Gemma wide differential expression

Eno2.R

library(gemma.R)
library(ggplot2)
library(magrittr)
library(dplyr)


# mouse Eno2 datasets that are annotated with the term for brain
brain_eno2 = get_gene_differential_expression_values(13807 , uris = "http://purl.obolibrary.org/obo/UBERON_0000955")

# mouse Eno2 datasets that are annotated with the term for blood
blood_eno2 =  get_gene_differential_expression_values(13807 , uris = "http://purl.obolibrary.org/obo/UBERON_0000178")


brain_frame = data.frame(tissue = 'brain',
                         `log2(Fold Change)` = abs(brain_eno2$factor.logfc),
                         `-log10(p)` = -log10(brain_eno2$factor.pvalue),check.names = FALSE)

blood_frame = data.frame(tissue = 'blood',
                         `log2(Fold Change)` = abs(blood_eno2$factor.logfc),
                         `-log10(p)` = -log10(blood_eno2$factor.pvalue), check.names = FALSE)

frame = rbind(brain_frame,blood_frame) 

# this removes results with extreme differential expression values
frame %<>% dplyr::filter(`log2(Fold Change)` <10)

# set 0 Ps to the smallest non 0 value.
frame$`-log10(p)`[frame$`-log10(p)` == Inf] = max(frame$`-log10(p)`[frame$`-log10(p)` != Inf],na.rm = TRUE)

frame %>% 
    ggplot(aes(x = `log2(Fold Change)`, y = `-log10(p)`, color = tissue))  + geom_point(alpha = 0.5) + cowplot::theme_cowplot()


# what other datasets CACNA1G DE in?

cacna1g_dif_exp = get_gene_differential_expression_values(8913)


cacna1g_dif_exp %>% filter(correctedPValue < 0.05) %$% experiment.ID %>% unique