Ejercicio PCR: Datos masivos
Ian Atzin Gonzalez Islas
2026-02-24
Ejercicio R de PCR Los Chunks los obtengo con :Ctrl+Alt+I/Cmd+Opt+I, #ctrl shift m para el simbolo %>% Paquetería
library(pacman)
p_load("vroom", # llamar base de datos
"dplyr", # facilita el manejo de datos
"ggplot2") #graficarLlamar 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 19.9Gen de interes
Gen_int <- Datos_PCR %>%
filter(Gen!="B-actina") #!=todos excepto
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 sirve para crear nuevas columnas
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>Seleccion de datos para graficar
Datos_grafica <- DCT %>%
select("Gen","DosDDCT")
Datos_grafica## # A tibble: 1,000 × 2
## Gen DosDDCT
## <chr> <dbl>
## 1 Gene_1 3.69
## 2 Gene_2 2.59
## 3 Gene_3 30.6
## 4 Gene_4 30.9
## 5 Gene_5 3.59
## 6 Gene_6 5.36
## 7 Gene_7 0.955
## 8 Gene_8 2.79
## 9 Gene_9 21.9
## 10 Gene_10 3.82
## # ℹ 990 more rowsGrafica
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 lineal #ctrl shift m para el simbolo %>%
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 0.0874 0.322
## 2 Gene_2 0.0704 0.182
## 3 Gene_3 0.0334 1.02
## 4 Gene_4 0.0247 0.763
## 5 Gene_5 0.425 1.53
## 6 Gene_6 0.0259 0.139
## 7 Gene_7 0.129 0.123
## 8 Gene_8 0.0781 0.218
## 9 Gene_9 0.0297 0.651
## 10 Gene_10 0.0603 0.231
## # ℹ 990 more rowsGraficar
Grafica_regresion2 <- ggplot(Datos_regresion,
aes(x = DosDCT_Cx_prom,
y = DosDCT_Tx_prom)) +
geom_point(color = "steelblue", size = 3, alpha = 0.7) + # puntos con color y transparencia
geom_abline(intercept = 0, slope = 1, color = "red", linetype = "dashed", size = 1) + # línea y=x
theme_minimal(base_size = 14) + # estilo limpio
theme(
axis.title = element_text(face = "bold"),
axis.text = element_text(color = "black")
)## 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.Grafica_regresion2