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| Rmd | d79cb8f | haiderinam | 2019-09-13 | Adding ggplotly plots |
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nameposctrl1<-'BRAF'
#Positive control 1
nameposctrl2<-'NRAS'
#Oncogene in Question
namegene<-'ATI'
#Mutation Boolean (Y or N)
mtn<-'N'
#Name Mutation for Positive Ctrl 1
nameposctrl1mt<-'V600E'
#Name of Mutation for Positive Ctrl 2
nameposctrl2mt<-'Q61L'
alldata=read.csv("output/all_data_skcm.csv",sep=",",header=T,stringsAsFactors=F)
head(alldata) X Patid mean_RPKM_1.19 mean_RPKM_20.29 Ratio20.29 mRNA_count
1 1 TCGA-BF-A1PU 0.62445977 1.24042009 1.986389 948
2 2 TCGA-BF-A1PV 0.02099345 0.15815619 7.533598 82
3 3 TCGA-BF-A1PX 0.01752838 0.09612414 5.483914 92
4 4 TCGA-BF-A1PZ 0.19874434 7.27553619 36.607514 2822
5 5 TCGA-BF-A1Q0 2.13353636 3.71661801 1.741999 2211
6 6 TCGA-BF-A3DJ 0.06244694 0.55656239 8.912565 281
BRAF NRAS RSEM_normalized ATI
1 V600E NaN 107.1429 0
2 NaN Q61L 8.9659 0
3 V600E NaN 14.5985 0
4 NaN Q61R 329.0810 1
5 NaN NaN 277.0434 0
6 V600E NaN 35.1542 0###Not mutation specific
alldata_comp=alldata_compiler(alldata,nameposctrl1,nameposctrl2,namegene,'N',"N/A","N/A")[[2]]
head(alldata_comp) X Patid Positive_Ctrl1 Positive_Ctrl2 genex rndmarray
1 1 TCGA-BF-A1PU 1 0 0 0
2 2 TCGA-BF-A1PV 0 1 0 1
3 3 TCGA-BF-A1PX 1 0 0 0
4 4 TCGA-BF-A1PZ 0 1 1 0
5 5 TCGA-BF-A1Q0 0 0 0 0
6 6 TCGA-BF-A3DJ 1 0 0 0BRAF_NRAS=fisher.test(contab_maker(alldata_comp$Positive_Ctrl1,alldata_comp$Positive_Ctrl2,alldata_comp))$p.value
BRAF_NRAS_odds=fisher.test(contab_maker(alldata_comp$Positive_Ctrl1,alldata_comp$Positive_Ctrl2,alldata_comp))$estimate
BRAF_ATI=fisher.test(contab_maker(alldata_comp$Positive_Ctrl1,alldata_comp$genex,alldata_comp))$p.value
BRAF_ATI_odds=fisher.test(contab_maker(alldata_comp$Positive_Ctrl1,alldata_comp$genex,alldata_comp))$estimate
NRAS_ATI=fisher.test(contab_maker(alldata_comp$Positive_Ctrl2,alldata_comp$genex,alldata_comp))$p.value
NRAS_ATI_odds=fisher.test(contab_maker(alldata_comp$Positive_Ctrl2,alldata_comp$genex,alldata_comp))$estimate
Rndm_ATI=fisher.test(contab_maker(alldata_comp$rndmarray,alldata_comp$genex,alldata_comp))$p.value
Rndm_ATI_odds=fisher.test(contab_maker(alldata_comp$rndmarray,alldata_comp$genex,alldata_comp))$estimate
###Mutation specific
alldata_comp=alldata_compiler(alldata,nameposctrl1,nameposctrl2,namegene,'Y',"V600E","Q61L")[[2]]
head(alldata_comp) X Patid Positive_Ctrl1 Positive_Ctrl2 genex rndmarray
1 1 TCGA-BF-A1PU 1 0 0 0
2 2 TCGA-BF-A1PV 0 1 0 1
3 3 TCGA-BF-A1PX 1 0 0 0
4 4 TCGA-BF-A1PZ 0 0 1 0
5 5 TCGA-BF-A1Q0 0 0 0 0
6 6 TCGA-BF-A3DJ 1 0 0 0BRAF_NRAS_mtn=fisher.test(contab_maker(alldata_comp$Positive_Ctrl1,alldata_comp$Positive_Ctrl2,alldata_comp))$p.value
BRAF_NRAS_mtn_odds=fisher.test(contab_maker(alldata_comp$Positive_Ctrl1,alldata_comp$Positive_Ctrl2,alldata_comp))$estimate
BRAF_ATI_mtn=fisher.test(contab_maker(alldata_comp$Positive_Ctrl1,alldata_comp$genex,alldata_comp))$p.value
BRAF_ATI_mtn_odds=fisher.test(contab_maker(alldata_comp$Positive_Ctrl1,alldata_comp$genex,alldata_comp))$estimate
NRAS_ATI_mtn=fisher.test(contab_maker(alldata_comp$Positive_Ctrl2,alldata_comp$genex,alldata_comp))$p.value
NRAS_ATI_mtn_odds=fisher.test(contab_maker(alldata_comp$Positive_Ctrl2,alldata_comp$genex,alldata_comp))$estimate
Rndm_ATI_mtn=fisher.test(contab_maker(alldata_comp$rndmarray,alldata_comp$genex,alldata_comp))$p.value
Rndm_ATI_mtn_odds=fisher.test(contab_maker(alldata_comp$rndmarray,alldata_comp$genex,alldata_comp))$estimate
###Summarizing ME data in a table:
alkati_me_summary=data.frame(cbind(rbind(BRAF_ATI,BRAF_ATI_mtn,NRAS_ATI,NRAS_ATI_mtn,BRAF_NRAS,BRAF_NRAS_mtn,Rndm_ATI,Rndm_ATI_mtn),rbind(BRAF_ATI_odds,BRAF_ATI_mtn_odds,NRAS_ATI_odds,NRAS_ATI_mtn_odds,BRAF_NRAS_odds,BRAF_NRAS_mtn_odds,Rndm_ATI_odds,Rndm_ATI_mtn_odds)))
colnames(alkati_me_summary)=c("P.value","Odds.Ratio")
alkati_me_summary P.value Odds.Ratio
BRAF_ATI 4.071283e-01 0.75313992
BRAF_ATI_mtn 6.033028e-01 0.78803106
NRAS_ATI 1.343398e-01 1.70083914
NRAS_ATI_mtn 3.627722e-01 1.76258457
BRAF_NRAS 2.024969e-29 0.02538251
BRAF_NRAS_mtn 8.315112e-03 0.00000000
Rndm_ATI 4.138644e-01 1.53645442
Rndm_ATI_mtn 4.610659e-02 2.38723915nsubsamples=50 # maybe this can be removed and instead calculated later.
nsims<-100 #
#Positive control 1
nameposctrl1<-'BRAF'
#Positive control 1
nameposctrl2<-'NRAS'
#Oncogene in Question
namegene<-'ATI'
#Mutation Boolean (Y or N)
mtn<-'N'
#Name Mutation for Positive Ctrl 1
nameposctrl1mt<-'V600E'
#Name of Mutation for Positive Ctrl 2
nameposctrl2mt<-'Q61L'
alldata=read.csv("output/all_data_skcm.csv",sep=",",header=T,stringsAsFactors=F)
nexperiments=7
alldata_comp=alldata_compiler(alldata,nameposctrl1,nameposctrl2,namegene,mtn,"N/A","N/A")[[2]]
genex_replication_prop=alldata_compiler(alldata,nameposctrl1,nameposctrl2,namegene,mtn,"N/A","N/A")[[1]]
simresults_comb=data.frame()
for(subsample_number in c(seq(5,25,5))){
nsubsamples=subsample_number
simresults=simresults_generator(alldata_comp,7)
simresults_comb=rbind(simresults_comb,simresults) ##iterative rbind this is not the most efficient way to do this
}simresults_concat=simresults_comb%>%
filter(exp_num%in%c(4))
# simresults_concat=simresults_comb
plotly=ggplot(simresults_concat,aes(x=factor(subsample_size),y=log10(p_val)))+
geom_boxplot()+
cleanup+
guides(fill=F)+
scale_y_continuous(name="log P-Value")+
scale_x_discrete(name="Subsample size")+
# scale_color_manual(values="#E78AC3")+
theme(plot.title = element_text(hjust=.5),
text = element_text(size=11,face="bold"),
axis.title = element_text(face="bold",size="12",color="black"),
axis.text=element_text(face="bold",size="11",color="black"))
ggplotly(plotly)# font_import()
# windowsFonts(Arial = windowsFont("Arial"))
# ggsave("output/alkati_subsamplesize_pval_fig1c.pdf",width = 3,height = 2,units = "in",useDingbats=F)
max(simresults_concat$OR_val)[1] 0.4594879plotly=ggplot(simresults_concat,aes(x=factor(subsample_size),y=(OR_val)))+
geom_boxplot()+
cleanup+
guides(fill=F)+
scale_y_continuous(name="log OR")+
scale_x_discrete(name="Subsample size")+
# scale_color_manual(values="#E78AC3")+
theme(plot.title = element_text(hjust=.5),
text = element_text(size=12,face="bold"),
axis.title = element_text(face="bold",size="12",color="black"),
axis.text=element_text(face="bold",size="12",color="black"))
ggplotly(plotly)# ggsave("output/alkati_subsamplesize_orval_fig1c.pdf",width = 3,height = 2,units = "in",useDingbats=F)
# geom_hline(yintercept=-1.59,size=1)nsubsamples=12 # maybe this can be removed and instead calculated later.
nsims<-100 #
#Positive control 1
nameposctrl1<-'BRAF'
#Positive control 1
nameposctrl2<-'NRAS'
#Oncogene in Question
namegene<-'ATI'
#Mutation Boolean (Y or N)
mtn<-'Y'
#Name Mutation for Positive Ctrl 1
nameposctrl1mt<-'V600E'
#Name of Mutation for Positive Ctrl 2
nameposctrl2mt<-'Q61L'
alldata=read.csv("output/all_data_skcm.csv",sep=",",header=T,stringsAsFactors=F)
nexperiments=7
###For mutation
alldata_comp=alldata_compiler(alldata,nameposctrl1,nameposctrl2,namegene,mtn,nameposctrl1mt,nameposctrl2mt)[[2]]
genex_replication_prop=alldata_compiler(alldata,nameposctrl1,nameposctrl2,namegene,mtn,nameposctrl1mt,nameposctrl2mt)[[1]]
simresults=simresults_generator(alldata_comp,7)
simresults$mtn='Y'
####For no mutation
mtn='N'
alldata_comp=alldata_compiler(alldata,nameposctrl1,nameposctrl2,namegene,mtn,"N/A","N/A")[[2]]
genex_replication_prop=alldata_compiler(alldata,nameposctrl1,nameposctrl2,namegene,mtn,"N/A","N/A")[[1]]
simresults_nomtn=simresults_generator(alldata_comp,7)
simresults_nomtn$mtn='N'
simresults=rbind(simresults,simresults_nomtn)simresults[simresults$exp_num==1,]$exp_name="BRAF & ALKATI"
simresults[simresults$exp_num==3,]$exp_name="NRAS & ALKATI"
simresults[simresults$exp_num==4,]$exp_name="BRAF & NRAS"
simresults$exp_name=factor(simresults$exp_name,levels=c("1","5","6","7","BRAF & ALKATI","NRAS & ALKATI","BRAF & NRAS"))
simresults$mtn_tag='N'
simresults[simresults$mtn=='Y',]$mtn_tag="Mutation-specific"
simresults[simresults$mtn=='N',]$mtn_tag="Non mutation-specific"
simresults$mtn_tag=factor(simresults$mtn_tag,levels=c("Non mutation-specific","Mutation-specific"))
simresults_concat=simresults%>%
filter(exp_num==c(1,3,4))Warning in exp_num == c(1, 3, 4): longer object length is not a multiple of
shorter object lengthplotly=ggplot(simresults_concat,aes(x=factor(exp_name),y=log10(p_val)))+
geom_boxplot(aes(fill=factor(exp_name)))+
facet_wrap(~factor(mtn_tag))+
cleanup+
guides(fill=F)+
scale_y_continuous(name="log P-Value",limits = c(NA,1.5))+
scale_x_discrete(name="Gene Pair")+
scale_fill_brewer(palette = "Set2",name="Gene Pair")+
theme(plot.title = element_text(hjust=.5),
text = element_text(size=12,face="bold"),
axis.title = element_text(face="bold",size="13",color="black"),
axis.text.x=element_text(angle=15,hjust=.5,vjust=.5),
axis.text=element_text(face="bold",size="9",color="black"))
ggplotly(plotly)# ggsave("output/alkati_mtn_pval_fig2B.pdf",width = 6,height = 4,units = "in",useDingbats=F)
###Performing the KS. test on these to show that the distribtutions of BRAF NRAS and BRAF ALKATI/NRAS ALKATI are significantly different .
# Mutation-specific
brafati=simresults_concat%>%filter(mtn_tag=="Mutation-specific",exp_num==1)
nrasati=simresults_concat%>%filter(mtn_tag=="Mutation-specific",exp_num==3)
brafnras=simresults_concat%>%filter(mtn_tag=="Mutation-specific",exp_num==4)
ks.test(brafati$p_val,brafnras$p_val)$p.valueWarning in ks.test(brafati$p_val, brafnras$p_val): cannot compute exact p-
value with ties[1] 2.607914e-13ks.test(nrasati$p_val,brafnras$p_val)$p.valueWarning in ks.test(nrasati$p_val, brafnras$p_val): cannot compute exact p-
value with ties[1] 1.71041e-11ks.test(brafati$p_val,nrasati$p_val)$p.valueWarning in ks.test(brafati$p_val, nrasati$p_val): cannot compute exact p-
value with ties[1] 0.06996336# Non-mutation-specific
brafati=simresults_concat%>%filter(mtn_tag=="Non mutation-specific",exp_num==1)
nrasati=simresults_concat%>%filter(mtn_tag=="Non mutation-specific",exp_num==3)
brafnras=simresults_concat%>%filter(mtn_tag=="Non mutation-specific",exp_num==4)
ks.test(brafati$p_val,brafnras$p_val)$p.valueWarning in ks.test(brafati$p_val, brafnras$p_val): cannot compute exact p-
value with ties[1] 9.325873e-15ks.test(nrasati$p_val,brafnras$p_val)$p.valueWarning in ks.test(nrasati$p_val, brafnras$p_val): cannot compute exact p-
value with ties[1] 9.325873e-15ks.test(brafati$p_val,nrasati$p_val)$p.valueWarning in ks.test(brafati$p_val, nrasati$p_val): cannot compute exact p-
value with ties[1] 0.02546396
sessionInfo()R version 3.5.2 (2018-12-20)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS Mojave 10.14.6
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRlapack.dylib
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] parallel grid stats graphics grDevices utils datasets
[8] methods base
other attached packages:
[1] extrafont_0.17 ggsignif_0.5.0 usethis_1.5.0
[4] devtools_2.0.2 RColorBrewer_1.1-2 reshape2_1.4.3
[7] doParallel_1.0.14 iterators_1.0.10 foreach_1.4.4
[10] dplyr_0.8.1 VennDiagram_1.6.20 futile.logger_1.4.3
[13] workflowr_1.3.0 tictoc_1.0 knitr_1.23
[16] plotly_4.9.0 ggplot2_3.1.1
loaded via a namespace (and not attached):
[1] httr_1.4.0 pkgload_1.0.2 tidyr_0.8.3
[4] jsonlite_1.6 viridisLite_0.3.0 shiny_1.3.2
[7] assertthat_0.2.1 yaml_2.2.0 remotes_2.0.4
[10] sessioninfo_1.1.1 Rttf2pt1_1.3.7 pillar_1.4.1
[13] backports_1.1.4 glue_1.3.1 extrafontdb_1.0
[16] digest_0.6.19 promises_1.0.1 colorspace_1.4-1
[19] htmltools_0.3.6 httpuv_1.5.1 plyr_1.8.4
[22] pkgconfig_2.0.2 xtable_1.8-4 purrr_0.3.2
[25] scales_1.0.0 processx_3.3.1 whisker_0.3-2
[28] later_0.8.0 git2r_0.25.2 tibble_2.1.2
[31] withr_2.1.2 lazyeval_0.2.2 cli_1.1.0
[34] magrittr_1.5 crayon_1.3.4 mime_0.6
[37] memoise_1.1.0 evaluate_0.14 ps_1.3.0
[40] fs_1.3.1 pkgbuild_1.0.3 tools_3.5.2
[43] data.table_1.12.2 prettyunits_1.0.2 formatR_1.6
[46] stringr_1.4.0 munsell_0.5.0 lambda.r_1.2.3
[49] callr_3.2.0 compiler_3.5.2 rlang_0.3.4
[52] htmlwidgets_1.3 crosstalk_1.0.0 labeling_0.3
[55] rmarkdown_1.13 gtable_0.3.0 codetools_0.2-16
[58] R6_2.4.0 rprojroot_1.3-2 futile.options_1.0.1
[61] desc_1.2.0 stringi_1.4.3 Rcpp_1.0.1
[64] tidyselect_0.2.5 xfun_0.7