We ahve previously computed GSVA and stored, we just need to get expression data to get clinical variables
Now we can
##now what do we see on a tissue level?
res<-tab%>%
spread(key=sex,value=score)%>%
group_by(pathName)%>%
mutate(pval=t.test(female,male)$p.value)%>%
select(pathName,pval)%>%distinct()%>%
ungroup()%>%
mutate(correctedP=p.adjust(pval))
sigs.all<-subset(res,correctedP<0.05)
sigs.all
## # A tibble: 3 x 3
## pathName pval correctedP
## <fct> <dbl> <dbl>
## 1 KIM_MYCL1_AMPLIFICATION_TARGETS_DN 0.00000895 0.0268
## 2 KIM_MYCL1_AMPLIFICATION_TARGETS_UP 0.000000261 0.000783
## 3 RUNNE_GENDER_EFFECT_DN 0.0000000869 0.000261
##now what do we see on a tissue level?
res.c<-tab%>%
spread(key=sex,value=score)%>%
group_by(pathName,tumorType)%>%
mutate(pval=t.test(female,male)$p.value)%>%
select(pathName,pval,tumorType)%>%distinct()%>%
ungroup()%>%
group_by(tumorType)%>%
mutate(correctedP=p.adjust(pval))
sigs<-subset(res.c,correctedP<0.05)
sigs
## # A tibble: 27 x 4
## # Groups: tumorType [2]
## pathName pval tumorType correctedP
## <fct> <dbl> <chr> <dbl>
## 1 BASSO_HAIRY_CELL_LEUKEMI… 4.61e-6 Malignant Peripheral Ne… 0.0137
## 2 CLIMENT_BREAST_CANCER_CO… 1.23e-6 Malignant Peripheral Ne… 0.00369
## 3 DISTECHE_ESCAPED_FROM_X_… 1.32e-6 Cutaneous Neurofibroma 0.00396
## 4 HASLINGER_B_CLL_WITH_11Q… 1.55e-5 Malignant Peripheral Ne… 0.0462
## 5 IIZUKA_LIVER_CANCER_PROG… 3.82e-6 Malignant Peripheral Ne… 0.0114
## 6 JAZAERI_BREAST_CANCER_BR… 4.29e-6 Malignant Peripheral Ne… 0.0128
## 7 KANG_GIST_WITH_PDGFRA_UP 1.97e-6 Malignant Peripheral Ne… 0.00589
## 8 KEGG_PANTOTHENATE_AND_CO… 1.36e-7 Malignant Peripheral Ne… 0.000408
## 9 KEGG_TERPENOID_BACKBONE_… 3.93e-9 Malignant Peripheral Ne… 0.0000118
## 10 KORKOLA_YOLK_SAC_TUMOR_UP 7.34e-6 Malignant Peripheral Ne… 0.0219
## # … with 17 more rows
Now we have a lot of significant pathways.
for(ct in unique(sigs$pathName)){
sigs.t=subset(sigs,pathName==ct)
tab.t=subset(tab,pathName%in%sigs.t$pathName)%>%subset(pathName==ct)
p<-ggplot(tab.t,palette='jco')+geom_boxplot(aes(x=pathName,fill=sex,y=score))+facet_grid(.~tumorType)+ theme(axis.text.x = element_text(angle = 90, hjust = 1))
print(p)
}
#for(meth in unique(sigs$pathName)){
# sigs.t=subset(sigs,pathName==meth)
# for(tu in sigs.t$tumorType){
# tab.t=subset(tab,tumorType==tu)%>%
# subset(pathName==meth)
# p<-ggplot(tab.t,palette='jco')+geom_boxplot(aes(x=tumorType,fill=sex,y=score))+theme(axis.text.x = element_text(angle = 90, hjust = 1))+scale_y_log10()+ggtitle(paste(meth,'scores'))
# print(p)
# }
#}