stability and cons revised bp out
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f244741e83
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cd9b1ad245
2 changed files with 222 additions and 125 deletions
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@ -321,6 +321,120 @@ sensP
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# coord_flip() + scale_x_reverse()
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# coord_flip() + scale_x_reverse()
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# sensP2
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# sensP2
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##############################################################
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#===================
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# Stability
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#===================
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# duetP
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duetP = stability_count_bp(plotdf = df3
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, df_colname = "duet_outcome"
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, leg_title = "mCSM-DUET"
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#, label_categories = labels_duet
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, yaxis_title = "Number of nsSNPs"
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, leg_position = "none"
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, subtitle_text = "mCSM-DUET"
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, bar_fill_values = c("#F8766D", "#00BFC4")
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, subtitle_colour= "black"
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, sts = 10
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, lts = 8
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, ats = 12
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, als = 11
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, ltis = 11
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, geom_ls = 2.5
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)
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duetP
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# foldx
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foldxP = stability_count_bp(plotdf = df3
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, df_colname = "foldx_outcome"
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#, leg_title = "FoldX"
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#, label_categories = labels_foldx
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, yaxis_title = ""
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, leg_position = "none"
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, subtitle_text = "FoldX"
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, bar_fill_values = c("#F8766D", "#00BFC4")
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, sts = 10
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, lts = 8
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, ats = 12
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, als = 11
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, ltis = 11
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, geom_ls = 2.5
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)
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# deepddg
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deepddgP = stability_count_bp(plotdf = df3
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, df_colname = "deepddg_outcome"
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#, leg_title = "DeepDDG"
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#, label_categories = labels_deepddg
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, yaxis_title = ""
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, leg_position = "none"
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, subtitle_text = "DeepDDG"
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, bar_fill_values = c("#F8766D", "#00BFC4")
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, sts = 10
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, lts = 8
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, ats = 12
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, als = 11
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, ltis = 11
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, geom_ls = 2.5
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)
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# deepddg
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dynamut2P = stability_count_bp(plotdf = df3
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, df_colname = "ddg_dynamut2_outcome"
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#, leg_title = "Dynamut2"
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#, label_categories = labels_ddg_dynamut2_outcome
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, yaxis_title = ""
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, leg_position = "none"
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, subtitle_text = "Dynamut2"
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, bar_fill_values = c("#F8766D", "#00BFC4")
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, sts = 10
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, lts = 8
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, ats = 12
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, als = 11
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, ltis = 11
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, geom_ls = 2.5
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)
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dynamut2P
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# provean
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proveanP = stability_count_bp(plotdf = df3
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, df_colname = "provean_outcome"
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#, leg_title = "PROVEAN"
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#, label_categories = labels_provean
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, yaxis_title = "Number of nsSNPs"
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, leg_position = "none" # top
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, subtitle_text = "PROVEAN"
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, bar_fill_values = c("#D01C8B", "#F1B6DA") # light pink and deep
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, sts = 10
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, lts = 8
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, ats = 12
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, als = 11
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, ltis = 11
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, geom_ls = 2.5
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)
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# snap2
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snap2P = stability_count_bp(plotdf = df3
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, df_colname = "snap2_outcome"
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#, leg_title = "SNAP2"
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#, label_categories = labels_snap2
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, yaxis_title = ""
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, leg_position = "none" # top
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, subtitle_text = "SNAP2"
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, bar_fill_values = c("#D01C8B", "#F1B6DA") # light pink and deep
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, sts = 10
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, lts = 8
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, ats = 12
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, als = 11
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, ltis = 11
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, geom_ls = 2.5)
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##############################################################
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##############################
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##############################
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# FIXME for other genes: ATTEMPTED to derive numbers
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# FIXME for other genes: ATTEMPTED to derive numbers
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##############################
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##############################
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@ -1,145 +1,127 @@
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duetP
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foldxP
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deepddgP
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dynamut2P
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proveanP
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snap2P
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mLigP
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mLigP
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mmLigP
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mmLigP
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posC_lig
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posC_lig
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ppi2P
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ppi2P
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posC_ppi2
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posC_ppi2
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peP
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peP
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sensP
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#========================
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# Common title settings
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#=========================
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theme_georgia <- function(...) {
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theme_georgia <- function(...) {
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theme_gray(base_family = "sans", ...) +
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theme_gray(base_family = "sans", ...) +
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theme(plot.title = element_text(face = "bold"))
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theme(plot.title = element_text(face = "bold"))
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}
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}
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title_theme <- calc_element("plot.title", theme_georgia())
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title_theme <- calc_element("plot.title", theme_georgia())
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###############################################################
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###############################################################
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common_bp_title = paste0("Sites <", DistCutOff, angstroms_symbol)
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common_bp_title = paste0("Sites <", DistCutOff, angstroms_symbol)
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# extract common legend
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# extract common legends
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# lig affinity
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common_legend_outcome = get_legend(mLigP +
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common_legend_outcome = get_legend(mLigP +
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guides(color = guide_legend(nrow = 1)) +
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guides(color = guide_legend(nrow = 1)) +
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theme(legend.position = "top"))
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theme(legend.position = "top"))
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# ###############################################################
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# stability
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# #================================
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common_legend_outcome = get_legend(duetP +
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# # Lig Affinity: outcome + site
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guides(color = guide_legend(nrow = 1)) +
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# #================================
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theme(legend.position = "top"))
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# ligT = paste0(common_bp_title, " ligand")
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# conservation
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# lig_affT = ggdraw() +
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cons_common_legend_outcome = get_legend(snap2P +
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# draw_label(
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guides(color = guide_legend(nrow = 1)) +
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# ligT,
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theme(legend.position = "top"))
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# fontfamily = title_theme$family,
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###################################################################
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# fontface = title_theme$face,
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#==================================
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# #size = title_theme$size
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# Stability+Consevation: COMBINE
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# size = 8
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#==================================
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# )
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tt_size = 10
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#----------------------------
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# stability and consv title
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#----------------------------
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tt_size = 10
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tt_stab = ggdraw() +
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draw_label(
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paste0("Stability outcome"),
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fontfamily = title_theme$family,
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fontface = title_theme$face,
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#size = title_theme$size
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size = tt_size
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)
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tt_cons = ggdraw() +
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draw_label(
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paste0("Conservation outcome"),
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fontfamily = title_theme$family,
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fontface = title_theme$face,
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size = tt_size
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)
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#----------------------
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# Output plot
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#-----------------------
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stab_cons_CLP = paste0(outdir_images
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,tolower(gene)
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,"_stab_cons_BP_CLP.png")
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print(paste0("plot filename:", stab_cons_CLP))
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png(stab_cons_CLP, units = "in", width = 10, height = 5, res = 300 )
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cowplot::plot_grid(
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cowplot::plot_grid(
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cowplot::plot_grid(
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tt_stab,
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common_legend_outcome,
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nrow = 2
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),
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cowplot::plot_grid(
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duetP,
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foldxP,
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deepddgP,
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dynamut2P,
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nrow = 1,
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labels = c("A", "B", "C", "D"),
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label_size = 12),
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nrow = 2,
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rel_heights=c(1,10)
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),
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NULL,
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cowplot::plot_grid(
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cowplot::plot_grid(
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cowplot::plot_grid(
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tt_cons,
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cons_common_legend_outcome,
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nrow = 2
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),
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cowplot::plot_grid(
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proveanP,
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snap2P,
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nrow=1,
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labels = c("E", "F"),
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align = "hv"),
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nrow = 2,
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rel_heights = c(1, 10),
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label_size = 12),
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nrow=1
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),
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rel_widths = c(2,0.15,1),
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nrow=1
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)
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dev.off()
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#################################################################
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#=======================================
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# Affinity barplots: COMBINE ALL three
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#========================================
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# #-------------
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# # Outplot
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# #-------------
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# ligaffP = paste0(outdir_images
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# ,tolower(gene)
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# ,"_lig_oc.png")
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#
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# #svg(affP, width = 20, height = 5.5)
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# print(paste0("plot filename:", ligaffP))
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# png(ligaffP, units = "in", width = 6, height = 4, res = 300 )
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# cowplot::plot_grid(cowplot::plot_grid(lig_affT,common_legend_outcome,
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# nrow = 2,
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# rel_heights = c(1,1)
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# ),
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# cowplot::plot_grid(mLigP, mmLigP, posC_lig
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# , nrow = 1
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# #, labels = c("A", "B", "C","D")
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# , rel_widths = c(1,1,1.8)
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# , align = "h"),
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# nrow = 2,
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# labels = c("A", ""),
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# label_size = 12,
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# rel_heights = c(1,8))
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# dev.off()
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# #############################################################
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# #================================
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# # PPI2 Affinity: outcome + site
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# #================================
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# ppi2T = paste0(common_bp_title, " PP-interface")
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# ppi2_affT = ggdraw() +
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# draw_label(
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# ppi2T,
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# fontfamily = title_theme$family,
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# fontface = title_theme$face,
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# #size = title_theme$size
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# size = 8
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# )
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#
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#
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# #-------------
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# # Outplot: PPI2
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# #-------------
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# ppiaffP = paste0(outdir_images
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# ,tolower(gene)
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# ,"_ppi2_oc.png")
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#
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# #svg(affP, width = 20, height = 5.5)
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# print(paste0("plot filename:", ppiaffP))
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# png(ppiaffP, units = "in", width = 6, height = 4, res = 300 )
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#
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#
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# cowplot::plot_grid(cowplot::plot_grid(ppi2_affT, common_legend_outcome,
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# nrow = 2,
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# rel_heights = c(1,1)),
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# cowplot::plot_grid(ppi2P, posC_ppi2
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# , nrow = 1
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# , rel_widths = c(1.2,1.8)
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# , align = "h"
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# , label_size = my_label_size),
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# nrow = 2,
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# labels = c("B", ""),
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# label_size = 12,
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# rel_heights = c(1,8)
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# )
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#
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# dev.off()
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# #############################################################
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#peP # pe counts
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#================================
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# PE + All position count
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#================================
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# peT_allT = ggdraw() +
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# draw_label(
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# paste0("All mutation sites"),
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# fontfamily = title_theme$family,
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# fontface = title_theme$face,
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# #size = title_theme$size
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# size = 8
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# )
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# #------------------------
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# # Outplot: lig+ppi2+pe
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# #------------------------
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# pe_allCL = paste0(outdir_images
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# ,tolower(gene)
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# ,"_pe_oc.png")
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#
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# #svg(affP, width = 20, height = 5.5)
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# print(paste0("plot filename:", pe_allCL))
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# png(pe_allCL, units = "in", width = 6, height = 4, res = 300 )
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#
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#
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# cowplot::plot_grid(peT_allT,
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# cowplot::plot_grid(peP, posC_all
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# , nrow = 1
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# , rel_widths = c(1, 2)
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# , align = "h"),
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# nrow = 2,
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# labels = c("C", "", ""),
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# label_size = 12,
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# rel_heights = c(1,8))
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#
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# dev.off()
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#===========================================
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# COMBINE ALL three
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#==========================================
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ligT = paste0(common_bp_title, " ligand")
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ligT = paste0(common_bp_title, " ligand")
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lig_affT = ggdraw() +
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lig_affT = ggdraw() +
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draw_label(
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draw_label(
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@ -150,7 +132,9 @@ lig_affT = ggdraw() +
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size = 8
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size = 8
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)
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)
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p1 = cowplot::plot_grid(cowplot::plot_grid(lig_affT,common_legend_outcome, nrow=2),
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p1 = cowplot::plot_grid(cowplot::plot_grid(lig_affT
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, common_legend_outcome
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, nrow=2),
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cowplot::plot_grid(mLigP, mmLigP, posC_lig
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cowplot::plot_grid(mLigP, mmLigP, posC_lig
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, nrow = 1
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, nrow = 1
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, rel_widths = c(1,1,1.8)
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, rel_widths = c(1,1,1.8)
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@ -217,7 +201,6 @@ mut_impact_CLP = paste0(outdir_images
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print(paste0("plot filename:", mut_impact_CLP))
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print(paste0("plot filename:", mut_impact_CLP))
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png(mut_impact_CLP, units = "in", width = w, height = h, res = 300 )
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png(mut_impact_CLP, units = "in", width = w, height = h, res = 300 )
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cowplot::plot_grid(p1, p2, p3
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cowplot::plot_grid(p1, p2, p3
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, nrow = 1
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, nrow = 1
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||||||
, labels = "AUTO"
|
, labels = "AUTO"
|
||||||
|
|
@ -270,5 +253,5 @@ print(paste0("plot filename:", sensCLP))
|
||||||
png(sensCLP, units = "in", width = 1, height = 1, res = 300 )
|
png(sensCLP, units = "in", width = 1, height = 1, res = 300 )
|
||||||
sensP
|
sensP
|
||||||
dev.off()
|
dev.off()
|
||||||
|
###########################################################
|
||||||
|
|
||||||
|
|
|
||||||
Loading…
Add table
Add a link
Reference in a new issue