#poner command, option I

install.packages("pacman")
## Installing package into '/cloud/lib/x86_64-pc-linux-gnu-library/4.4'
## (as 'lib' is unspecified)
library("pacman")
p_load("readr", 
       "dplyr",
       "ggplot2")

#cargar las bases de datos

PCR <- read.csv(file="https://raw.githubusercontent.com/ManuelLaraMVZ/Transcript-mica/main/datos_miRNAs.csv")
head(PCR)

#Aislar el control y seleccionar solo a u 6

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

  gen_ref
 Gen_int <- PCR %>% 
    filter(Condicion=="Target") %>% 
    select(-2)
  Gen_int
 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  
Mean_int <- Gen_int %>% 
    mutate(Prom_Cx=(Cx1+Cx2+Cx3)/3,
           Prom_Tx=(T1+T2+T3)/3) %>% 
    select("Gen", "Prom_Cx","Prom_Tx")
Mean_int
Analisis <- Mean_int %>% 
  mutate(DCT_Cx= Mean_int$Prom_Cx-Mean_ref$Prom_Cx,
         DCT_Tx= Mean_int$Prom_Tx-Mean_ref$Prom_Cx,
         DosDCT_Cx=2^-(DCT_Cx),
         DosDCT_Tx=2^-(DCT_Tx)) %>% 
  mutate(DosDDCT=DosDCT_Tx/ DosDCT_Cx)
Analisis
Grafica_1 <- ggplot(Analisis,
                    mapping = aes(x= DosDCT_Cx,
                                    y= DosDCT_Tx))+
  geom_point(color="green")+
  theme_classic() +
  labs(title= "Cambios de expresion de miRNAs",
       subtitle = "Grafica de dispersion",
       caption= "Creo: Regina Castro",
       x= "Condicion control (2^-DCT)",
       Y="Tratamiento (2^-DCT)")+
  geom_smooth(method="lm",
              color="pink",
              alpha=0.005,
              linewidth=0.2)
Grafica_1
## `geom_smooth()` using formula = 'y ~ x'