DATOS PCR

Se realizara un ejemplo de análisis de datos Para sacar chunks es : Ctrl + alt + i

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", # para graficar
        "dplyr", #para facilitar el manejo de datos 
        "vroom") #llamar repositorios 

Llamar a bases de datos

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

## `curl` package not installed, falling back to using `url()`
## 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

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

Generar base de datos con genes de interes

Gen_int <- Datos_PCR %>% 
  filter(Gen != "B-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 %>% 
  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: 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_gráfica <- DCT %>% 
  select("Gen","DosDDCT")
Datos_gráfica 

## # 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

Gráfica

Gráfica_PCR <- ggplot(Datos_gráfica,
                      aes(x=Gen,
                          y=DosDDCT))+
  
  geom_col()+
  theme_classic()
Gráfica_PCR

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