generated ggpairs plots finally
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11 changed files with 657 additions and 939 deletions
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@ -1,51 +1,88 @@
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source("~/git/LSHTM_analysis/config/embb.R")
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source("~/git/LSHTM_analysis/scripts/plotting/plotting_colnames.R")
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source("~/git/LSHTM_analysis/scripts/plotting/get_plotting_dfs.R")
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#source("~/git/LSHTM_analysis/config/embb.R")
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#source("~/git/LSHTM_analysis/scripts/plotting/plotting_colnames.R")
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#source("~/git/LSHTM_analysis/scripts/plotting/get_plotting_dfs.R")
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my_gg_pairs=function(plot_df){
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my_gg_pairs=function(plot_df, plot_title
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, tt_args_size = 2.5
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, gp_args_size = 2.5){
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ggpairs(plot_df,
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columns = 1:(ncol(plot_df)-1),
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upper = list(
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continuous = wrap('cor',
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continuous = wrap('cor', # ggally_cor()
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method = "spearman",
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use = "pairwise.complete.obs",
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title="ρ",
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digits=2,
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justify_labels = "left",
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title_args=c(colour="black")
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justify_labels = "centre",
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#title_args=c(colour="black"),
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title_args=c(size=tt_args_size),#2.5
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group_args=c(size=gp_args_size)#2.5
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)
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),
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lower = list(
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continuous = wrap("points",
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alpha = 0.7,
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size=0.5),
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size=0.125),
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combo = wrap("dot",
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alpha = 0.7,
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size=0.5)
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size=0.125)
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),
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aes(colour = factor(ifelse(plot_df$dst_mode==0,
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aes(colour = factor(ifelse(dst_mode==0,
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"S",
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"R") ),
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alpha = 0.5),
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title="Stability") +
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title=plot_title) +
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scale_colour_manual(values = c("red", "blue")) +
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scale_fill_manual(values = c("red", "blue")) +
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theme(text = element_text(size=12,
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face="bold") )
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scale_fill_manual(values = c("red", "blue")) #+
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# theme(text = element_text(size=7,
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# face="bold"))
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}
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DistCutOff = 10
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###########################################################################
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geneL_normal = c("pnca")
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geneL_na = c("gid", "rpob")
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geneL_ppi2 = c("alr", "embb", "katg", "rpob")
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merged_df3 = as.data.frame(merged_df3)
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corr_plotdf = corr_data_extract(merged_df3
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, gene = gene
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, drug = drug
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, extract_scaled_cols = F)
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aff_dist_cols = colnames(corr_plotdf)[grep("Dist", colnames(corr_plotdf))]
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static_cols = c("Log10(MAF)"
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, "Log10(OR)")
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static_cols = c("Log10(MAF)")
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#, "Log10(OR)")
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############################################################
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#=============================================
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# Creating masked df for affinity data
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#=============================================
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corr_affinity_df = corr_plotdf
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#----------------------
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# Mask affinity columns
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#-----------------------
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corr_affinity_df[corr_affinity_df["Lig-Dist"]>DistCutOff,"mCSM-lig"]=0
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corr_affinity_df[corr_affinity_df["Lig-Dist"]>DistCutOff,"mmCSM-lig"]=0
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if (tolower(gene)%in%geneL_ppi2){
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corr_affinity_df[corr_affinity_df["PPI-Dist"]>DistCutOff,"mCSM-PPI2"]=0
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}
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# if (tolower(gene)%in%geneL_na){
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# corr_affinity_df[corr_affinity_df["NA-Dist"]>DistCutOff,"mCSM-NA"]=0
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# }
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# count 0
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#res <- colSums(corr_affinity_df==0)/nrow(corr_affinity_df)*100
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unmasked_vals <- nrow(corr_affinity_df) - colSums(corr_affinity_df==0)
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unmasked_vals
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##########################################################
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#================
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# Stability
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#================
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corr_ps_colnames = c(static_cols
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, "DUET"
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, "FoldX"
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@ -54,14 +91,13 @@ corr_ps_colnames = c(static_cols
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, aff_dist_cols
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, "dst_mode")
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corr_df_ps = corr_plotdf[, corr_ps_colnames]
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complete_obs_ps = nrow(corr_df_ps) - sum(is.na(corr_df_ps$`Log(OR)`))
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color_coln = which(colnames(corr_df_ps) == "dst_mode")
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corr_end = color_coln-1
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# Plot #1
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plot_corr_df_ps = my_gg_pairs(corr_df_ps)
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plot_corr_df_ps = my_gg_pairs(corr_df_ps, plot_title="Stability features")
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##########################################################
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#================
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# Conservation
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#================
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corr_conservation_cols = c( static_cols
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, "ConSurf"
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, "SNAP2"
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@ -71,74 +107,66 @@ corr_conservation_cols = c( static_cols
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)
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corr_df_cons = corr_plotdf[, corr_conservation_cols]
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complete_obs_cons = nrow(corr_df_cons) - sum(is.na(corr_df_cons$`Log(OR)`))
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color_coln = which(colnames(corr_df_cons) == "dst_mode")
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corr_end = color_coln-1
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# Plot #2
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plot_corr_df_cons = my_gg_pairs(corr_df_cons, plot_title="Conservation features")
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#my_gg_pairs(corr_df_cons)
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plot_corr_df_cons = my_gg_pairs(corr_df_cons)
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##########################################################
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#================
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# Affinity: lig, ppi and na as applicable
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#================
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#corr_df_lig = corr_plotdf[corr_plotdf["Lig-Dist"]<DistCutOff,]
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common_aff_colnames = c("mCSM-lig"
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, "mmCSM-lig")
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if (tolower(gene)%in%geneL_normal){
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aff_colnames = common_aff_colnames
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}
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if (tolower(gene)%in%geneL_ppi2){
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aff_colnames = c(common_aff_colnames, "mCSM-PPI2")
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}
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corr_df_lig = corr_plotdf[corr_plotdf["Lig-Dist"]<DistCutOff,]
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corr_lig_colnames = c(static_cols
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, "mCSM-lig"
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, "mmCSM-lig"
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, "dst_mode")
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if (tolower(gene)%in%geneL_na){
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aff_colnames = c(common_aff_colnames, "mCSM-NA")
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}
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corr_df_lig = corr_plotdf[, corr_lig_colnames]
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# building ffinal affinity colnames for correlation
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corr_aff_colnames = c(static_cols
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, aff_colnames
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, "dst_mode") # imp
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corr_df_aff = corr_affinity_df[, corr_aff_colnames]
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colnames(corr_df_aff)
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complete_obs_lig = nrow(corr_df_lig) - sum(is.na(corr_df_lig$`Log(OR)`))
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color_coln = which(colnames(corr_df_lig) == "dst_mode")
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corr_end = color_coln-1
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# Plot #3
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plot_corr_df_aff = my_gg_pairs(corr_df_aff, plot_title="Affinity features", tt_args_size = 4, gp_args_size =4)
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#my_gg_pairs(corr_df_lig)
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plot_corr_df_lig = my_gg_pairs(corr_df_lig)
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#=============
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# combine
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#=============
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corr_df_ppi2 = corr_plotdf[corr_plotdf["PPI-Dist"]<DistCutOff,]
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corr_ppi2_colnames = c(static_cols
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, "mCSM-PPI2"
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, "dst_mode"
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)
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corr_df_ppi2 = corr_plotdf[, corr_ppi2_colnames]
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complete_obs_ppi2 = nrow(corr_df_ppi2) - sum(is.na(corr_df_ppi2$`Log(OR)`))
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color_coln = which(colnames(corr_df_ppi2) == "dst_mode")
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corr_end = color_coln-1
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#png("/home/tanu/tmp/gg_pairs_all.png", height = 6, width=11.75, unit="in",res=300)
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png(paste0(outdir_images
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,tolower(gene)
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,"_CorrAB.png"), height = 6, width=11.75, unit="in",res=300)
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# NOTE: DELETE LOG OR FROM CORRELATION PLOTS!!!!!
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# NOTE: ALSO MAYBE DELETE DISTANCES AS WELL
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# NOTE: http://ggobi.github.io/ggally/reference/ggally_cor.html
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# "***" if the p-value is < 0.001
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# "**" if the p-value is < 0.01
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# "*" if the p-value is < 0.05
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# "." if the p-value is < 0.10
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# "" otherwise
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#
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# Plot #4
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#my_gg_pairs(corr_df_ppi2)
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plot_corr_df_ppi2 = my_gg_pairs(corr_df_ppi2)
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# corr_df_na = corr_df_na[corr_df_na["NA-Dist"]<DistCutOff,]
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# corr_na_colnames = c(static_cols
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# , "mCSM-NA"
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# , "dst_mode"
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# )
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#
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# corr_df_na = corr_plotdf[, corr_na_colnames]
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# complete_obs_na = nrow(corr_df_na) - sum(is.na(corr_df_na$`Log(OR)`))
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# color_coln = which(colnames(corr_df_na) == "dst_mode")
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# corr_end = color_coln-1
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#
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# # Plot #5
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# #my_gg_pairs(corr_df_na)
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# plot_corr_df_na = my_gg_pairs(corr_df_na)
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png("/tmp/gg_pairs_all.png", height = 8, width=11.75, unit="in",res=300)
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cowplot::plot_grid(ggmatrix_gtable(plot_corr_df_ps),ggmatrix_gtable(plot_corr_df_cons),
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ggmatrix_gtable(plot_corr_df_lig),ggmatrix_gtable(plot_corr_df_ppi2),
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nrow=2, ncol=2, rel_heights = 7,7,3,3)
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cowplot::plot_grid(ggmatrix_gtable(plot_corr_df_ps),
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ggmatrix_gtable(plot_corr_df_cons),
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# ggmatrix_gtable(plot_corr_df_aff),
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# nrow=1, ncol=3, rel_heights = 7,7,3
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nrow=1,
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#rel_heights = 1,1
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labels = "AUTO",
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label_size = 12)
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dev.off()
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# affinity corr
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#png("/home/tanu/tmp/gg_pairs_affinity.png", height =7, width=7, unit="in",res=300)
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png(paste0(outdir_images
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,tolower(gene)
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,"_CorrC.png"), height =7, width=7, unit="in",res=300)
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cowplot::plot_grid(ggmatrix_gtable(plot_corr_df_aff),
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labels = "C",
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label_size = 12)
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dev.off()
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