Ejercicio_PCR2

Ejercicio R de pcR Los chunks losmobtengo con : Ctrl+Alt+I / Comd+Opt+I paquetería

install.packages("pacman")

## Installing package into '/cloud/lib/x86_64-pc-linux-gnu-library/4.5'
## (as 'lib' is unspecified)

#pacman llama a otros paquetes, y si no estan, los instala
library(pacman)
p_load("vroom", # llamar base de datos
       "dplyr", # facilita el manejo de datos
       "ggplot2") #graficar

Llamar 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 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  19.5  18.9  18.5  18.8  18.2

Gen de interés

Gen_int <- Datos_PCR %>%
  filter(Gen!="B-actina") #!=todos excepto
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

DCT de datos de interferencia

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(Prom_Cx = (DosDCT_C1+DosDCT_C2+DosDCT_C3)/3,
       Prom_Tx = (DosDCT_T1+DosDCT_T2+DosDCT_T3)/3) %>%
mutate(DosDDCT = Prom_Cx/Prom_Tx)

DCT 

## # A tibble: 6 × 22
##   Gen      C1    C2    C3    T1    T2    T3 DCTC1  DCTC2 DCTC3 DCTT1 DCTT2 DCTT3
##   <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: DosDCT_C1 <dbl>, DosDCT_C2 <dbl>, DosDCT_C3 <dbl>,
## #   DosDCT_T1 <dbl>, DosDCT_T2 <dbl>, DosDCT_T3 <dbl>, Prom_Cx <dbl>,
## #   Prom_Tx <dbl>, DosDDCT <dbl>

Datos grafica

Datos_grafica <- DCT %>%
  select("Gen","DosDDCT")
Datos_grafica

## # A tibble: 6 × 2
##   Gen    DosDDCT
##   <chr>    <dbl>
## 1 PIF1  127.    
## 2 PLK1    1.15  
## 3 CCNB1   0.0360
## 4 PCNA   NA     
## 5 CCNB2  NA     
## 6 BRCA    0.617

library(ggplot2)

Datos_grafica <- DCT %>%
  select("Gen","DosDDCT")

Grafica_PCR <- ggplot(Datos_grafica,
                      aes(x = Gen,
                          y = DosDDCT,
                          fill = Gen)) +   # cada barra con color distinto
  geom_col() +
  labs(
    title = "Expresión génica relativa",
    subtitle = "Resultados del análisis PCR en tiempo real",
    caption = "Fuente: Datos experimentales DCT"
  ) +
  theme_minimal(base_size = 14) +          # estilo limpio
  theme(
    plot.background = element_rect(fill = "white"), # fondo blanco
    panel.background = element_rect(fill = "white"),
    legend.position = "none"               # ocultar leyenda si no es necesaria
  )

Grafica_PCR

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