16
## # ℹ 41 more rows
Curvas_PCRa <- Datos_PCRa %>%
mutate(Ciclos = Cycle, SJM = `50 ng`, AVY = `10 ng`, RAM = `5 ng`, RAS = `1 ng`, Otro_grupo_1 = `0.5 ng`, Otro_grupo_2 = `0.1 ng`, Muestra_1 = `G1-M`, Muestra_2 = `G2-M`) %>%
select(-Cycle:-`G2-M`)
Curvas_PCRa
## # A tibble: 51 × 9
## Ciclos SJM AVY RAM RAS Otro_grupo_1 Otro_grupo_2 Muestra_1
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 21.3 0.124 -2.25 2.06 -1.78 -2.32 22.1
## 2 2 27.9 1.39 2.61 8.66 5.96 2.18 92.0
## 3 3 34.5 -0.660 11.4 12.2 5.94 2.34 132.
## 4 4 56.6 1.000 1.87 6.49 5.20 1.05 164.
## 5 5 65.2 -0.484 -1.18 1.77 -5.23 0.606 184.
## 6 6 72.3 1.44 -0.477 2.35 -0.958 4.81 214.
## 7 7 89.1 -5.10 -3.87 -4.93 -3.60 -4.87 249.
## 8 8 101. -1.67 -3.52 -3.31 -2.34 0.403 277.
## 9 9 114. 2.66 5.35 -2.11 -1.40 -0.659 302.
## 10 10 123. 13.6 10.4 -5.98 2.27 1.13 332.
## # ℹ 41 more rows
## # ℹ 1 more variable: Muestra_2 <dbl>
Curvas_PCRai <- Curvas_PCRa %>%
select(Ciclos, SJM)
Curvas_PCRai
## # A tibble: 51 × 2
## Ciclos SJM
## <dbl> <dbl>
## 1 1 21.3
## 2 2 27.9
## 3 3 34.5
## 4 4 56.6
## 5 5 65.2
## 6 6 72.3
## 7 7 89.1
## 8 8 101.
## 9 9 114.
## 10 10 123.
## # ℹ 41 more rows
Curvas_PCRaai <- Curvas_PCRai %>%
pivot_longer(cols=-Ciclos,
names_to="muestras",
values_to="Fluorescencias")
Curvas_PCRaai
## # A tibble: 51 × 3
## Ciclos muestras Fluorescencias
## <dbl> <chr> <dbl>
## 1 1 SJM 21.3
## 2 2 SJM 27.9
## 3 3 SJM 34.5
## 4 4 SJM 56.6
## 5 5 SJM 65.2
## 6 6 SJM 72.3
## 7 7 SJM 89.1
## 8 8 SJM 101.
## 9 9 SJM 114.
## 10 10 SJM 123.
## # ℹ 41 more rows
umbral_ciclos <- 0.008
Graficas_PCRai <- ggplot(Curvas_PCRaai,
mapping = aes(x = Ciclos,
y = Fluorescencias,
color = muestras)) +
geom_line(linewidth = 1.5)+
geom_hline(yintercept = umbral_ciclos, linetype = "dashed", color = "#5286d0")+
theme_classic()+
labs(title = "Curvas de Amplificacion de valores absolutos",
subtitle = "Equipo",
caption = "Diseño: SJM",
x = "Ciclos",
y = "Fluorescencias(u.a.)")
Graficas_PCRai
ggsave(filename = "Grafica curva amplificacion absoluta SJM.jpg", plot = Graficas_PCRai, dpi = 300)
## Saving 7 x 5 in image
Curvas_PCRaa <- Curvas_PCRa %>%
pivot_longer(cols=-Ciclos,
names_to="muestras",
values_to="Fluorescencias")
Curvas_PCRaa
## # A tibble: 408 × 3
## Ciclos muestras Fluorescencias
## <dbl> <chr> <dbl>
## 1 1 SJM 21.3
## 2 1 AVY 0.124
## 3 1 RAM -2.25
## 4 1 RAS 2.06
## 5 1 Otro_grupo_1 -1.78
## 6 1 Otro_grupo_2 -2.32
## 7 1 Muestra_1 22.1
## 8 1 Muestra_2 -0.184
## 9 2 SJM 27.9
## 10 2 AVY 1.39
## # ℹ 398 more rows
umbral_ciclos <- 0.008
Graficas_PCRa <- ggplot(Curvas_PCRaa,
mapping = aes(x = Ciclos,
y = Fluorescencias,
color = muestras)) +
geom_line(linewidth = 1.5)+
geom_hline(yintercept = umbral_ciclos, linetype = "dashed", color = "#5286d0")+
theme_classic()+
labs(title = "Curvas de Amplificacion de valores absolutos",
subtitle = "Grupal",
caption = "Diseño: SJM",
x = "Ciclos",
y = "Fluorescencias(u.a.)")
Graficas_PCRa
ggsave(filename = "Grafica curva amplificacion absoluta grupal.jpg", plot = Graficas_PCRa, dpi = 300)
## Saving 7 x 5 in image