Instalar y correr la paquetería

#install.packages("pacman")
library("pacman")


p_load("readr",
       "dplyr", 
       "ggplot2")

Cargar la base de datos

PCR <- read.csv(file="https://raw.githubusercontent.com/ManuelLaraMVZ/Transcript-mica/main/datos_miRNAs.csv")
head(PCR)
##                  Gen Condicion     Cx1     Cx2     Cx3      T1      T2      T3
## 1    U6 snRNA-001973   Control 13.7904 11.7446 11.9293 13.1954 12.9566 12.6417
## 2 ath-miR159a-000338    Target 35.0000 35.0000 35.0000 35.0000 35.0000 35.0000
## 3  hsa-let-7a-000377    Target 20.4943 21.0031 21.0073 20.3820 19.5925 20.9335
## 4  hsa-let-7b-002619    Target 18.3545 19.0325 19.1368 18.2551 17.4500 18.9675
## 5  hsa-let-7c-000379    Target 22.1791 23.6921 23.7763 22.9127 21.9639 23.9877
## 6  hsa-let-7d-002283    Target 22.1898 22.6786 21.8660 21.9711 21.2081 21.9410

Aislar el control y solo seleccionar gen ref

Gen_ref <- PCR %>% filter(Condicion=="Control") %>% 
  select(-2) %>%  filter(Gen=="U6 snRNA-001973")

Gen_ref
##               Gen     Cx1     Cx2     Cx3      T1      T2      T3
## 1 U6 snRNA-001973 13.7904 11.7446 11.9293 13.1954 12.9566 12.6417

Filtrar solo los genes de interés

Gen_int <- PCR %>% 
  filter(Condicion=="Target") %>%
  select(-2)   
  
  
head ( Gen_int)
##                  Gen     Cx1     Cx2     Cx3      T1      T2      T3
## 1 ath-miR159a-000338 35.0000 35.0000 35.0000 35.0000 35.0000 35.0000
## 2  hsa-let-7a-000377 20.4943 21.0031 21.0073 20.3820 19.5925 20.9335
## 3  hsa-let-7b-002619 18.3545 19.0325 19.1368 18.2551 17.4500 18.9675
## 4  hsa-let-7c-000379 22.1791 23.6921 23.7763 22.9127 21.9639 23.9877
## 5  hsa-let-7d-002283 22.1898 22.6786 21.8660 21.9711 21.2081 21.9410
## 6  hsa-let-7e-002406 17.9679 18.3915 18.4673 18.0119 17.2932 18.5586

Sacar promedio de las tres repeticiones, y adjuntar nuevas columnas

Mean_ref <- Gen_ref %>% 
  mutate(Prom_Cx=(Cx1+Cx2+Cx3)/3,
         Prom_Tx=(T1+T2+T3)/3) %>% 
  select("Gen","Prom_Cx","Prom_Tx")
Mean_ref
##               Gen Prom_Cx  Prom_Tx
## 1 U6 snRNA-001973 12.4881 12.93123
Mean_int <- Gen_int %>% 
  mutate(Prom_Cx=(Cx1+Cx2+Cx3)/3,
         Prom_Tx=(T1+T2+T3)/3) %>% 
  select("Gen","Prom_Cx","Prom_Tx")
head(Mean_int)
##                  Gen  Prom_Cx  Prom_Tx
## 1 ath-miR159a-000338 35.00000 35.00000
## 2  hsa-let-7a-000377 20.83490 20.30267
## 3  hsa-let-7b-002619 18.84127 18.22420
## 4  hsa-let-7c-000379 23.21583 22.95477
## 5  hsa-let-7d-002283 22.24480 21.70673
## 6  hsa-let-7e-002406 18.27557 17.95457

Obtención de DosDCt

Analisis <- Mean_int %>% 
  mutate(DCT_Cx= Mean_int$Prom_Cx- Mean_ref$Prom_Cx, 
         DCT_Tx= Mean_int$Prom_Tx- Mean_ref$Prom_Tx,
         DosDCT_Cx= 2^-(DCT_Cx),
         DosDCT_Tx=2^-(DCT_Tx)) %>% 
  mutate(DosDDCT=DosDCT_Tx/DosDCT_Cx)

Creación de gráfica

Grafica_1 <- ggplot(Analisis,
                   mapping = aes(x=DosDCT_Cx,
                                 y=DosDCT_Tx))+
  geom_point(color="#a9ff41") +
  theme_dark() +
  labs(title = "Cambios de expresión de miRNAs",
       subtitle = "Gráfica de dispersión",
       caption = "Creado por Paulo Escobedo",
       x="Condición control (2^-DCt)",
       y="Condición tratamiento (2^-DCt)")+

geom_smooth(method = "lm", 
            color= "#ff41f1",
            alpha= 0.005 ,
            linewidth = 0.2)
               
Grafica_1
## `geom_smooth()` using formula = 'y ~ x'