chunks: Ctrl+Alt+I, o Comand+Option+I se realizará un ejemplo de análisis de datos

install.packages("pacman")
## Installing package into '/cloud/lib/x86_64-pc-linux-gnu-library/4.5'
## (as 'lib' is unspecified)
library ("pacman")
p_load("ggplot2", "dplyr", "vroom")

llama a base de datos

Datos_PCR <- vroom(file="https://raw.githubusercontent.com/ManuelLaraMVZ/resultados_PCR_practica/refs/heads/main/Genes.csv")
## 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  19.5

aislar los genes de referencia de cada condición

Gen_ref <- Datos_PCR %>%
  filter(Gen == "B-actina") #== significa "que diga exactamente igual"
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  18.2

generar base de datos con genes de interés

Gen_int <- Datos_PCR %>%
  filter(Gen != "B-actina") #!= significa menos la fila de la 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  19.5

análisis

DCT <- Gen_int %>% #restar el gen de referencia a los GOI
  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 crea nuevas columnas, $ es para seleccionar esa columma de esa base de datos
  mutate(DosDCTC1 = 2^-DC1,
         DosDCTC2 = 2^-DC2,
         DosDCTC3 = 2^-DC3,
         DosDCTT1 = 2^-DT1,
         DosDCTT2 = 2^-DT2,
         DosDCTT3 = 2^-DT3,) %>% 
  #dividir tx entre la situación control, pero hay por triplicado, hay que sacar el promedio del Cx y Tx
  mutate (DosDCTCx = (DosDCTC1+DosDCTC2+DosDCTC3)/3,
          DosDCTTx = (DosDCTT1+DosDCTT2+DosDCTT3)/3) %>%
  #hacer la división tx/cx
  mutate (DosDDCT = DosDCTTx/DosDCTCx)


DCT
## # A tibble: 6 × 22
##   Gen      C1    C2    C3    T1    T2    T3   DC1    DC2   DC3   DT1   DT2   DT3
##   <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: DosDCTC1 <dbl>, DosDCTC2 <dbl>, DosDCTC3 <dbl>,
## #   DosDCTT1 <dbl>, DosDCTT2 <dbl>, DosDCTT3 <dbl>, DosDCTCx <dbl>,
## #   DosDCTTx <dbl>, DosDDCT <dbl>

aislar datos

Datos_grafica <- DCT %>%
  select("Gen","DosDDCT")
Datos_grafica
## # A tibble: 6 × 2
##   Gen    DosDDCT
##   <chr>    <dbl>
## 1 PIF1   0.00790
## 2 PLK1   0.869  
## 3 CCNB1 27.7    
## 4 PCNA  NA      
## 5 CCNB2 NA      
## 6 BRCA   1.62

grafica

Grafica_PCR <- ggplot(Datos_grafica,
                      aes(x = Gen,
                          y = DosDDCT))+
  geom_col()+
  theme_classic()

Grafica_PCR
## Warning: Removed 2 rows containing missing values or values outside the scale range
## (`geom_col()`).

gráfica regresión lineal: necesita nombres de genes, promedios de 2^-deltaCT de cx y tx ctrl+shift+m: magrritr

Datos_regresion <- DCT  %>%
  select("Gen", "DosDCTCx", "DosDCTTx")
Datos_regresion
## # A tibble: 6 × 3
##   Gen    DosDCTCx DosDCTTx
##   <chr>     <dbl>    <dbl>
## 1 PIF1   0.169     0.00134
## 2 PLK1   0.161     0.140  
## 3 CCNB1  0.000340  0.00945
## 4 PCNA   0.495    NA      
## 5 CCNB2 NA         0.0132 
## 6 BRCA   0.369     0.599

graficar regresión lineal

Grafica_regresion <- ggplot(Datos_regresion,
                            aes(x = DosDCTCx, y = DosDCTTx)) +
  geom_point(color = "steelblue", size = 3, alpha = 0.7) +   # puntos más visibles
  geom_abline(intercept = 0, slope = 1,                      # línea con pendiente 1
              color = "red", linetype = "dashed", size = 1) +
  geom_smooth(method = "lm", se = FALSE, color = "darkgreen") +
  labs(title = "Comparación de DosDCTCx vs DosDCTTx",
       x = "DosDCTCx",
       y = "DosDCTTx") +
  theme_minimal(base_size = 14) +                            # estilo limpio
  theme(plot.title = element_text(hjust = 0.5, face = "bold")) # título centrado
## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
Grafica_regresion
## `geom_smooth()` using formula = 'y ~ x'
## Warning: Removed 2 rows containing non-finite outside the scale range
## (`stat_smooth()`).
## Warning: Removed 2 rows containing missing values or values outside the scale range
## (`geom_point()`).