Datos PCR
Karla Gómez
2026-02-18
Se realizará un ejemplo de análisis de datos Chunck cmd+opt+I
#install.packages("pacman") #es un paquete que llama a otros paquetes y si no estan los instala
library("pacman") #esta función llama al paquete instalado
p_load("ggplot2","dplyr","vroom") #ggplot es para graficar, dplyr facilitar el manejo de datos y vroom llamar repositoriosLlama a 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 los genes de referencia para cada condicion
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 19.9Generar bases de datos con genes de interes
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(DC1=C1-Gen_ref$C1,
DC2=C2-Gen_ref$C2,
DC3=C3-Gen_ref$C3,
DT1=T1-Gen_ref$T1,
DT2=T2-Gen_ref$T2,
DT3=T3-Gen_ref$T3) %>%
mutate(DosDCTC1=2^-DC1,
DosDCTC2=2^-DC2,
DosDCTC3=2^-DC3,
DosDCTT1=2^-DT1,
DosDCTT2=2^-DT2,
DosDCTT3=2^-DT3) %>%
mutate(DosDCTCx= (DosDCTC1+DosDCTC2+DosDCTC3)/3,
DosDCTTx= (DosDCTT1+DosDCTT2+DosDCTT3)/3) %>%
mutate(DosDDCT= DosDCTTx/DosDCTCx)
DCT## # A tibble: 1,000 × 22
## Gen C1 C2 C3 T1 T2 T3 DC1 DC2 DC3 DT1 DT2
## <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: DT3 <dbl>, DosDCTC1 <dbl>, DosDCTC2 <dbl>,
## # DosDCTC3 <dbl>, DosDCTT1 <dbl>, DosDCTT2 <dbl>, DosDCTT3 <dbl>,
## # DosDCTCx <dbl>, DosDCTTx <dbl>, DosDDCT <dbl>Aislar datos
Datos_grafica <- DCT %>%
select(1,22)
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 rowsGráfica
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
Grafica regresión lineal
Datos_regresion <- DCT %>%
select("Gen","DosDCTCx","DosDCTTx")
Datos_regresion## # A tibble: 1,000 × 3
## Gen DosDCTCx DosDCTTx
## <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 regresión
Grafica_regresion <- ggplot(Datos_regresion,
aes(x = DosDCTCx,
y = DosDCTTx)) +
geom_point(color = "steelblue", size = 3, alpha = 0.7) +
geom_abline(intercept = 0, slope = 1, color = "red",
linetype = "dashed", size = 1) +
geom_smooth(method = "lm", se = FALSE, color = "darkgreen") +
labs(title = "Comparación entre DosDCTCx y DosDCTTx", x = "DosDCTCx", y = "DosDCTTx") + theme_minimal(base_size = 14) +
theme(plot.title = element_text(hjust = 0.5, face = "bold")) ## 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_regresion## `geom_smooth()` using formula = 'y ~ x'
#la roja es la de pendiente 1, la verde es la de los datos