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FirstPassAnalysis_BaitPrey.R
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### RBP-network analysis
#Manipulation of Bait-Target Master Table
library(tidyverse)
library(dplyr)
library(ggplot2)
#install.packages("readxl")
library(readxl)
#read-in table
master_table<- read_csv("/Users/katierothamel/Dropbox (Personal)/Rothamel,Katherine/Projects/RBP_Networks/RawData/20220417_RBP_fullData2_final_PPI_table_withIntensity_IP_DeepProteome_forStoichiometry.csv")
#No self-loops
master_table<- master_table %>% filter(Prey != Bait)
#ordering bait based of life-cyclestep
master_table <- master_table[order(master_table$Life_cycle_step),]
#write_csv(master_table, "20220613_noselfloop_master_table.csv")
#Plotting interaction type for each unique bait, faceted on RNA-life cycle
#Ordering Bargraph
master_table %>%
ggplot(aes(x = fct_reorder(Bait, Life_cycle_step), fill = Interaction_type))+
theme_minimal() +
geom_bar(alpha=.5) +
labs(y= "Number of Interactions", title = "Bait and Prey First Pass") +
theme_minimal()+
theme(axis.text.x = element_text(angle = 90)) +
theme(axis.text=element_text(size=rel(0.6))) +
theme(legend.position="bottom")
master_table %>%
#filter(Interaction_type == "Direct") %>%
ggplot( aes(x = Bait,fill = PPI_support))+
theme_minimal() +
geom_bar(alpha=.5) +
labs(y= "Number of Interactions", title = "Direct Bait and Prey Interactions") +
facet_wrap(~ Life_cycle_step , scales = "free_x") +
#facet_wrap(~PPI_support) +
theme_minimal()+
theme(axis.text.x = element_text(angle = 90)) +
theme(axis.text=element_text(size=rel(0.6))) +
theme(legend.position="bottom")
master_table %>%
#filter(Interaction_type == "RNA mediated") %>%
ggplot( aes(x = Bait, fill = Interaction_type))+
theme_minimal() +
geom_bar(alpha=.5) +
labs(y= "Number of Interactions", title = "RNA mediated Bait and Prey Interactions") +
facet_wrap(~ Life_cycle_step , scales = "free_x") +
#facet_wrap(~PPI_support) +
theme_minimal()+
theme(axis.text.x = element_text(angle = 90)) +
theme(axis.text=element_text(size=rel(0.6))) +
theme(legend.position="bottom")
#Is intensity score differ do to interaction type? We have +/- RNAse conditions. High Bait/Prey ratio means more bait compared to prey.
library(ggrepel)
ggplot(master_table, aes(x= log(`bait_prey_intensity-ratio-IP_R`),y= log(`bait_prey_intensity-ratio-IP_noR`), color = Interaction_type, label= Prey))+
geom_point() +
theme_minimal() +
geom_abline(intercept = 0, slope = 1) +
theme(axis.text.x = element_text(angle = 90))
master_table %>% filter(Interaction_type == c("RNA shielded")) %>%
ggplot(aes(x= log(`bait_prey_intensity-ratio-IP_R`),y= log(`bait_prey_intensity-ratio-IP_noR`), label= Prey))+
theme_minimal() +
geom_point(alpha=0.9, color = "#619CFF") +
theme(axis.text.x = element_text(angle = 90)) +
geom_abline(intercept = 0, slope = 1) +
geom_text_repel(max.overlaps = 20)
###count the average number
meancounts<- master_table %>%
filter(Interaction_type =="RNA shielded") %>%
group_by(Bait)
sum(meancounts$n)
length(which(meancounts$PPI_support == "False"))
master_table %>%
gather(condition, intensity, bait_prey_intensity.ratio.IP_R:bait_prey_intensity.ratio.IP_noR) %>%
# dplyr::filter(Prey %in% c("G3BP2")) %>%
ggplot( aes(x= Interaction_type, y= log(intensity), fill = Interaction_type)) +
geom_boxplot(alpha=0.5) +
theme_minimal()+
#geom_hline(aes(yintercept = log(bait_intensity_noR)), linetype = "dashed", alpha= 0.5, color = "red") +
theme(axis.text.x = element_text(angle = 90)) +
theme(legend.position="top") +
facet_wrap(~condition)
master_table %>%
gather(condition, intensity, bait_prey_intensity.ratio.IP_R:bait_prey_intensity.ratio.IP_noR) %>%
# dplyr::filter(Prey %in% c("G3BP2")) %>%
ggplot( aes(x= Interaction_type, y= log(intensity), fill = Interaction_type)) +
geom_boxplot(alpha=0.5) +
theme_minimal()+
#geom_hline(aes(yintercept = log(bait_intensity_noR)), linetype = "dashed", alpha= 0.5, color = "red") +
theme(axis.text.x = element_text(angle = 90)) +
theme(legend.position="top") +
facet_wrap(~condition)
test<- master_table %>%
filter(Bait %in% c("DLD", "DLST", "DLAT", "OGDH", "PDHA1", "PDHB", "PDHX"))
# plotting no_R vs R
master_table %>% filter(Bait %in% c("DLD", "DLST", "DLAT", "OGDH", "PDHA1", "PDHB", "PDHX")) %>%
gather(condition, intensity, bait_prey_intensity.ratio.IP_R:bait_prey_intensity.ratio.IP_noR)%>%
ggplot(aes(x = Interaction_type, y= log(intensity), fill = Interaction_type)) +
geom_boxplot(alpha=0.5) +
theme_minimal()+
facet_wrap(~condition) +
#geom_hline(aes(yintercept = log(bait_intensity_noR)), linetype = "dashed", alpha= 0.5, color = "black") +
#geom_hline(aes(yintercept = log(bait_intensity_R)), linetype = "dashed", alpha= 0.5, color = "gray") +
theme(axis.text.x = element_text(angle = 90)) +
theme(legend.position="top") +
labs(title= "PDH Interactors")
master_table %>%
gather(condition, intensity, bait_prey_intensity.ratio.IP_R:bait_prey_intensity.ratio.IP_noR) %>%
filter(Bait %in% c("EXOSC10", "EXOSC2", "EXOSC9")) %>%
ggplot(aes(x = Interaction_type, y= log(intensity), fill = Interaction_type)) +
geom_boxplot(alpha=0.5) +
theme_minimal()+
facet_wrap(~condition) +
#geom_hline(aes(yintercept = log(bait_intensity_noR)), linetype = "dashed", alpha= 0.5, color = "black") +
#geom_hline(aes(yintercept = log(bait_intensity_R)), linetype = "dashed", alpha= 0.5, color = "gray") +
theme(axis.text.x = element_text(angle = 90)) +
theme(legend.position="top") +
labs(title= "Exosome Interactors" )
master_table %>%
gather(condition, intensity, bait_prey_intensity.ratio.IP_R:bait_prey_intensity.ratio.IP_noR) %>%
# filter(Bait %in% c("KARS1", "MARS1", "RARS1", "DARS")) %>%
ggplot(aes(x = Interaction_type, y= log(intensity), fill = Interaction_type)) +
geom_violin(alpha=0.5) +
geom_boxplot( width = .5, aplpha = 0.5) +
theme_minimal()+
facet_wrap(~condition) +
#geom_hline(aes(yintercept = log(bait_intensity_noR)), linetype = "dashed", alpha= 0.5, color = "black") +
#geom_hline(aes(yintercept = log(bait_intensity_R)), linetype = "dashed", alpha= 0.5, color = "gray") +
theme(axis.text.x = element_text(angle = 90)) +
theme(legend.position="top") +
labs(title= "Global Interactions")
master_table %>% filter(Prey == "CPSF2") %>%
ggplot( aes(x= log(bait_prey_ratio_R), y= log(bait_prey_ratio_noR), color = Interaction_type, label= Bait)) +
geom_point(aes( size = 5)) +
theme_minimal()+
geom_text_repel()
#geom_hline(yintercept = log(25000), linetype = "dashed", alpha= 0.5, color = "red") +
theme(axis.text.x = element_text(angle = 90)) +
theme(legend.position="top")
library(ggrepel)
master_table %>%
ggplot(aes(x = log(bait_prey_intensity.ratio.IP_R),
y= log(bait_prey_intensity.ratio.IP_noR))) +
geom_abline(slope = 1, intercept = 0) +
geom_jitter(aes(color = Interaction_type)) +
#geom_label_repel(aes(label = Prey)) +
theme_minimal()
master_table %>%
gather(condition, intensity, bait_prey_intensity.ratio.IP_R:bait_prey_intensity.ratio.IP_noR) %>%
ggplot(aes(x = Interaction_type, y= log(intensity), fill = Interaction_type))+
#geom_hline(yintercept = 1) + geom_hline(yintercept = -1) +
#geom_vline(xintercept = 1) + geom_vline(xintercept = -1) +
#geom_abline(1, intercept = 0) +
geom_boxplot(alpha= 0.5) +
theme_minimal() +
facet_wrap(~ Life_cycle_step + condition)
master_table %>%
gather(condition, intensity, bait_prey_intensity.ratio.IP_R:bait_prey_intensity.ratio.IP_noR) %>%
ggplot(aes(x = Interaction_type, y= log(intensity), color = Interaction_type))+
#geom_hline(yintercept = 1) + geom_hline(yintercept = -1) +
#geom_vline(xintercept = 1) + geom_vline(xintercept = -1) +
geom_abline(1, intercept = 0) +
geom_point() +
theme_minimal()
#wrangling data into matrix (0,1) for interactions with Bait also separating by interaction
clean_long_master_table<- master_table %>%
filter(Interaction_type %in% c("RNA mediated", "Direct")) %>%
select(Prey,Bait, bait_prey_intensity.ratio.IP_noR)%>%
unique() %>%
group_by(Prey) %>%
tidyr::spread(Bait,bait_prey_intensity.ratio.IP_noR)
clean_long_master_table[is.na(clean_long_master_table)] = 0
clean_long_master_table<-clean_long_master_table %>%
mutate_if(is.numeric, ~1 * (. > 0))
corRBP<-cor(clean_long_master_table[,-1:-2])
corRBP[is.na(corRBP)] = 0
write.csv(corRBP, "corRBP_DirectRNAmediated_BaitBait")
length(unique(master_table$Bait))
RNAmed_long_master_table<- master_table %>%
filter(Interaction_type == "RNA mediated") %>%
select(Prey, Bait, bait_prey_ratio_noR)%>%
group_by(Bait) %>% unique() %>%
spread(Prey, bait_prey_ratio_noR)
RNAmed_long_master_table[is.na(RNAmed_long_master_table)] = 0
RNAmed_long_master_table<-RNAmed_long_master_table %>%
mutate_if(is.numeric, ~1 * (. > 0))
#annot_clean_long<- left_join(clean_long_master_table, master_table, by="Bait")
# heatmap and hierarchical clustering
library(pheatmap)
library(RColorBrewer)
direct<-cor(direct_long_master_table[,-1])
RNAmed<-cor(RNAmed_long_master_table[,-1])
RNAmed[is.na(RNAmed)] = 0
direct_cor_matrix<-pheatmap(RNAmed,
color = colorRampPalette(rev(brewer.pal(n = 11, name = "RdYlBu")))(1000),
cellwidth = 1,
cellheight = 1,
cluster_cols=T,
cluster_rows = T,
show_rownames = F,
show_colnames = F)
RNAmed_cor_matrix<-pheatmap(RNAmed,
color = colorRampPalette(rev(brewer.pal(n = 11, name = "RdYlBu")))(1000),
cellwidth = 1,
cellheight = 1,
cluster_cols=T,
cluster_rows = T,
show_rownames = F,
show_colnames = F, cutree_rows = 12
)
(direct_cor_matrix$gtable$childrenOrder)
direct_hclust_gene <- hclust(dist(RNAmed), method = "complete")
direct_hclust_gene_ordered= data.frame(direct_hclust_gene$order)
direct_hclust_gene_ordered_bind = cbind(RNAmed, direct_hclust_gene_ordered[,1])
write.csv(direct_hclust_gene_ordered_bind, "test")
##################3
go_ontology<- read_excel("/Users/katierothamel/Desktop/Go_Ontology_Preys.xlsx")
go_ontology_long<- go_ontology %>%
gather(Column, Prey, -'High level GO category', -N) %>% na.omit()
write.csv(go_ontology_long, "go_ontology_long")
####
master_GO_table<- left_join(master_table, go_ontology_long, by= "Prey")
ThreePrime_processing<-master_GO_table %>% filter(Life_cycle_step == "3' end processing")
write_csv(ThreePrime_processing, "ThreePrime_Processing")
ggplot(ThreePrime_processing, aes(x = Bait, fill = `High level GO category`))+
theme_minimal() +
geom_bar(alpha=.5) +
labs(y= "Number of Interactions", title = "Bait and Prey First Pass") +
#facet_wrap(~ `High level GO category` ) +
theme(axis.text.x = element_text(angle = 90))
###############
#filtering master table by k means
kmean_queries<- read_csv("/Users/katierothamel/Dropbox (Personal)/Rothamel,Katherine/Projects/RBP_Networks/12kmeans_clusterz_preyinteraction.csv")
cluster1<- kmean_queries %>%
filter(k_means_12 == "12") %>%
rename(Bait = id )
test<- left_join(cluster1, master_table, by = "Bait") %>%
filter(Prey != "NA")
write_csv(test, "cluster_12.1_mastertable")
head(master_table)
head(clean_long_master_table)
############### RBP disease association
RBPdiseases<-read_excel("/Users/katierothamel/Dropbox (VU Basic Sciences)/Ascano Lab/Manuscripts/DynamicPTGR_Review/NRG-20-113 Hentze Supplementary Table 2.xlsx")
RBPdiseases_reduced<- RBPdiseases %>%
rename(Prey = gene_name) %>%
group_by(Prey,disease_name) %>%
summarise(Exp = unique(Therapeutic_area_name))
t_master_table<-read_excel("/Users/katierothamel/Dropbox (Personal)/Rothamel,Katherine/Projects/RBP_Networks/Trans_BaitPreyInteraction.xlsx")
disease_anno_KB_table<- full_join(master_table, RBPdiseases_reduced, by="Prey") %>% na.omit()
write_csv(disease_anno_KB_table, "disease_anno_KB_table")
ggplot(disease_anno_KB_table, aes(x=Therapeutic_area_name)) +
geom_bar() +
theme_minimal() +
theme(axis.text.x = element_text(angle = 90)) +
theme(axis.text=element_text(size=rel(0.6)))
test<-disease_anno_KB_table %>%
group_by(Bait, Prey) %>%
summarise_()
reactome_table<- read.csv("/Users/katierothamel/Desktop/ReactomeTest.csv")
reactome_table2<- reactome_table %>%
gather(Column, Gene, -'Pathway.name')
write.csv(reactome_table2, "reactome_table")
clusters<- read.csv("/Users/katierothamel/Dropbox (Personal)/Rothamel,Katherine/Projects/RBP_Networks/ClustergeneList_ForPreyPreyHeatMap.csv")
long_clusters<- clusters %>% gather(Cluster, Prey, na.rm = TRUE)
master_cluster<- full_join(long_clusters, master_table, by= "Prey")
master_cluster_test<- master_cluster[is.na(master_cluster$Bait) == FALSE , ]
write.csv(pot_bridges, "Prey_potential_bridges.csv")