Ejercicio PCR
Danha Pineda
2026-02-18
Ejercicio R de PCR Los chunk los obtengo con: Ctrl+Alt+I PaqueterÃa
library("pacman")
p_load("vroom",
"dplyr",
"ggplot2")Llamar base de datos
Datos_PCR <-
vroom(file = "https://raw.githubusercontent.com/ManuelLaraMVZ/resultados_PCR_practica/refs/heads/main/Genes.csv")## `curl` package not installed, falling back to using `url()`
## Rows: 7 Columns: 7
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): Gen
## dbl (6): C1, C2, C3, T1, T2, T3
##
## 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: 7 × 7
## Gen C1 C2 C3 T1 T2 T3
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 B-actina 19 19.5 18.9 18.5 18.8 18.2
## 2 PIF1 22.4 22 21 28 28.2 27.9
## 3 PLK1 22 21.8 21.6 21.7 21 21.5
## 4 CCNB1 30.1 31.2 30.8 25.2 25.2 25.3
## 5 PCNA 20 20.3 20.2 24 24.2 NA
## 6 CCNB2 33 NA 33.1 24 25 26
## 7 BRCA 21 20.5 20.4 19.1 19.2 19.5Aislar gen de referencia
Gen_ref <- Datos_PCR %>%
filter(Gen == "B-actina")
Gen_ref## # A tibble: 1 × 7
## Gen C1 C2 C3 T1 T2 T3
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 B-actina 19 19.5 18.9 18.5 18.8 18.2Aislar gen de interés
Gen_int <- Datos_PCR %>%
filter(Gen!="B-actina")
Gen_int## # A tibble: 6 × 7
## Gen C1 C2 C3 T1 T2 T3
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 PIF1 22.4 22 21 28 28.2 27.9
## 2 PLK1 22 21.8 21.6 21.7 21 21.5
## 3 CCNB1 30.1 31.2 30.8 25.2 25.2 25.3
## 4 PCNA 20 20.3 20.2 24 24.2 NA
## 5 CCNB2 33 NA 33.1 24 25 26
## 6 BRCA 21 20.5 20.4 19.1 19.2 19.5Analisis
DCT <- Gen_int %>%
mutate(DCTC1 = C1 - Gen_ref$C1,
DCTC2 = C2 - Gen_ref$C2,
DCTC3 = C3 - Gen_ref$C3,
DCTT1 = T1 - Gen_ref$T1,
DCTT2 = T2 - Gen_ref$T2,
DCTT3 = T3 - Gen_ref$T3) %>%
mutate(DosDCT_C1 = 2^-DCTC1,
DosDCT_C2 = 2^-DCTC2,
DosDCT_C3 = 2^-DCTC3,
DosDCT_T1 = 2^-DCTT1,
DosDCT_T2 = 2^-DCTT2,
DosDCT_T3 = 2^-DCTT3,) %>%
mutate(DosDCT_Cx_prom = (DosDCT_C1+DosDCT_C2+DosDCT_C3/3),
DosDCT_Tx_prom = (DosDCT_T1+DosDCT_T2+DosDCT_T3/3)) %>%
mutate(DosDDCT = DosDCT_Tx_prom/DosDCT_Cx_prom)
DCT## # A tibble: 6 × 22
## Gen C1 C2 C3 T1 T2 T3 DCTC1 DCTC2 DCTC3 DCTT1 DCTT2 DCTT3
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 PIF1 22.4 22 21 28 28.2 27.9 3.4 2.5 2.08 9.5 9.45 9.7
## 2 PLK1 22 21.8 21.6 21.7 21 21.5 3 2.3 2.68 3.2 2.25 3.3
## 3 CCNB1 30.1 31.2 30.8 25.2 25.2 25.3 11.1 11.7 11.9 6.7 6.45 7.1
## 4 PCNA 20 20.3 20.2 24 24.2 NA 1 0.800 1.28 5.5 5.45 NA
## 5 CCNB2 33 NA 33.1 24 25 26 14 NA 14.2 5.5 6.25 7.8
## 6 BRCA 21 20.5 20.4 19.1 19.2 19.5 2 1 1.48 0.600 0.450 1.3
## # ℹ 9 more variables: DosDCT_C1 <dbl>, DosDCT_C2 <dbl>, DosDCT_C3 <dbl>,
## # DosDCT_T1 <dbl>, DosDCT_T2 <dbl>, DosDCT_T3 <dbl>, DosDCT_Cx_prom <dbl>,
## # DosDCT_Tx_prom <dbl>, DosDDCT <dbl>Selección de datos para graficar
# Selección de columnas
Datos_grafica <- DCT %>%
dplyr::select(Gen, DosDDCT)Grafica
Grafica_PCR <- ggplot(Datos_grafica,
aes(x = Gen, y = DosDDCT, fill = Gen)) +
geom_col(width = 0.7, color = "black") + # borde opcional para contraste
labs(
title = "Expresión relativa por gen",
subtitle = "Resultados de PCR en tiempo real (método ΔΔCt)",
x = "Gen",
y = "2^-ΔΔCt",
caption = "Fuente: DCT (procesamiento propio)"
) +
# Paleta de colores (elige una; aquà uso 'Dark2' que da colores bien contrastados)
scale_fill_brewer(palette = "Dark2") +
# Fondo blanco
theme_classic(base_size = 12) +
# Ajustes estéticos
theme(
legend.position = "none", # quita la leyenda (opcional)
plot.title = element_text(face = "bold"),
plot.subtitle = element_text(color = "gray30"),
axis.text.x = element_text(angle = 45, hjust = 1),
panel.background = element_rect(fill = "white", color = NA),
plot.background = element_rect(fill = "white", color = NA)
)
Grafica_PCR## Warning: Removed 2 rows containing missing values or values outside the scale range
## (`geom_col()`).