if(!require(pacman))
install.packages("pacman")
## Loading required package: pacman
library("pacman")
p_load("vroom",
"ggplot2",
"ggrepel",
"dplyr",
"tidyr",
"plotly")
Datos_melting <- vroom(file = "https://raw.githubusercontent.com/ManuelLaraMVZ/resultados_PCR_practica/refs/heads/main/Disoci_ejemplo1.csv")
## Rows: 55 Columns: 6
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## dbl (6): Temperature, A1, B1, C1, D1, E1
##
## 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_melting
## # A tibble: 55 × 6
## Temperature A1 B1 C1 D1 E1
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 65 1.96 1.98 2.11 2.11 0.0869
## 2 65.5 1.94 1.99 2.06 2.08 0.0834
## 3 66 1.89 1.93 2.03 2.07 0.0932
## 4 66.5 1.86 1.89 1.98 2.02 0.0676
## 5 67 1.82 1.89 1.94 1.98 0.0738
## 6 67.5 1.77 1.83 1.90 1.93 0.0766
## 7 68 1.74 1.78 1.86 1.87 0.0686
## 8 68.5 1.70 1.76 1.79 1.84 0.0689
## 9 69 1.65 1.71 1.79 1.80 0.0665
## 10 69.5 1.60 1.64 1.75 1.76 0.0744
## # ℹ 45 more rows
Datos_melting2 <- Datos_melting %>%
mutate(Temperatura = Temperature, JFRW = A1, JRL = B1, PPOF = C1, JTT = D1, AALE = E1) %>%
select(-1:-6)
Datos_melting2
## # A tibble: 55 × 6
## Temperatura JFRW JRL PPOF JTT AALE
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 65 1.96 1.98 2.11 2.11 0.0869
## 2 65.5 1.94 1.99 2.06 2.08 0.0834
## 3 66 1.89 1.93 2.03 2.07 0.0932
## 4 66.5 1.86 1.89 1.98 2.02 0.0676
## 5 67 1.82 1.89 1.94 1.98 0.0738
## 6 67.5 1.77 1.83 1.90 1.93 0.0766
## 7 68 1.74 1.78 1.86 1.87 0.0686
## 8 68.5 1.70 1.76 1.79 1.84 0.0689
## 9 69 1.65 1.71 1.79 1.80 0.0665
## 10 69.5 1.60 1.64 1.75 1.76 0.0744
## # ℹ 45 more rows
Datos_melting3 <- Datos_melting2 %>%
pivot_longer(cols = -Temperatura,
names_to = "Muestras",
values_to = "Fluorescencia")
Datos_melting3
## # A tibble: 275 × 3
## Temperatura Muestras Fluorescencia
## <dbl> <chr> <dbl>
## 1 65 JFRW 1.96
## 2 65 JRL 1.98
## 3 65 PPOF 2.11
## 4 65 JTT 2.11
## 5 65 AALE 0.0869
## 6 65.5 JFRW 1.94
## 7 65.5 JRL 1.99
## 8 65.5 PPOF 2.06
## 9 65.5 JTT 2.08
## 10 65.5 AALE 0.0834
## # ℹ 265 more rows
Grafica_melting <- ggplot(Datos_melting3,
mapping = aes(x = Temperatura,
y = Fluorescencia,
color = Muestras))+
geom_line (size = 1.5)+
theme_classic() +
labs(title = "Curvas de disociación RT-qPCR",
subtitle = "Práctica 1, \nEquipo: JTT",
caption = "Todas las muestras",
x = "Temperatura (° C)",
y = "Fluorescencia (u.a.)") +
theme(
axis.line = element_line(size = 1.2, color = "black"),
axis.title = element_text(face = "bold"),
axis.text = element_text(face = "bold"),
legend.title = element_text(face = "bold"),
legend.text = element_text(face = "bold")) +
scale_x_continuous(breaks = seq(min(Datos_melting3$Temperatura), max(Datos_melting3$Temperatura), by = 5))+
scale_y_continuous(labels = scales::number_format(accuracy = 0.05),
breaks = seq(min(Datos_melting3$Fluorescencia), max(Datos_melting3$Fluorescencia), by = 0.25))
## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once per session.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
## Warning: The `size` argument of `element_line()` is deprecated as of ggplot2 3.4.0.
## ℹ Please use the `linewidth` argument instead.
## This warning is displayed once per session.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
Grafica_melting
Grafica_melting_equipo <- ggplot(Datos_melting2,
aes(x = Temperatura,
y = JTT))+
geom_line(size = 1.5, color = "#6287f1")+
theme_classic() +
labs(title = "Curvas de disociación RT-qPCR",
subtitle = "Práctica 1, \nEquipo: JTT",
caption = "Todas las muestras",
x = "Temperatura (° C)",
y = "Fluorescencia (u.a.)") +
theme(
axis.line = element_line(size = 1.2, color = "black"),
axis.title = element_text(face = "bold"),
axis.text = element_text(face = "bold"),
legend.title = element_text(face = "bold"),
legend.text = element_text(face = "bold")) +
scale_x_continuous(breaks = seq(min(Datos_melting3$Temperatura), max(Datos_melting3$Temperatura), by = 5))+
scale_y_continuous(labels = scales::number_format(accuracy = 0.05),
breaks = seq(min(Datos_melting3$Fluorescencia), max(Datos_melting3$Fluorescencia), by = 0.25))
Grafica_melting_equipo
Derivadas <- Datos_melting2 %>%
mutate(across(JFRW:AALE, ~ -c(NA, diff(.x) / diff(Datos_melting2$Temperatura)), .names = "d_{.col}")) %>%
select(-2: -6) %>%
slice(-1)
Derivadas
## # A tibble: 54 × 6
## Temperatura d_JFRW d_JRL d_PPOF d_JTT d_AALE
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 65.5 0.0365 -0.0147 0.0940 0.0711 0.00703
## 2 66 0.102 0.108 0.0614 0.0146 -0.0197
## 3 66.5 0.0695 0.0883 0.112 0.108 0.0512
## 4 67 0.0652 0.00898 0.0646 0.0772 -0.0123
## 5 67.5 0.109 0.114 0.0903 0.0923 -0.00567
## 6 68 0.0666 0.0994 0.0707 0.122 0.0162
## 7 68.5 0.0774 0.0335 0.139 0.0558 -0.000754
## 8 69 0.0860 0.0979 -0.00165 0.0850 0.00488
## 9 69.5 0.111 0.146 0.0908 0.0844 -0.0159
## 10 70 0.0582 0.0279 0.130 0.0864 0.00967
## # ℹ 44 more rows
Derivadas2 <- Derivadas %>%
pivot_longer(cols = -Temperatura,
names_to = "Muestras",
values_to = "Derivadas")
Derivadas2
## # A tibble: 270 × 3
## Temperatura Muestras Derivadas
## <dbl> <chr> <dbl>
## 1 65.5 d_JFRW 0.0365
## 2 65.5 d_JRL -0.0147
## 3 65.5 d_PPOF 0.0940
## 4 65.5 d_JTT 0.0711
## 5 65.5 d_AALE 0.00703
## 6 66 d_JFRW 0.102
## 7 66 d_JRL 0.108
## 8 66 d_PPOF 0.0614
## 9 66 d_JTT 0.0146
## 10 66 d_AALE -0.0197
## # ℹ 260 more rows
Grafica_melting <- ggplot(Derivadas2,
aes(x = Temperatura,
y = Derivadas,
color = Muestras)) +
geom_line() +
theme_classic() +
labs(title = "Curvas de disociación RT-qPCR",
subtitle = "Práctica 1, \nEquipo: JTT",
caption = "Todas las muestras",
x = "Temperatura (°C)",
y = expression("-" * Delta * "F/" * Delta * "T")) +
theme(
axis.line = element_line(size = 1.2, color = "black"),
axis.title = element_text(face = "bold"),
axis.text = element_text(face = "bold"),
legend.title = element_text(face = "bold"),
legend.text = element_text(face = "bold")
) +
scale_x_continuous(breaks = seq(min(Derivadas2$Temperatura), max(Derivadas2$Temperatura), by = 5)) +
scale_y_continuous(labels = scales::number_format(accuracy = 0.05),
breaks = seq(min(Derivadas2$Derivadas), max(Derivadas2$Derivadas), by = 0.25))
Grafica_melting
library(plotly)
Curva_3D <- plot_ly(Derivadas2,
x = ~Temperatura,
y = ~Derivadas,
z = ~Muestras,
color = ~factor(Muestras),
type = "scatter3d",
mode = "lines",
line = list(width = 6))
Curva_3D
Grafica_derivada_equipo <- ggplot(Derivadas,
aes(x = Temperatura,
y = d_JTT))+
geom_line(size = 1.5, color = "#6287f1" )+
theme_classic() +
labs(title = "Curvas de la derivada de disociación RT-qPCR",
subtitle = "Tejido: Corazón",
caption = "Caption: Julieta Tavera",
x = "Temperatura (° C)",
y = expression("-" * Delta * "F/" * Delta * "T")) +
theme(
axis.line = element_line(size = 1.2, color = "black"),
axis.title = element_text(face = "bold"),
axis.text = element_text(face = "bold"),
legend.title = element_text(face = "bold"),
legend.text = element_text(face = "bold")) +
scale_x_continuous(breaks = seq(min(Derivadas$Temperatura), max(Derivadas$Temperatura), by = 5))+
scale_y_continuous(labels = scales::number_format(accuracy = 0.05),
breaks = seq(min(Derivadas$d_JTT), max(Derivadas$d_JTT), by = 0.05))
Grafica_derivada_equipo
Peak_Tm <- max(Derivadas$d_JTT)
Peak_Tm
## [1] 0.738565
Tm <- Derivadas %>%
filter(d_JTT == Peak_Tm) %>%
select(1, d_JTT)
Tm
## # A tibble: 1 × 2
## Temperatura d_JTT
## <dbl> <dbl>
## 1 82 0.739
Etiqueta <- Grafica_derivada_equipo +
geom_vline (xintercept = Tm$Temperatura, color = "#900C3F", size = 1, linetype = "dashed")+
geom_label_repel(data = Tm,
aes(x = Temperatura,
y = d_JTT,
label = paste("Tm = ", round(Temperatura, 2), "° C")),
nudge_x = 3,
max.overlaps = 100,
fill = "#9eb7fc",
color = "black")
Etiqueta
