cargo los paquetes y la data
selecciono los genes y los datasets
hago los graficos
# HAGO LOS GRAFICO
genes_int = genes.quiero
for (i in 1:length(genes_int)){
# preparo la data
gen = genes_int[i]
exp.gen = exp[,gen]
exp.gen.l = log2(exp.gen + 1)
grupo = clin$grupo
experimento = clin$Experimento
datitos = data.frame(grupo, experimento, exp.gen, exp.gen.l )
# grafico
ploteo = ggplot(data = datitos %>% filter(experimento %in% ensayos.quiero ), aes(x=grupo, y=exp.gen.l )) +
geom_jitter(aes(color=experimento), size=3, width = 0.1)+
xlab(NULL) +
ylab(paste(gen, "expression \n log2 (norm counts +1)")) +
theme(legend.position = "bottom") +
theme_bw() +
theme(axis.text = element_text(size = 10),
axis.title = element_text(size = 10),
plot.title =element_text(size = 15),
axis.text.x = element_text(angle = 45, hjust=1)) +#,
#legend.position = 'none') + # para sacar la leyenda
stat_summary(fun=mean,
geom="point",
shape= '_',
size=10)
# imprimo
print(ploteo)
# lo agrego a pw
# guardo el plot
assign(paste(gen,'ploteo_DGE', sep = '_'), ploteo )
}







componer una figura unificada, esta parte la estoy desarrollando ahora
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