Heatmap y PCA
Manuel Lara Lozano
2025-04-01
Chunk: Ctrl+Alt+I / Cmd+Opt+I
Cargar los paquetes, utilizando pacman
if (!require("pacman"))
install.packages("pacman")## Loading required package: pacmanlibrary("pacman")
p_load("pheatmap", #rear los Heatmaps
"RColorBrewer",
"ggplot2",
"dplyr",
"vroom",
"FactoMineR",
"factoextra",
"tibble")Llamar la base de datos
Datos_PCR <- vroom("https://raw.githubusercontent.com/ManuelLaraMVZ/Heatmaps/refs/heads/main/Ejemplo%206x4.csv")## Rows: 8 Columns: 8
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (2): Gene, Condition
## dbl (6): Control_1, Control_2, Control_3, Tratamiento_1, Tratamiento_2, Trat...
##
## 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.head(Datos_PCR)## # A tibble: 6 × 8
## Gene Condition Control_1 Control_2 Control_3 Tratamiento_1 Tratamiento_2
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Gen_1 Target 31.0 29.3 29.3 26.7 25.8
## 2 Gen_2 Target 30.1 29.1 29.5 28.8 26.3
## 3 Gen_3 Target 30.3 29.0 29.9 27.6 26.6
## 4 Gen_4 Target 25.4 26.5 25.5 32.1 28.7
## 5 Gen_5 Target 29.2 28.5 26.8 29.3 30.3
## 6 Gen_6 Target 27.9 27.9 27.8 30.6 29.9
## # ℹ 1 more variable: Tratamiento_3 <dbl>Sacar los genes de referencia, para poderlos utilizar como comparativos
Ref_gen_prom <- Datos_PCR %>%
filter(Condition == "Reference") %>%
select(-1, -2) %>%
summarise(across(everything(), mean, na.rm = T))## Warning: There was 1 warning in `summarise()`.
## ℹ In argument: `across(everything(), mean, na.rm = T)`.
## Caused by warning:
## ! The `...` argument of `across()` is deprecated as of dplyr 1.1.0.
## Supply arguments directly to `.fns` through an anonymous function instead.
##
## # Previously
## across(a:b, mean, na.rm = TRUE)
##
## # Now
## across(a:b, \(x) mean(x, na.rm = TRUE))head(Ref_gen_prom)## # A tibble: 1 × 6
## Control_1 Control_2 Control_3 Tratamiento_1 Tratamiento_2 Tratamiento_3
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 25.9 24.5 25.2 24.8 25.3 25.6Obtener el valor de Delta Ct
DCt <- Datos_PCR %>%
filter(Condition == "Target") %>%
select(-2) %>%
mutate(across(-1, ~ -(. -Ref_gen_prom[[cur_column()]][[1]]),
.names = "DCt_{.col}")) %>%
select(Gene, starts_with("DCt_"))
head(DCt)## # A tibble: 6 × 7
## Gene DCt_Control_1 DCt_Control_2 DCt_Control_3 DCt_Tratamiento_1
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 Gen_1 -5.06 -4.81 -4.07 -1.93
## 2 Gen_2 -4.18 -4.57 -4.27 -4.06
## 3 Gen_3 -4.39 -4.51 -4.64 -2.78
## 4 Gen_4 0.543 -1.99 -0.295 -7.32
## 5 Gen_5 -3.25 -4.05 -1.52 -4.51
## 6 Gen_6 -1.95 -3.39 -2.58 -5.77
## # ℹ 2 more variables: DCt_Tratamiento_2 <dbl>, DCt_Tratamiento_3 <dbl>Escalar los datos
miRNA_escalado <- DCt %>%
column_to_rownames(var = "Gene") %>%
scale(center = T,
scale = T) %>%
as.data.frame()
miRNA_escalado <- miRNA_escalado * 2
miRNA_escalado## DCt_Control_1 DCt_Control_2 DCt_Control_3 DCt_Tratamiento_1
## Gen_1 -1.955055 -1.7446042 -1.354102 2.5112385
## Gen_2 -1.093947 -1.2935813 -1.581784 0.3403755
## Gen_3 -1.301984 -1.1783013 -2.009708 1.6466580
## Gen_4 3.481844 3.5965362 2.997433 -2.9804692
## Gen_5 -0.191901 -0.3094469 1.583660 -0.1141984
## Gen_6 1.061043 0.9293975 0.364501 -1.4036044
## DCt_Tratamiento_2 DCt_Tratamiento_3
## Gen_1 2.227612 2.106222
## Gen_2 1.642266 2.034587
## Gen_3 1.354608 1.052326
## Gen_4 -0.791582 -2.670643
## Gen_5 -2.380496 -1.300499
## Gen_6 -2.052409 -1.221992Definir los colores del Heatmap
paleta_colores <- colorRampPalette(c("#150f44", "white", "#fbe104"))(100)Construir el Heatmap
Heatmap <- pheatmap(miRNA_escalado,
color = paleta_colores,
cluster_rows = T,
cluster_cols = T,
show_rownames = T, #TRUE para mostrar los nombres de los genes, FALSE para quitarlos
show_colnames = T,
fontsize_row = 14,
fontsize_col = 14,
border_color = "black",
main = "Heatmap de expresión de miRNAs",
fontface_row = "bold")
Heatmap
Análisis de PCA
Calcular el PCA
PCA_resultados <- prcomp(t(miRNA_escalado),
center = TRUE,
scale. = T)
summary(PCA_resultados)## Importance of components:
## PC1 PC2 PC3 PC4 PC5 PC6
## Standard deviation 2.277 0.7830 0.32267 0.31401 0.01889 9.655e-18
## Proportion of Variance 0.864 0.1022 0.01735 0.01643 0.00006 0.000e+00
## Cumulative Proportion 0.864 0.9661 0.98351 0.99994 1.00000 1.000e+00Screenplot: Varianza explicada por componente
fviz_eig(PCA_resultados,
addlabels = T,
barfill = "#7e085e",
barcolor = "#340527")
Gráfica de PCA (biplot)
PCA_df <- as.data.frame(PCA_resultados$x)
PCA_df$Sample <- rownames(PCA_df)Graficarlo
PCA_plot <- ggplot(PCA_df,
aes(x = PC1,
y = PC2)) +
geom_point(size = 4, aes(color = Sample)) +
# geom_text(aes(label = Sample), vjust = -0.1, size = 3) + # Descomentarlo si quieres etiquetas
geom_hline(yintercept = 0, linetype = "solid", color = "black", linewidth = 1.5) +
geom_vline(xintercept = 0, linetype = "solid", color = "black", linewidth = 1.5) +
scale_x_continuous(breaks = seq(floor(min(PCA_df$PC1)), ceiling(max(PCA_df$PC1)), by = 0.5)) + # Escalado en X
scale_y_continuous(breaks = seq(floor(min(PCA_df$PC2)), ceiling(max(PCA_df$PC2)), by = 0.5)) + # Escalado en Y
labs(title = "PCA de expresión de miRNAs",
x = "PC1",
y = "PC2") +
theme_minimal()
PCA_plot
Analizar el comportamiento de los genes
PCA_resultados_genes <- prcomp(miRNA_escalado,
center = T,
scale. = T)
summary(PCA_resultados_genes)## Importance of components:
## PC1 PC2 PC3 PC4 PC5 PC6
## Standard deviation 2.2857 0.74666 0.39132 0.24689 0.06404 2.288e-17
## Proportion of Variance 0.8707 0.09292 0.02552 0.01016 0.00068 0.000e+00
## Cumulative Proportion 0.8707 0.96364 0.98916 0.99932 1.00000 1.000e+00fviz_eig(PCA_resultados_genes, addlabels = T,
barfill = "#7e085e",
barcolor = "#340527")
PCA_df_genes <- as.data.frame(PCA_resultados_genes$x)
PCA_df_genes$Gene <- row.names(PCA_df_genes)Gráfica
PCA_plot_genes <- ggplot(PCA_df_genes,
aes(x = PC1,
y = PC2,
label = Gene))+
geom_point(size = 4, aes(color = Gene), show.legend = F)+
geom_text(vjust = -0.05, size = 3)+
geom_hline(yintercept = 0, linetype = "solid", color = "black", linewidth = 1.5) +
geom_vline(xintercept = 0, linetype = "solid", color = "black", linewidth = 1.5) +
labs(title = "PCA de expresión de miRNAs",
x = "PC1",
y = "PC2") +
theme_minimal()
PCA_plot_genes