saving other_plots.R
This commit is contained in:
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5102bbea1b
commit
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5 changed files with 227 additions and 241 deletions
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@ -319,8 +319,7 @@ all.equal(foo, bar)
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# clear variables
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rm(foo, bar, gene_metadata
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, in_filename_params, infile_params, merging_cols
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, in_filename_gene_metadata, infile_gene_metadata
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, merged_df2v2, merged_df2v3)
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, in_filename_gene_metadata, infile_gene_metadata)
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#*************************
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#####################################################################
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# Combining: LIG
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@ -64,3 +64,13 @@ table(snpsBYpos_df$snpsBYpos)
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# with dups
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1 2 3 4 5 6 7
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39 30 25 23 20 6 2
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#======================
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# dr and other muts count = PS
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#======================
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dr_mutations_pyrazinamide : 227
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other_mutations_pyrazinamide : 197
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@ -18,19 +18,29 @@ source("other_plots_data.R")
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#=======
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# output
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#=======
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dr_other_plots_combined = "dr_other_muts.svg"
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plot_dr_other_plots_combined = paste0(plotdir,"/", dr_other_plots_combined)
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dr_other_combined = "dr_other_muts.svg"
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plot_dr_other_combined = paste0(plotdir,"/", dr_other_combined)
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dr_other_foldx = "dr_other_muts_foldx.svg"
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plot_dr_other_foldx = paste0(plotdir,"/", dr_other_foldx)
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point_legend = "point_legend.svg"
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plot_point_legend = paste0(plotdir,"/", point_legend)
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########################################################################
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# end of data extraction and cleaning for plots #
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########################################################################
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#my_comparisons <- list( c(dr_muts_col, other_muts_col) )
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my_comparisons <- list( c("DM", "OM") )
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my_ats = 15# axis text size
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my_als = 20 # axis label size
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my_fls = 20 # facet label size
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my_pts = 22 # plot title size
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#===========
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# Plot1: PS
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#===========
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my_stat_ps = compare_means(param_value~mutation_info, group.by = "param_type"
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, data = df_lf_ps, paired = FALSE, p.adjust.method = "BH")
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@ -42,22 +52,23 @@ p1 = ggplot(df_lf_ps, aes(x = mutation_info
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, nrow = 1
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, scales = "free_y") +
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geom_boxplot(fill = "white", outlier.colour = NA
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# position = position_dodge(width = 0.9)
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, width = 0.5) +
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#, position = position_dodge(width = 0.9)
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, width = 0.2) +
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geom_point(position = position_jitterdodge(dodge.width=0.01)
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, alpha = 0.5
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, show.legend = FALSE
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, aes(colour = factor(duet_outcome))) +
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theme(axis.text.x = element_text(size = 15)
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, axis.text.y = element_text(size = 15
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theme(axis.text.x = element_text(size = my_ats)
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, axis.text.y = element_text(size = my_ats
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, angle = 0
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, hjust = 1
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, vjust = 0)
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, axis.title.x = element_text(size = 15)
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, axis.title.y = element_text(size = 15)
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, plot.title = element_text(size = 20, hjust = 0.5)
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, strip.text.x = element_text(size = 15, colour = "black")
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, legend.title = element_text(color = "black", size = 20)
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, legend.text = element_text(size = 15)
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, axis.title.x = element_text(size = my_ats)
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, axis.title.y = element_text(size = my_ats)
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, plot.title = element_text(size = my_pts , hjust = 0.5)
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, strip.text.x = element_text(size = my_fls, colour = "black")
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, legend.title = element_text(color = "black", size = my_als)
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, legend.text = element_text(size = my_ats)
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, legend.direction = "vertical") +
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labs(title = "DUET"
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, x = ""
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@ -68,51 +79,9 @@ p1 = ggplot(df_lf_ps, aes(x = mutation_info
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#, label = "p.format")
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, label = "p.signif")
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p1
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#===============================================================
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#===========
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# Plot 2: Foldx
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#===========
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my_stat_foldx = compare_means(param_value~mutation_info, group.by = "param_type"
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, data = df_lf_foldx, paired = FALSE, p.adjust.method = "BH")
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y_value = "param_value"
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p2 = ggplot(df_lf_foldx, aes(x = mutation_info
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, y = eval(parse(text=y_value)) )) +
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facet_wrap(~ param_type
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, nrow = 1
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, scales = "free_y") +
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geom_boxplot(fill = "white", outlier.colour = NA
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# position = position_dodge(width = 0.9)
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, width = 0.5) +
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geom_point(position = position_jitterdodge(dodge.width=0.01)
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, show.legend = FALSE
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, aes(colour = factor(foldx_outcome))) +
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theme(axis.text.x = element_text(size = 15)
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, axis.text.y = element_text(size = 15
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, angle = 0
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, hjust = 1
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, vjust = 0)
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, axis.title.x = element_text(size = 15)
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, axis.title.y = element_text(size = 15)
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, plot.title = element_text(size = 20, hjust = 0.5)
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, strip.text.x = element_text(size = 15, colour = "black")
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, legend.title = element_text(color = "black", size = 20)
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, legend.text = element_text(size = 15)
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, legend.direction = "vertical") +
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labs(title = "Foldx"
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, x = ""
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, y = "")+
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stat_compare_means(comparisons = my_comparisons
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, method = "wilcox.test"
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, paired = FALSE
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#, label = "p.format")
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, label = "p.signif")
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p2
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#===========
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# Plot 3: LIG
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# Plot 2: LIG
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#===========
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my_stat_lig = compare_means(param_value~mutation_info, group.by = "param_type"
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@ -120,28 +89,29 @@ my_stat_lig = compare_means(param_value~mutation_info, group.by = "param_type"
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y_value = "param_value"
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p3 = ggplot(df_lf_lig, aes(x = mutation_info
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p2 = ggplot(df_lf_lig, aes(x = mutation_info
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, y = eval(parse(text=y_value)) )) +
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facet_wrap(~ param_type
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, nrow = 1
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, scales = "free_y") +
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geom_boxplot(fill = "white", outlier.colour = NA
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# position = position_dodge(width = 0.9)
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, width = 0.5) +
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#, position = position_dodge(width = 0.9)
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, width = 0.2) +
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geom_point(position = position_jitterdodge(dodge.width=0.01)
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, alpha = 0.5
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, show.legend = FALSE
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, aes(colour = factor(ligand_outcome))) +
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theme(axis.text.x = element_text(size = 15)
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, axis.text.y = element_text(size = 15
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theme(axis.text.x = element_text(size = my_ats)
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, axis.text.y = element_text(size = my_ats
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, angle = 0
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, hjust = 1
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, vjust = 0)
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, axis.title.x = element_text(size = 15)
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, axis.title.y = element_text(size = 15)
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, plot.title = element_text(size = 20, hjust = 0.5)
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, strip.text.x = element_text(size = 15, colour = "black")
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, legend.title = element_text(color = "black", size = 20)
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, legend.text = element_text(size = 15)
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, axis.title.x = element_text(size = my_ats)
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, axis.title.y = element_text(size = my_ats)
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, plot.title = element_text(size = my_pts , hjust = 0.5)
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, strip.text.x = element_text(size = my_fls, colour = "black")
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, legend.title = element_text(color = "black", size = my_als)
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, legend.text = element_text(size = my_ats)
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, legend.direction = "vertical") +
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labs(title = "Ligand Affinity"
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, x = ""
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@ -152,20 +122,108 @@ p3 = ggplot(df_lf_lig, aes(x = mutation_info
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#, label = "p.format")
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, label = "p.signif")
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p2
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#====================================================================
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#===========
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# Plot 3: Foldx
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#===========
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my_stat_foldx = compare_means(param_value~mutation_info, group.by = "param_type"
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, data = df_lf_foldx, paired = FALSE, p.adjust.method = "BH")
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y_value = "param_value"
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p3 = ggplot(df_lf_foldx, aes(x = mutation_info
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, y = eval(parse(text=y_value)) )) +
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facet_wrap(~ param_type
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, nrow = 1
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, scales = "free_y") +
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geom_boxplot(fill = "white", outlier.colour = NA
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#, position = position_dodge(width = 0.2)
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, width = 0.5) +
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geom_point(position = position_jitterdodge(dodge.width=0.01)
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, alpha = 0.5
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, show.legend = FALSE
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, aes(colour = factor(foldx_outcome))) +
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theme(axis.text.x = element_text(size = my_ats)
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, axis.text.y = element_text(size = my_ats
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, angle = 0
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, hjust = 1
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, vjust = 0)
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, axis.title.x = element_text(size = my_ats)
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, axis.title.y = element_text(size = my_ats)
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, plot.title = element_text(size = my_pts , hjust = 0.5)
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, strip.text.x = element_text(size = my_fls, colour = "black")
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#, legend.title = element_text(color = "black", size = my_als)
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, legend.text = element_text(size = my_ats)
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, legend.direction = "vertical"
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, legend.title = element_blank()) +
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labs(title = ""
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, x = ""
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, y = "") +
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stat_compare_means(comparisons = my_comparisons
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, method = "wilcox.test"
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, paired = FALSE
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#, label = "p.format")
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, label = "p.signif")
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p3
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#====================================================================
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#===========
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# legend
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#===========
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legend = ggplot(df_lf_foldx, aes(x = mutation_info
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, y = eval(parse(text=y_value)) )) +
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facet_wrap(~ param_type
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, nrow = 1
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, scales = "free_y") +
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geom_boxplot(fill = "white", outlier.colour = NA
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#, position = position_dodge(width = 0.2)
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, width = 0.5) +
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geom_point(position = position_jitterdodge(dodge.width=0.01)
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, aes(colour = factor(foldx_outcome))) +
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theme(legend.text = element_text(size = 25)
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, legend.direction = "vertical"
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, legend.title = element_blank())+
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guides(color = guide_legend(override.aes = list(size = 6)))
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legend
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#===========================
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# combine
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#===========================
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#svg(plot_or_combined, width = 32, height = 12)
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svg("test.svg", width = 25, height = 12)
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#theme_set(theme_gray()) # to preserve default theme
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#---------
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# plot 1
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#---------
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cat("Output plot:",plot_dr_other_combined)
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svg(plot_dr_other_combined, width = 24, height = 12)
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printFile = cowplot::plot_grid(plot_grid(p1, p2, p3
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, nrow = 3
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, align = 'h'
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#theme_set(theme_gray()) # to preserve default theme
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OutPlot = cowplot::plot_grid(p1, p2
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, nrow = 2
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, align = "hv"
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, axis = "b"
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, labels = c("", "", "")
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, label_size = 20))
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print(printFile)
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, label_size = my_als)
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print(OutPlot)
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dev.off()
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#---------
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# plot 2
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#---------
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svg(plot_dr_other_foldx , width = 6, height = 7)
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OutPlot2 = p3
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print(OutPlot2)
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dev.off()
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#---------
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# plot 3: legend
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#---------
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svg(plot_point_legend, width = 6, height = 7)
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OutPlot3 = legend
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print(OutPlot3)
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dev.off()
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@ -50,7 +50,6 @@ if ( all(c1 == c2) ){
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exit()
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}
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# name tidying
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df_ps$mutation_info = as.factor(df_ps$mutation_info)
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df_ps$duet_outcome = as.factor(df_ps$duet_outcome)
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@ -60,6 +59,21 @@ df_ps$ligand_outcome = as.factor(df_ps$ligand_outcome)
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# check
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table(df_ps$mutation_info)
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# further checks to make sure dr and other muts are indeed unique
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dr_muts = df_ps[df_ps$mutation_info == dr_muts_col,]
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dr_muts_names = unique(dr_muts$mutation)
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other_muts = df_ps[df_ps$mutation_info == other_muts_col,]
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other_muts_names = unique(other_muts$mutation)
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if ( table(dr_muts_names%in%other_muts_names)[[1]] == length(dr_muts_names) &&
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table(other_muts_names%in%dr_muts_names)[[1]] == length(other_muts_names) ){
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cat("PASS: dr and other muts are indeed unique")
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}else{
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cat("FAIL: dr adn others muts are NOT unique!")
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quit()
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}
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#%%%%%%%%%%%%%%%%%%%
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# REASSIGNMENT: LIG
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#%%%%%%%%%%%%%%%%%%%%
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@ -133,60 +147,7 @@ levels(df_lf_ps$mutation_info)[levels(df_lf_ps$mutation_info)==other_muts_col] <
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levels(df_lf_ps$mutation_info); table(df_lf_ps$mutation_info)
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############################################################################
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#===========
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# Data: foldx
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#===========
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# keep similar dtypes cols together
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cols_to_select_foldx = c("mutationinformation", "mutation", "position", "mutation_info"
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, "foldx_outcome"
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, "foldx_scaled"
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, "ligand_distance"
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, "asa"
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, "rsa"
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, "rd_values"
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, "kd_values")
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df_wf_foldx = df_ps[, cols_to_select_foldx]
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pivot_cols_foldx = cols_to_select_foldx[1:5]; pivot_cols_foldx
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expected_rows_lf_foldx = nrow(df_wf_foldx) * (length(df_wf_foldx) - length(pivot_cols_foldx))
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expected_rows_lf_foldx
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# LF data: foldx
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df_lf_foldx = gather(df_wf_foldx, param_type, param_value, foldx_scaled:kd_values, factor_key=TRUE)
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if (nrow(df_lf_foldx) == expected_rows_lf_foldx){
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cat("PASS: long format data created for foldx")
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}else{
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cat("FAIL: long format data could not be created for foldx")
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exit()
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}
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# assign pretty labels: param type
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levels(df_lf_foldx$param_type); table(df_lf_foldx$param_type)
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levels(df_lf_foldx$param_type)[levels(df_lf_foldx$param_type)=="foldx_scaled"] <- "Foldx"
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levels(df_lf_foldx$param_type)[levels(df_lf_foldx$param_type)=="ligand_distance"] <- "Ligand Distance"
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levels(df_lf_foldx$param_type)[levels(df_lf_foldx$param_type)=="asa"] <- "ASA"
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levels(df_lf_foldx$param_type)[levels(df_lf_foldx$param_type)=="rsa"] <- "RSA"
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levels(df_lf_foldx$param_type)[levels(df_lf_foldx$param_type)=="rd_values"] <- "RD"
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levels(df_lf_foldx$param_type)[levels(df_lf_foldx$param_type)=="kd_values"] <- "KD"
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# check
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levels(df_lf_foldx$param_type); table(df_lf_foldx$param_type)
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# assign pretty labels: mutation_info
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levels(df_lf_foldx$mutation_info); table(df_lf_foldx$mutation_info)
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sum(table(df_lf_foldx$mutation_info)) == nrow(df_lf_foldx)
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levels(df_lf_foldx$mutation_info)[levels(df_lf_foldx$mutation_info)==dr_muts_col] <- "DM"
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levels(df_lf_foldx$mutation_info)[levels(df_lf_foldx$mutation_info)==other_muts_col] <- "OM"
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# check
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levels(df_lf_foldx$mutation_info); table(df_lf_foldx$mutation_info)
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############################################################################
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#===========
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# LF data: LIG
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#===========
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@ -239,7 +200,61 @@ levels(df_lf_lig$mutation_info)[levels(df_lf_lig$mutation_info)==other_muts_col]
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# check
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levels(df_lf_lig$mutation_info); table(df_lf_lig$mutation_info)
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###############################################################################
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#############################################################################
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#===========
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# Data: foldx
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#===========
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# keep similar dtypes cols together
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cols_to_select_foldx = c("mutationinformation", "mutation", "position", "mutation_info"
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, "foldx_outcome"
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, "foldx_scaled")
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#, "ligand_distance"
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#, "asa"
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#, "rsa"
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#, "rd_values"
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#, "kd_values")
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|
||||
|
||||
df_wf_foldx = df_ps[, cols_to_select_foldx]
|
||||
|
||||
pivot_cols_foldx = cols_to_select_foldx[1:5]; pivot_cols_foldx
|
||||
|
||||
expected_rows_lf_foldx = nrow(df_wf_foldx) * (length(df_wf_foldx) - length(pivot_cols_foldx))
|
||||
expected_rows_lf_foldx
|
||||
|
||||
# LF data: foldx
|
||||
df_lf_foldx = gather(df_wf_foldx, param_type, param_value, foldx_scaled, factor_key=TRUE)
|
||||
|
||||
if (nrow(df_lf_foldx) == expected_rows_lf_foldx){
|
||||
cat("PASS: long format data created for foldx")
|
||||
}else{
|
||||
cat("FAIL: long format data could not be created for foldx")
|
||||
exit()
|
||||
}
|
||||
|
||||
# assign pretty labels: param type
|
||||
levels(df_lf_foldx$param_type); table(df_lf_foldx$param_type)
|
||||
|
||||
levels(df_lf_foldx$param_type)[levels(df_lf_foldx$param_type)=="foldx_scaled"] <- "Foldx"
|
||||
#levels(df_lf_foldx$param_type)[levels(df_lf_foldx$param_type)=="ligand_distance"] <- "Ligand Distance"
|
||||
#levels(df_lf_foldx$param_type)[levels(df_lf_foldx$param_type)=="asa"] <- "ASA"
|
||||
#levels(df_lf_foldx$param_type)[levels(df_lf_foldx$param_type)=="rsa"] <- "RSA"
|
||||
#levels(df_lf_foldx$param_type)[levels(df_lf_foldx$param_type)=="rd_values"] <- "RD"
|
||||
#levels(df_lf_foldx$param_type)[levels(df_lf_foldx$param_type)=="kd_values"] <- "KD"
|
||||
# check
|
||||
levels(df_lf_foldx$param_type); table(df_lf_foldx$param_type)
|
||||
|
||||
# assign pretty labels: mutation_info
|
||||
levels(df_lf_foldx$mutation_info); table(df_lf_foldx$mutation_info)
|
||||
sum(table(df_lf_foldx$mutation_info)) == nrow(df_lf_foldx)
|
||||
|
||||
levels(df_lf_foldx$mutation_info)[levels(df_lf_foldx$mutation_info)==dr_muts_col] <- "DM"
|
||||
levels(df_lf_foldx$mutation_info)[levels(df_lf_foldx$mutation_info)==other_muts_col] <- "OM"
|
||||
# check
|
||||
levels(df_lf_foldx$mutation_info); table(df_lf_foldx$mutation_info)
|
||||
|
||||
############################################################################
|
||||
|
||||
# clear excess variables
|
||||
rm(cols_to_select_ps, cols_to_select_foldx, cols_to_select_lig
|
||||
|
|
|
|||
|
|
@ -1,96 +0,0 @@
|
|||
setwd("/home/tanu/git/LSHTM_analysis/scripts/plotting")
|
||||
|
||||
source("combining_dfs_plotting.R")
|
||||
|
||||
table(merged_df3$mutation_info)
|
||||
|
||||
# assign foldx
|
||||
#ddg<0 = "Stabilising" (-ve)
|
||||
table(merged_df3$ddg < 0)
|
||||
merged_df3$foldx_outcome = ifelse(merged_df3$ddg < 0, "Stabilising", "Destabilising")
|
||||
|
||||
#===========
|
||||
# PS data
|
||||
#===========
|
||||
dr_muts = merged_df3[merged_df3$mutation_info == "dr_mutations_pyrazinamide",]
|
||||
other_muts = merged_df3[merged_df3$mutation_info == "other_mutations_pyrazinamide",]
|
||||
|
||||
par(mfrow = c(1,1))
|
||||
par(mfrow = c(2,6))
|
||||
|
||||
# mcsm duet
|
||||
boxplot(dr_muts$duet_scaled, other_muts$duet_scaled, main = "DUET"
|
||||
#, col = factor(merged_df3$duet_outcome)
|
||||
)
|
||||
wilcox.test(dr_muts$duet_scaled, other_muts$duet_scaled, paired = F)
|
||||
|
||||
# foldx ddg
|
||||
boxplot(dr_muts$ddg, other_muts$ddg, main = "Foldx")
|
||||
wilcox.test(dr_muts$ddg, other_muts$ddg, paired = F)
|
||||
|
||||
# rd
|
||||
boxplot(dr_muts$rd_values, other_muts$rd_values, main = "RD")
|
||||
wilcox.test(dr_muts$rd_values, other_muts$rd_values)
|
||||
|
||||
# kd
|
||||
boxplot(dr_muts$kd_values, other_muts$kd_values, main = "KD")
|
||||
wilcox.test(dr_muts$kd_values, other_muts$kd_values)
|
||||
|
||||
# asa
|
||||
boxplot(dr_muts$asa, other_muts$asa, main = "ASA")
|
||||
wilcox.test(dr_muts$asa, other_muts$asa)
|
||||
|
||||
# rsa
|
||||
boxplot(dr_muts$rsa, other_muts$rsa, main = "RSA")
|
||||
wilcox.test(dr_muts$rsa, other_muts$rsa)
|
||||
|
||||
#===================================================================
|
||||
#==========
|
||||
# LIG data
|
||||
#==========
|
||||
dr_muts_lig = merged_df3_lig[merged_df3_lig$mutation_info == "dr_mutations_pyrazinamide",]
|
||||
other_muts_lig = merged_df3_lig[merged_df3_lig$mutation_info == "other_mutations_pyrazinamide",]
|
||||
|
||||
# mcsm ligand affinity
|
||||
boxplot(dr_muts_lig$duet_scaled, other_muts_lig$duet_scaled, main = "Ligand affinity")
|
||||
wilcox.test(dr_muts_lig$duet_scaled, other_muts_lig$duet_scaled, paired = F)
|
||||
|
||||
# rd
|
||||
boxplot(dr_muts_lig$rd_values, other_muts_lig$rd_values, main = "RD")
|
||||
wilcox.test(dr_muts_lig$rd_values, other_muts_lig$rd_values)
|
||||
|
||||
# kd
|
||||
boxplot(dr_muts_lig$kd_values, other_muts_lig$kd_values, main = "KD")
|
||||
wilcox.test(dr_muts_lig$kd_values, other_muts_lig$kd_values)
|
||||
|
||||
# asa
|
||||
boxplot(dr_muts_lig$asa, other_muts_lig$asa, main = "ASA")
|
||||
wilcox.test(dr_muts_lig$asa, other_muts_lig$asa)
|
||||
|
||||
# rsa
|
||||
boxplot(dr_muts_lig$rsa, other_muts_lig$rsa, main = "RSA")
|
||||
wilcox.test(dr_muts_lig$rsa, other_muts_lig$rsa)
|
||||
|
||||
# checking agreement b/w mcsm and foldx
|
||||
cols_to_select = c("mutationinformation"
|
||||
, "mutation_info"
|
||||
, "duet_scaled"
|
||||
, "ddg"
|
||||
, "duet_outcome"
|
||||
, "foldx_outcome")
|
||||
|
||||
merged_df3_short = select(merged_df3, cols_to_select)
|
||||
|
||||
mcsm_foldx = merged_df3_short[which(merged_df3_short$duet_outcome != merged_df3_short$foldx_outcome),]
|
||||
|
||||
mcsm_foldx$sign_comp = ifelse(sign(mcsm_foldx$duet_scaled)==sign(mcsm_foldx$ddg), "PASS", "FAIL")
|
||||
table(mcsm_foldx$sign_comp)
|
||||
|
||||
# another way of checking
|
||||
merged_df3$sign_comp = ifelse(sign(merged_df3$duet_scaled)==sign(merged_df3$ddg), "PASS", "FAIL")
|
||||
table(merged_df3$sign_comp)
|
||||
|
||||
disagreement = table(merged_df3$sign_comp)[2]/nrow(merged_df3)*100
|
||||
agreement = 100 - disagreement
|
||||
|
||||
cat("There is", agreement, "% between mcsm and foldx predictions")
|
||||
Loading…
Add table
Add a link
Reference in a new issue