added pnca plots
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210
scripts/plotting/plotting_thesis/pnca/gg_pairs_all_pnca.R
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210
scripts/plotting/plotting_thesis/pnca/gg_pairs_all_pnca.R
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source("~/git/LSHTM_analysis/config/pnca.R")
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source("~/git/LSHTM_analysis/scripts/plotting/get_plotting_dfs.R")
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#=======
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# output
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#=======
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outdir_images = paste0("~/git/Writing/thesis/images/results/", tolower(gene), "/")
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cat("plots will output to:", outdir_images)
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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', # 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 = "centre",
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title_args=list(size=tt_args_size, colour="black"),#2.5
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group_args=list(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=0.125),
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combo = wrap("dot",
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alpha = 0.7,
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size=0.125)
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),
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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=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=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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)
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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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, "mCSM-DUET"
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, "FoldX"
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, "DeepDDG"
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, "Dynamut2"
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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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# Plot #1
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plot_corr_df_ps = my_gg_pairs(corr_df_ps, plot_title="Stability estimates")
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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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, "PROVEAN"
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#, aff_dist_cols
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, "dst_mode"
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)
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corr_df_cons = corr_plotdf[, corr_conservation_cols]
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# Plot #2
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plot_corr_df_cons = my_gg_pairs(corr_df_cons, plot_title="Conservation estimates")
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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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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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# 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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# Plot #3
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plot_corr_df_aff = my_gg_pairs(corr_df_aff
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, plot_title="Affinity estimates"
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#, tt_args_size = 4
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#, gp_args_size = 4
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)
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#### Combine plots #####
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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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#
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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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#
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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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#
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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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#### Combine A ####
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png(paste0(outdir_images
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,tolower(gene)
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,"_CorrA.png"), height =8, width=8, unit="in",res=300)
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cowplot::plot_grid(ggmatrix_gtable(plot_corr_df_ps),
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labels = "A",
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label_size = 12)
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dev.off()
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#### Combine B+C ####
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# B + C
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png(paste0(outdir_images
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,tolower(gene)
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,"_CorrBC.png"), height = 6, width=11.75, unit="in",res=300)
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cowplot::plot_grid(ggmatrix_gtable(plot_corr_df_cons),
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ggmatrix_gtable(plot_corr_df_aff),
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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 = c("B", "C"),
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label_size = 12)
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dev.off()
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318
scripts/plotting/plotting_thesis/pnca/pnca_dm_om_plots.R
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scripts/plotting/plotting_thesis/pnca/pnca_dm_om_plots.R
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#################
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# Numbers
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##################
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all_dm_om_df = dm_om_wf_lf_data(df = merged_df3, gene = gene)
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#
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# lf_duet = all_dm_om_df[['lf_duet']]
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# table(lf_duet$param_type)
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################################################################
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#======================
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# Data: Dist+Genomics
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#======================
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lf_dist_genP = all_dm_om_df[['lf_dist_gen']]
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wf_dist_genP = all_dm_om_df[['wf_dist_gen']]
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levels(lf_dist_genP$param_type)
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#lf_dist_genP$param_type <- factor(lf_dist_genP$param_type, levels=c("Log10(MAF)", "Lig Dist(Å)", "PPI Dist(Å)"))
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table(lf_dist_genP$param_type)
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genomics_param = c("Log10(MAF)")
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dist_genP = lf_bp2(lf_dist_genP
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#, p_title
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, violin_quantiles = c(0.5), monochrome = F)
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#dist_genP
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#-------------------
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# Genomics data plot
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#-------------------
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genomics_dataP = lf_dist_genP[lf_dist_genP$param_type%in%genomics_param,]
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genomics_dataP$param_type = factor(genomics_dataP$param_type)
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table(genomics_dataP$param_type)
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genomicsP = lf_bp2(genomics_dataP
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#, p_title = ""
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, dot_transparency = 0.3 #0.3 default
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, violin_quantiles = c(0.5), monochrome = F)
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genomicsP
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# #check
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# wilcox.test(wf_dist_genP$`Log10(MAF)`[wf_dist_genP$mutation_info_labels=="R"]
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# , wf_dist_genP$`Log10(MAF)`[wf_dist_genP$mutation_info_labels=="S"], paired = FALSE)
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#
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# tapply(wf_dist_genP$`Log10(MAF)`, wf_dist_genP$mutation_info_labels, summary)
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#-------------------
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# Distance data plot:
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#--------------------
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# not genomics
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dist_dataP = lf_dist_genP[!lf_dist_genP$param_type%in%genomics_param,]
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dist_dataP$param_type = factor(dist_dataP$param_type)
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table(dist_dataP$param_type)
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levels(dist_dataP$param_type)
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# relevel factor to control ordering of appearance of plot
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dist_dataP$param_type <-relevel(dist_dataP$param_type, "Lig Dist(Å)" )
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table(dist_dataP$param_type)
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levels(dist_dataP$param_type)
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distanceP = lf_bp2(dist_dataP
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#, p_title = ""
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, violin_quantiles = c(0.5), monochrome = F)
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distanceP
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# # check
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# wilcox.test(wf_dist_genP$`PPI Dist(Å)`[wf_dist_genP$mutation_info_labels=="R"]
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# , wf_dist_genP$`PPI Dist(Å)`[wf_dist_genP$mutation_info_labels=="S"], paired = FALSE)
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#
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# wilcox.test(wf_dist_genP$`Lig Dist(Å)`[wf_dist_genP$mutation_info_labels=="R"]
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# , wf_dist_genP$`Lig Dist(Å)`[wf_dist_genP$mutation_info_labels=="S"], paired = FALSE)
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#
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# tapply(wf_dist_genP$`PPI Dist(Å)`, wf_dist_genP$mutation_info_labels, summary)
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#
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# tapply(wf_dist_genP$`Lig Dist(Å)`, wf_dist_genP$mutation_info_labels, summary)
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#-------------------
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# Distance data plot: LigDist
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#--------------------
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levels(dist_dataP$param_type)[[1]]
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#Lig Dist(Å), PPI Dist(Å)
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dist_data_lig = dist_dataP[dist_dataP$param_type%in%c(levels(dist_dataP$param_type)[[1]]),]
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dist_data_lig$param_type = factor(dist_data_lig$param_type)
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table(dist_data_lig$param_type)
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levels(dist_data_lig$param_type)
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distanceP_lig = lf_bp2(dist_data_lig
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#, p_title = ""
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, violin_quantiles = c(0.5), monochrome = F)
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distanceP_lig
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if (tolower(gene)%in%geneL_ppi2){
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#-------------------
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# Distance data plot: LigDist
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#--------------------
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levels(dist_dataP$param_type)[[2]]
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#Lig Dist(Å), PPI Dist(Å)
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dist_data_ppi2 = dist_dataP[dist_dataP$param_type%in%c(levels(dist_dataP$param_type)[[2]]),]
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dist_data_ppi2$param_type = factor(dist_data_ppi2$param_type)
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table(dist_data_ppi2$param_type)
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levels(dist_data_ppi2$param_type)
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distanceP_ppi2 = lf_bp2(dist_data_ppi2
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#, p_title = ""
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, violin_quantiles = c(0.5)
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, dot_transparency = 0.2
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, monochrome = F)
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distanceP_ppi2
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}
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if (tolower(gene)%in%geneL_na){
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#-------------------
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# Distance data plot: NADist
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#--------------------
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levels(dist_dataP$param_type)[[2]]
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#Lig Dist(Å), PPI Dist(Å)
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dist_data_na = dist_dataP[dist_dataP$param_type%in%c(levels(dist_dataP$param_type)[[2]]),]
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dist_data_na$param_type = factor(dist_data_na$param_type)
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table(dist_data_na$param_type)
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levels(dist_data_na$param_type)
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distanceP_na = lf_bp2(dist_data_na
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#, p_title = ""
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, violin_quantiles = c(0.5), monochrome = F)
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distanceP_na
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}
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#==============
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# Plot:DUET
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#==============
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lf_duetP = all_dm_om_df[['lf_duet']]
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#lf_duetP = lf_duet[!lf_duet$param_type%in%c(static_colsP),]
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table(lf_duetP$param_type)
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lf_duetP$param_type = factor(lf_duetP$param_type)
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table(lf_duetP$param_type)
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duetP = lf_bp2(lf_duetP
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#, p_title = ""
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, violin_quantiles = c(0.5), monochrome = F
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, dot_transparency = 0.2)
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#==============
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# Plot:FoldX
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#==============
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lf_foldxP = all_dm_om_df[['lf_foldx']]
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#lf_foldxP = lf_foldx[!lf_foldx$param_type%in%c(static_colsP),]
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table(lf_foldxP$param_type)
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lf_foldxP$param_type = factor(lf_foldxP$param_type)
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table(lf_foldxP$param_type)
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foldxP = lf_bp2(lf_foldxP
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#, p_title = ""
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, violin_quantiles = c(0.5), monochrome = F
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, dot_transparency = 0.1)
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#==============
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# Plot:DeepDDG
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#==============
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lf_deepddgP = all_dm_om_df[['lf_deepddg']]
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#lf_deepddgP = lf_deepddg[!lf_deepddg$param_type%in%c(static_colsP),]
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table(lf_deepddgP$param_type)
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lf_deepddgP$param_type = factor(lf_deepddgP$param_type)
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table(lf_deepddgP$param_type)
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deepddgP = lf_bp2(lf_deepddgP
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#, p_title = ""
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, violin_quantiles = c(0.5), monochrome = F
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, dot_transparency = 0.2)
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deepddgP
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#==============
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# Plot: Dynamut2
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#==============
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lf_dynamut2P = all_dm_om_df[['lf_dynamut2']]
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#lf_dynamut2P = lf_dynamut2[!lf_dynamut2$param_type%in%c(static_colsP),]
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table(lf_dynamut2P$param_type)
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lf_dynamut2P$param_type = factor(lf_dynamut2P$param_type)
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table(lf_dynamut2P$param_type)
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dynamut2P = lf_bp2(lf_dynamut2P
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#, p_title = ""
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, violin_quantiles = c(0.5), monochrome = F
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, dot_transparency = 0.2)
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#==============
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# Plot:ConSurf
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#==============
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lf_consurfP = all_dm_om_df[['lf_consurf']]
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#lf_consurfP = lf_consurf[!lf_consurf$param_type%in%c(static_colsP),]
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table(lf_consurfP$param_type)
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lf_consurfP$param_type = factor(lf_consurfP$param_type)
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table(lf_consurfP$param_type)
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consurfP = lf_bp2(lf_consurfP
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#, p_title = ""
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, violin_quantiles = c(0.5), monochrome = F)
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#==============
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# Plot:PROVEAN
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#==============
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lf_proveanP = all_dm_om_df[['lf_provean']]
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#lf_proveanP = lf_provean[!lf_provean$param_type%in%c(static_colsP),]
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table(lf_proveanP$param_type)
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lf_proveanP$param_type = factor(lf_proveanP$param_type)
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table(lf_proveanP$param_type)
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proveanP = lf_bp2(lf_proveanP
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#, p_title = ""
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, violin_quantiles = c(0.5), monochrome = F)
|
||||
|
||||
#==============
|
||||
# Plot:SNAP2
|
||||
#==============
|
||||
lf_snap2P = all_dm_om_df[['lf_snap2']]
|
||||
#lf_snap2P = lf_snap2[!lf_snap2$param_type%in%c(static_colsP),]
|
||||
table(lf_snap2P$param_type)
|
||||
lf_snap2P$param_type = factor(lf_snap2P$param_type)
|
||||
table(lf_snap2P$param_type)
|
||||
|
||||
snap2P = lf_bp2(lf_snap2P
|
||||
#, p_title = ""
|
||||
, violin_quantiles = c(0.5), monochrome = F)
|
||||
|
||||
|
||||
############################################################################
|
||||
#================
|
||||
# Plot: mCSM-lig
|
||||
#================
|
||||
lf_mcsm_ligP = all_dm_om_df[['lf_mcsm_lig']]
|
||||
#lf_mcsm_ligP = lf_mcsm_lig[!lf_mcsm_lig$param_type%in%c(static_colsP),]
|
||||
table(lf_mcsm_ligP$param_type)
|
||||
lf_mcsm_ligP$param_type = factor(lf_mcsm_ligP$param_type)
|
||||
table(lf_mcsm_ligP$param_type)
|
||||
|
||||
mcsmligP = lf_bp2(lf_mcsm_ligP
|
||||
#, p_title = ""
|
||||
, violin_quantiles = c(0.5), monochrome = F
|
||||
, dot_transparency = 0.8)
|
||||
|
||||
mcsmligP
|
||||
#=================
|
||||
# Plot: mmCSM-lig2
|
||||
#=================
|
||||
lf_mmcsm_lig2P = all_dm_om_df[['lf_mmcsm_lig2']]
|
||||
#lf_mmcsm_lig2P = lf_mmcsm_lig2P[!lf_mmcsm_lig2P$param_type%in%c(static_colsP),]
|
||||
table(lf_mmcsm_lig2P$param_type)
|
||||
lf_mmcsm_lig2P$param_type = factor(lf_mmcsm_lig2P$param_type)
|
||||
table(lf_mmcsm_lig2P$param_type)
|
||||
|
||||
mcsmlig2P = lf_bp2(lf_mmcsm_lig2P
|
||||
#, p_title = ""
|
||||
, violin_quantiles = c(0.5), monochrome = F
|
||||
, dot_transparency = 0.8)
|
||||
|
||||
mcsmlig2P
|
||||
|
||||
#================
|
||||
# Plot: mCSM-ppi2
|
||||
#================
|
||||
if (tolower(gene)%in%geneL_ppi2){
|
||||
lf_mcsm_ppi2P = all_dm_om_df[['lf_mcsm_ppi2']]
|
||||
#lf_mcsm_ppi2P = lf_mcsm_ppi2[!lf_mcsm_ppi2$param_type%in%c(static_colsP),]
|
||||
table(lf_mcsm_ppi2P$param_type)
|
||||
lf_mcsm_ppi2P$param_type = factor(lf_mcsm_ppi2P$param_type)
|
||||
table(lf_mcsm_ppi2P$param_type)
|
||||
|
||||
mcsmppi2P = lf_bp2(lf_mcsm_ppi2P
|
||||
#, p_title = ""
|
||||
, violin_quantiles = c(0.5), monochrome = F
|
||||
, dot_transparency = 0.3)
|
||||
|
||||
}
|
||||
|
||||
#==============
|
||||
# Plot: mCSM-NA
|
||||
#==============
|
||||
if (tolower(gene)%in%geneL_na){
|
||||
lf_mcsm_naP = all_dm_om_df[['lf_mcsm_na']]
|
||||
#lf_mcsm_naP = lf_mcsm_na[!lf_mcsm_na$param_type%in%c(static_colsP),]
|
||||
table(lf_mcsm_naP$param_type)
|
||||
lf_mcsm_naP$param_type = factor(lf_mcsm_naP$param_type)
|
||||
table(lf_mcsm_naP$param_type)
|
||||
|
||||
mcsmnaP = lf_bp2(lf_mcsm_naP
|
||||
#, p_title = ""
|
||||
, violin_quantiles = c(0.5), monochrome = F
|
||||
, dot_transparency = 0.4)
|
||||
|
||||
}
|
||||
|
||||
######################################
|
||||
# Outplot with stats
|
||||
######################################
|
||||
# outdir_images = paste0("~/git/Writing/thesis/images/results/", tolower(gene), "/")
|
||||
#
|
||||
# dm_om_combinedP = paste0(outdir_images
|
||||
# ,tolower(gene)
|
||||
# ,"_dm_om_all.svg" )
|
||||
#
|
||||
# cat("DM OM plots with stats:", dm_om_combinedP)
|
||||
# svg(dm_om_combinedP, width = 32, height = 18)
|
||||
# cowplot::plot_grid(
|
||||
# cowplot::plot_grid(duetP, foldxP, deepddgP, dynamut2P, genomicsP, distanceP
|
||||
# , nrow=1
|
||||
# , rel_widths = c(1/7, 1/7,1/7,1/7, 1/7, 1.75/7)),
|
||||
# #, rel_widths = c(1/8, 1/8,1/8,1/8, 1/8, 2.75/8)), # for 3 distances
|
||||
# cowplot::plot_grid(consurfP, proveanP, snap2P
|
||||
# , mcsmligP
|
||||
# , mcsmlig2P
|
||||
# , mcsmppi2P
|
||||
# #, mcsmnaP
|
||||
# , nrow=1),
|
||||
# nrow=2)
|
||||
#
|
||||
# dev.off()
|
||||
|
||||
|
||||
176
scripts/plotting/plotting_thesis/pnca/pnca_dm_om_plots_layout.R
Normal file
176
scripts/plotting/plotting_thesis/pnca/pnca_dm_om_plots_layout.R
Normal file
|
|
@ -0,0 +1,176 @@
|
|||
# source dm_om_plots.R
|
||||
source("/home/tanu/git/LSHTM_analysis/scripts/plotting/plotting_thesis/pnca/pnca_dm_om_plots.R")
|
||||
|
||||
##### plots to combine ####
|
||||
duetP
|
||||
foldxP
|
||||
deepddgP
|
||||
dynamut2P
|
||||
genomicsP
|
||||
consurfP
|
||||
proveanP
|
||||
snap2P
|
||||
mcsmligP
|
||||
mcsmlig2P
|
||||
#mcsmppi2P
|
||||
|
||||
# Plot labels
|
||||
tit1 = "Stability changes"
|
||||
tit2 = "Genomic measure"
|
||||
tit3 = "Distance to partners"
|
||||
tit4 = "Evolutionary Conservation"
|
||||
tit5 = "Affinity changes"
|
||||
pt_size = 30
|
||||
|
||||
theme_georgia <- function(...) {
|
||||
theme_gray(base_family = "sans", ...) +
|
||||
theme(plot.title = element_text(face = "bold"))
|
||||
}
|
||||
|
||||
|
||||
title_theme <- calc_element("plot.title", theme_georgia())
|
||||
|
||||
pt1 = ggdraw() +
|
||||
draw_label(
|
||||
tit1,
|
||||
fontfamily = title_theme$family,
|
||||
fontface = title_theme$face,
|
||||
#size = title_theme$size
|
||||
size = pt_size
|
||||
)
|
||||
|
||||
pt2 = ggdraw() +
|
||||
draw_label(
|
||||
tit2,
|
||||
fontfamily = title_theme$family,
|
||||
fontface = title_theme$face,
|
||||
size = pt_size
|
||||
)
|
||||
|
||||
pt3 = ggdraw() +
|
||||
draw_label(
|
||||
tit3,
|
||||
fontfamily = title_theme$family,
|
||||
fontface = title_theme$face,
|
||||
size = pt_size
|
||||
)
|
||||
|
||||
pt4 = ggdraw() +
|
||||
draw_label(
|
||||
tit4,
|
||||
fontfamily = title_theme$family,
|
||||
fontface = title_theme$face,
|
||||
size = pt_size
|
||||
)
|
||||
|
||||
|
||||
pt5 = ggdraw() +
|
||||
draw_label(
|
||||
tit5,
|
||||
fontfamily = title_theme$family,
|
||||
fontface = title_theme$face,
|
||||
size = pt_size
|
||||
)
|
||||
|
||||
#======================
|
||||
# Output plot function
|
||||
#======================
|
||||
OutPlot_dm_om = function(x){
|
||||
|
||||
# dist b/w plot title and plot
|
||||
relH_tp = c(0.08, 0.92)
|
||||
|
||||
my_label_size = 25
|
||||
#----------------
|
||||
# Top panel
|
||||
#----------------
|
||||
top_panel = cowplot::plot_grid(
|
||||
cowplot::plot_grid(pt1,
|
||||
cowplot::plot_grid(duetP, foldxP, deepddgP, dynamut2P
|
||||
, nrow = 1
|
||||
, labels = c("A", "B", "C", "D")
|
||||
, label_size = my_label_size)
|
||||
, ncol = 1
|
||||
, rel_heights = relH_tp
|
||||
),
|
||||
NULL,
|
||||
cowplot::plot_grid(pt2,
|
||||
cowplot::plot_grid(genomicsP
|
||||
, nrow = 1
|
||||
, labels = c("E")
|
||||
, label_size = my_label_size)
|
||||
, ncol = 1
|
||||
, rel_heights = relH_tp
|
||||
),
|
||||
NULL,
|
||||
cowplot::plot_grid(pt3,
|
||||
cowplot::plot_grid( #distanceP
|
||||
distanceP_lig
|
||||
#, distanceP_ppi2
|
||||
, nrow = 1
|
||||
, labels = c("F")
|
||||
, label_size = my_label_size)
|
||||
, ncol = 1
|
||||
, rel_heights = relH_tp
|
||||
),
|
||||
nrow = 1,
|
||||
rel_widths = c(2/6, 0, 0.5/6, 0, 0.5/6)
|
||||
)
|
||||
|
||||
#----------------
|
||||
# Bottom panel
|
||||
#----------------
|
||||
bottom_panel = cowplot::plot_grid(
|
||||
cowplot::plot_grid(pt4,
|
||||
cowplot::plot_grid(consurfP, proveanP, snap2P
|
||||
, nrow = 1
|
||||
, labels = c("H", "I", "J")
|
||||
, label_size = my_label_size)
|
||||
, ncol = 1
|
||||
, rel_heights =relH_tp
|
||||
),NULL,
|
||||
cowplot::plot_grid(pt5,
|
||||
cowplot::plot_grid(mcsmligP
|
||||
, mcsmlig2P
|
||||
#, mcsmppi2P
|
||||
, nrow = 1
|
||||
, labels = c("K", "L")
|
||||
, label_size = my_label_size)
|
||||
, ncol = 1
|
||||
, rel_heights = relH_tp
|
||||
),NULL,
|
||||
nrow = 1,
|
||||
rel_widths = c(3/6,0.1/6,3/6, 0.1/6 )
|
||||
)
|
||||
|
||||
#-------------------------------
|
||||
# combine: Top and Bottom panel
|
||||
#-------------------------------
|
||||
cowplot::plot_grid (top_panel, bottom_panel
|
||||
, nrow =2
|
||||
, rel_widths = c(1, 1)
|
||||
, align = "hv")
|
||||
}
|
||||
|
||||
#=====================
|
||||
# OutPlot: svg and png
|
||||
#======================
|
||||
dm_om_combinedP = paste0(outdir_images
|
||||
,tolower(gene)
|
||||
,"_dm_om_all.svg")
|
||||
|
||||
cat("DM OM plots with stats:", dm_om_combinedP)
|
||||
svg(dm_om_combinedP, width = 32, height = 18)
|
||||
|
||||
OutPlot_dm_om()
|
||||
dev.off()
|
||||
|
||||
|
||||
dm_om_combinedP_png = paste0(outdir_images
|
||||
,tolower(gene)
|
||||
,"_dm_om_all.png")
|
||||
cat("DM OM plots with stats:", dm_om_combinedP_png)
|
||||
png(dm_om_combinedP_png, width = 32, height = 18, units = "in", res = 300)
|
||||
|
||||
OutPlot_dm_om()
|
||||
dev.off()
|
||||
Loading…
Add table
Add a link
Reference in a new issue