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/Metabolomica_2026_1/refs/heads/main/Datos_ejercicio_PCR1.1.csv")## Rows: 1001 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: 1,001 × 7
## Gen C1 C2 C3 T1 T2 T3
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 B-actina 19.9 20.2 19.9 20.0 20.1 19.9
## 2 Gene_1 23.9 23.0 24.0 21.7 22.4 21.0
## 3 Gene_2 24.5 22.9 25.5 21.3 23.3 24.3
## 4 Gene_3 28.1 25.0 23.9 19.1 23.3 19.7
## 5 Gene_4 25.1 24.7 27.4 20.6 19.4 25.0
## 6 Gene_5 25.3 19.9 25.3 27.2 17.9 23.8
## 7 Gene_6 28.4 27.1 23.8 21.9 26.4 22.7
## 8 Gene_7 25.9 25.5 21.4 23.8 22.5 23.1
## 9 Gene_8 22.5 29.8 23.7 21.6 22.8 22.4
## 10 Gene_9 23.6 26.4 29.1 23.2 19.6 21.1
## # ℹ 991 more rowsAislar 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 19.9 20.2 19.9 20.0 20.1 19.9Aislar gen de interés
Gen_int <- Datos_PCR %>%
filter(Gen!="B-actina")
Gen_int## # A tibble: 1,000 × 7
## Gen C1 C2 C3 T1 T2 T3
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Gene_1 23.9 23.0 24.0 21.7 22.4 21.0
## 2 Gene_2 24.5 22.9 25.5 21.3 23.3 24.3
## 3 Gene_3 28.1 25.0 23.9 19.1 23.3 19.7
## 4 Gene_4 25.1 24.7 27.4 20.6 19.4 25.0
## 5 Gene_5 25.3 19.9 25.3 27.2 17.9 23.8
## 6 Gene_6 28.4 27.1 23.8 21.9 26.4 22.7
## 7 Gene_7 25.9 25.5 21.4 23.8 22.5 23.1
## 8 Gene_8 22.5 29.8 23.7 21.6 22.8 22.4
## 9 Gene_9 23.6 26.4 29.1 23.2 19.6 21.1
## 10 Gene_10 24.1 24.1 23.9 21.9 24.3 21.3
## # ℹ 990 more rowsAnalisis
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: 1,000 × 22
## Gen C1 C2 C3 T1 T2 T3 DCTC1 DCTC2 DCTC3 DCTT1 DCTT2
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Gene_1 23.9 23.0 24.0 21.7 22.4 21.0 4.02 2.81 4.12 1.72 2.27
## 2 Gene_2 24.5 22.9 25.5 21.3 23.3 24.3 4.68 2.72 5.61 1.37 3.18
## 3 Gene_3 28.1 25.0 23.9 19.1 23.3 19.7 8.26 4.76 4.06 -0.876 3.22
## 4 Gene_4 25.1 24.7 27.4 20.6 19.4 25.0 5.28 4.54 7.58 0.626 -0.689
## 5 Gene_5 25.3 19.9 25.3 27.2 17.9 23.8 5.40 -0.298 5.49 7.22 -2.17
## 6 Gene_6 28.4 27.1 23.8 21.9 26.4 22.7 8.57 6.88 3.91 1.95 6.29
## 7 Gene_7 25.9 25.5 21.4 23.8 22.5 23.1 6.06 5.30 1.52 3.85 2.34
## 8 Gene_8 22.5 29.8 23.7 21.6 22.8 22.4 2.61 9.63 3.85 1.65 2.63
## 9 Gene_9 23.6 26.4 29.1 23.2 19.6 21.1 3.77 6.17 9.23 3.24 -0.507
## 10 Gene_10 24.1 24.1 23.9 21.9 24.3 21.3 4.25 3.91 4.02 1.92 4.14
## # ℹ 990 more rows
## # ℹ 10 more variables: DCTT3 <dbl>, 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 por Gen para colores distintos
geom_col() +
labs(title = "Expresión relativa de genes",
subtitle = "Normalización con B-actina como referencia",
caption = "Diseño: XXXX") +
theme_minimal(base_size = 14) + # estilo minimalista
theme(plot.background = element_rect(fill = "white", color = NA),
panel.background = element_rect(fill = "white", color = NA)) +
scale_fill_brewer(palette = "Set3") # paleta con colores distintos
Grafica_PCR
Análisis de regresión de datos
Datos_regresion <- DCT %>%
select("Gen", "DosDCT_Cx_prom", "DosDCT_Tx_prom")
Datos_regresion## # A tibble: 1,000 × 3
## Gen DosDCT_Cx_prom DosDCT_Tx_prom
## <chr> <dbl> <dbl>
## 1 Gene_1 0.224 0.662
## 2 Gene_2 0.198 0.515
## 3 Gene_3 0.0601 2.32
## 4 Gene_4 0.0706 2.27
## 5 Gene_5 1.26 4.54
## 6 Gene_6 0.0333 0.320
## 7 Gene_7 0.156 0.301
## 8 Gene_8 0.188 0.538
## 9 Gene_9 0.0879 1.67
## 10 Gene_10 0.140 0.445
## # ℹ 990 more rowsGraficar
Grafica_regresion <- ggplot(Datos_regresion,
aes(x = DosDCT_Cx_prom,
y = DosDCT_Tx_prom)) +
geom_point(size = 2.2, alpha = 0.75, color = "#2C7FB8") +
# LÃnea identidad: y = x (pendiente 1, intercepto 0)
geom_abline(slope = 1, intercept = 0,
linetype = "dashed", linewidth = 1,
color = "grey35") +
# (Opcional) lÃnea de regresión
# geom_smooth(method = "lm", se = FALSE,linewidth = 1.1, color = "#D7191C") +
coord_equal() +
labs(
title = "Relación entre DosDCT (Cx vs Tx)",
subtitle = "LÃnea gris: y = x (pendiente 1). LÃnea roja: ajuste lineal",
x = "DosDCT_Cx_prom",
y = "DosDCT_Tx_prom"
) +
theme_minimal(base_size = 12) +
theme(
plot.title = element_text(face = "bold"),
panel.grid.minor = element_blank()
)
Grafica_regresion