268 lines
8.4 KiB
R
268 lines
8.4 KiB
R
#################
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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$param_type)
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genomics_param = c("Log10(MAF)")
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dist_genP = lf_bp2(lf_dist_genP
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#, p_title
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, violin_quantiles = c(0.5), monochrome = F)
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#-------------------
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# Genomics data plot
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#-------------------
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genomics_dataP = lf_dist_genP[lf_dist_genP$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$param_type)
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genomicsP = lf_bp2(genomics_dataP
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#, p_title = ""
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, violin_quantiles = c(0.5), monochrome = F)
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genomicsP
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#check
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wilcox.test(wf_dist_genP$`Log10(MAF)`[wf_dist_genP$mutation_info_labels=="R"]
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, wf_dist_genP$`Log10(MAF)`[wf_dist_genP$mutation_info_labels=="S"], paired = FALSE)
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tapply(wf_dist_genP$`Log10(MAF)`, wf_dist_genP$mutation_info_labels, summary)
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#---------------------------------------
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# Distance data plot: not genomics data
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#---------------------------------------
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dist_dataP = lf_dist_genP[!lf_dist_genP$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$param_type)
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distanceP = lf_bp2(dist_dataP
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#, p_title = ""
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, violin_quantiles = c(0.5), monochrome = F)
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distanceP
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# check
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wilcox.test(wf_dist_genP$`PPI Dist(Å)`[wf_dist_genP$mutation_info_labels=="R"]
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, wf_dist_genP$`PPI Dist(Å)`[wf_dist_genP$mutation_info_labels=="S"], paired = FALSE)
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wilcox.test(wf_dist_genP$`Lig Dist(Å)`[wf_dist_genP$mutation_info_labels=="R"]
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, wf_dist_genP$`Lig Dist(Å)`[wf_dist_genP$mutation_info_labels=="S"], paired = FALSE)
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tapply(wf_dist_genP$`PPI Dist(Å)`, wf_dist_genP$mutation_info_labels, summary)
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tapply(wf_dist_genP$`Lig Dist(Å)`, wf_dist_genP$mutation_info_labels, summary)
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#==============
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# Plot:DUET
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#==============
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lf_duetP = all_dm_om_df[['lf_duet']]
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#lf_duetP = lf_duet[!lf_duet$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$param_type)
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proveanP = lf_bp2(lf_proveanP
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#, p_title = ""
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, violin_quantiles = c(0.5), monochrome = F)
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#==============
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# Plot:SNAP2
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#==============
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lf_snap2P = all_dm_om_df[['lf_snap2']]
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#lf_snap2P = lf_snap2[!lf_snap2$param_type%in%c(static_colsP),]
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table(lf_snap2P$param_type)
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lf_snap2P$param_type = factor(lf_snap2P$param_type)
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table(lf_snap2P$param_type)
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snap2P = lf_bp2(lf_snap2P
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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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#================
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# Plot: mCSM-lig
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#================
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lf_mcsm_ligP = all_dm_om_df[['lf_mcsm_lig']]
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#lf_mcsm_ligP = lf_mcsm_lig[!lf_mcsm_lig$param_type%in%c(static_colsP),]
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table(lf_mcsm_ligP$param_type)
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lf_mcsm_ligP$param_type = factor(lf_mcsm_ligP$param_type)
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table(lf_mcsm_ligP$param_type)
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mcsmligP = lf_bp2(lf_mcsm_ligP
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#, p_title = ""
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, violin_quantiles = c(0.5), monochrome = F
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, dot_transparency = 1)
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#=================
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# Plot: mmCSM-lig2
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#=================
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lf_mmcsm_lig2P = all_dm_om_df[['lf_mmcsm_lig2']]
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#lf_mmcsm_lig2P = lf_mmcsm_lig2P[!lf_mmcsm_lig2P$param_type%in%c(static_colsP),]
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table(lf_mmcsm_lig2P$param_type)
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lf_mmcsm_lig2P$param_type = factor(lf_mmcsm_lig2P$param_type)
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table(lf_mmcsm_lig2P$param_type)
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mcsmlig2P = lf_bp2(lf_mmcsm_lig2P
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#, p_title = ""
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, violin_quantiles = c(0.5), monochrome = F
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, dot_transparency = 1)
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mcsmlig2P
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#================
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# Plot: mCSM-ppi2
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#================
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if (tolower(gene)%in%geneL_ppi2){
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lf_mcsm_ppi2P = all_dm_om_df[['lf_mcsm_ppi2']]
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#lf_mcsm_ppi2P = lf_mcsm_ppi2[!lf_mcsm_ppi2$param_type%in%c(static_colsP),]
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table(lf_mcsm_ppi2P$param_type)
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lf_mcsm_ppi2P$param_type = factor(lf_mcsm_ppi2P$param_type)
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table(lf_mcsm_ppi2P$param_type)
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mcsmppi2P = lf_bp2(lf_mcsm_ppi2P
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#, p_title = ""
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, violin_quantiles = c(0.5), monochrome = F
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, dot_transparency = 1)
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}
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#==============
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# Plot: mCSM-NA
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#==============
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if (tolower(gene)%in%geneL_na){
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lf_mcsm_naP = all_dm_om_df[['lf_mcsm_na']]
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#lf_mcsm_naP = lf_mcsm_na[!lf_mcsm_na$param_type%in%c(static_colsP),]
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table(lf_mcsm_naP$param_type)
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lf_mcsm_naP$param_type = factor(lf_mcsm_naP$param_type)
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table(lf_mcsm_naP$param_type)
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mcsmnaP = lf_bp2(lf_mcsm_naP
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#, p_title = ""
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, violin_quantiles = c(0.5), monochrome = F
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, dot_transparency = 1)
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}
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######################################
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# Outplot with stats
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######################################
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outdir_images = paste0("~/git/Writing/thesis/images/results/", tolower(gene), "/")
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dm_om_combinedP = paste0(outdir_images
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,tolower(gene)
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,"_dm_om_all.svg" )
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cat("DM OM plots with stats:", dm_om_combinedP)
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svg(dm_om_combinedP, width = 32, height = 18)
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cowplot::plot_grid(
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cowplot::plot_grid(duetP, foldxP, deepddgP, dynamut2P, genomicsP, distanceP
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, nrow=1
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, rel_widths = c(1/7, 1/7,1/7,1/7, 1/7, 1.75/7)),
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#, rel_widths = c(1/8, 1/8,1/8,1/8, 1/8, 2.75/8)), # for 3 distances
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cowplot::plot_grid(consurfP, proveanP, snap2P
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, mcsmligP
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, mcsmlig2P
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, mcsmppi2P
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#, mcsmnaP
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, nrow=1),
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nrow=2)
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dev.off()
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#foo = lf_consurfP
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# proveanP = lf_bp2(lf_proveanP, colour_categ = "mutation_info_labels"
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# , p_title = paste0("Evolutionary conservation")
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# , dot_transparency = 1
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# , violin_quantiles = c(0.5), monochrome = F)
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#
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# proveanP
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