things
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4 changed files with 142 additions and 99 deletions
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@ -26,7 +26,8 @@ dm_om_wf_lf_data <- function(df
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, categ_cols_to_factor){
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df = as.data.frame(df)
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df$maf = log10(df$maf) # can't see otherwise
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# Initialise the required dfs based on gene name
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geneL_normal = c("pnca")
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geneL_na = c("gid", "rpob")
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@ -93,7 +94,9 @@ dm_om_wf_lf_data <- function(df
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, "mcsm_ppi2_affinity" , "mcsm_ppi2_scaled" , "mcsm_ppi2_outcome"
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, "consurf_score" , "consurf_scaled" , "consurf_outcome" # exists now
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, "snap2_score" , "snap2_scaled" , "snap2_outcome"
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, "mcsm_na_affinity" , "mcsm_na_scaled" , "mcsm_na_outcome")
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, "mcsm_na_affinity" , "mcsm_na_scaled" , "mcsm_na_outcome"
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, "provean_score" , "provean_scaled" , "provean_outcome")
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}else{
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colnames_to_extract = c(mut_colname, mut_info_colname, mut_info_label_colname
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, aa_pos_colname, LigDist_colname
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@ -143,29 +146,31 @@ dynamut2_dn = paste0("Dynamut2 " , stability_suffix); dynamut2_dn
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mcsm_na_dn = paste0("mCSM-NA ", stability_suffix); mcsm_na_dn
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mcsm_ppi2_dn = paste0("mCSM-PPI2 ", stability_suffix); mcsm_ppi2_dn
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consurf_dn = paste0("Consurf"); consurf_dn
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consurf_dn = paste0("ConSurf"); consurf_dn
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snap2_dn = paste0("SNAP2"); snap2_dn
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provean_dn = paste0("PROVEAN"); provean_dn
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# change column names: plyr
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new_colnames = c(asa = "ASA"
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, rsa = "RSA"
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, rd_values = "RD"
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, kd_values = "KD"
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, log10_or_mychisq = "Log10 (OR)"
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, neglog_pval_fisher = "-Log (P)"
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#, af = "MAF"
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, maf = "MAF"
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#, ligand_dist_colname = lig_dn # cannot handle variable name 'ligand_dist_colname'
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#, log10_or_mychisq = "Log10(OR)"
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#, neglog_pval_fisher = "-Log(P)"
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#, af = "MAF"
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, maf = "Log10(MAF)"
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#, ligand_dist_colname= lig_dn # cannot handle variable name 'ligand_dist_colname'
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, affinity_scaled = mcsm_lig_dn
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, duet_scaled = duet_dn
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, foldx_scaled = foldx_dn
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, deepddg_scaled = deepddg_dn
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, ddg_dynamut2_scaled = dynamut2_dn
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, mcsm_na_scaled = mcsm_na_dn
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, mcsm_ppi2_affinity = mcsm_ppi2_dn
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, consurf_score = consurf_dn
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, snap2_score = snap2_dn)
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, mcsm_ppi2_scaled = mcsm_ppi2_dn
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, consurf_scaled = consurf_dn
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, snap2_scaled = snap2_dn
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, provean_scaled = provean_dn)
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comb_df_sl1 = plyr::rename(comb_df_s
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, replace = new_colnames
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@ -173,7 +178,8 @@ comb_df_sl1 = plyr::rename(comb_df_s
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, warn_duplicated = T)
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# renaming colname using variable i.e ligand_dist_colname: dplyr
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comb_df_sl = comb_df_sl1 %>% dplyr::rename(!!lig_dn := all_of(ligand_dist_colname))
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#comb_df_sl = comb_df_sl1 %>% dplyr::rename(!!lig_dn := all_of(ligand_dist_colname))
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comb_df_sl = comb_df_sl1 %>% dplyr::rename(!!lig_dn := all_of(LigDist_colname)) # NEW
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names(comb_df_sl)
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#=======================
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@ -206,9 +212,9 @@ static_cols_end = c(lig_dn
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, "RSA"
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, "RD"
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, "KD"
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, "MAF"
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, "Log10 (OR)"
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#, "-Log (P)"
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, "Log10(MAF)"
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#, "Log10(OR)"
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#, "-Log(P)"
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)
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#########################################################################
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@ -344,19 +350,22 @@ wf_lf_dataL[['lf_dynamut2']] = lf_dynamut2
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#1--> "most_variable", 2--> "", 3-->"", 4-->""
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#5-->"", 6-->"", 7-->"", 8-->"", 9-->"most_conserved"
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#====================
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# FIXME: if you add category column to consurf
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# WF data: consurf
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cols_to_select_consurf = c(static_cols_start, c("consurf_outcome", consurf_dn), static_cols_end)
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wf_consurf = comb_df_sl[, cols_to_select_consurf]
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pivot_cols_consurf = cols_to_select_consurf[1: (length(static_cols_start) + 1)]; pivot_cols_consurf
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# WF data: consurf
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cols_to_select_consurf = c(static_cols_start, c(consurf_dn), static_cols_end)
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wf_consurf = comb_df_sl[, cols_to_select_consurf]
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pivot_cols_consurf = cols_to_select_consurf[1: (length(static_cols_start))]; pivot_cols_consurf
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expected_rows_lf = nrow(wf_consurf) * (length(wf_consurf) - length(pivot_cols_consurf))
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expected_rows_lf
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# when outcome didn't exist
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#cols_to_select_consurf = c(static_cols_start, c(consurf_dn), static_cols_end)
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#wf_consurf = comb_df_sl[, cols_to_select_consurf]
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#
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# pivot_cols_consurf = cols_to_select_consurf[1: (length(static_cols_start))]; pivot_cols_consurf
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# expected_rows_lf = nrow(wf_consurf) * (length(wf_consurf) - length(pivot_cols_consurf))
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# expected_rows_lf
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# LF data: consurf
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lf_consurf = gather(wf_consurf
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, key = param_type
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@ -403,6 +412,37 @@ if (nrow(lf_snap2) == expected_rows_lf){
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# Assign them to the output list
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wf_lf_dataL[['wf_snap2']] = wf_snap2
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wf_lf_dataL[['lf_snap2']] = lf_snap2
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#==============
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# Provean2: LF
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#==============
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# WF data: provean
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cols_to_select_provean = c(static_cols_start, c("provean_outcome", provean_dn), static_cols_end)
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wf_provean = comb_df_sl[, cols_to_select_provean]
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pivot_cols_provean = cols_to_select_provean[1: (length(static_cols_start) + 1)]; pivot_cols_provean
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expected_rows_lf = nrow(wf_provean) * (length(wf_provean) - length(pivot_cols_provean))
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expected_rows_lf
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# LF data: provean
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lf_provean = gather(wf_provean
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, key = param_type
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, value = param_value
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, all_of(provean_dn):tail(static_cols_end,1)
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, factor_key = TRUE)
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if (nrow(lf_provean) == expected_rows_lf){
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cat("\nPASS: long format data created for ", provean_dn)
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}else{
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cat("\nFAIL: long format data could not be created for duet")
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quit()
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}
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# Assign them to the output list
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wf_lf_dataL[['wf_provean']] = wf_provean
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wf_lf_dataL[['lf_provean']] = lf_provean
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###########################################################################
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# AFFINITY cols
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###########################################################################
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@ -25,7 +25,7 @@ lf_bp <- function(lf_df = lf_duet
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, make_boxplot = FALSE
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, bp_width = c("auto", 0.5)
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, add_stats = TRUE
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, stat_grp_comp = c("DM", "OM")
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, stat_grp_comp = c("R", "S")
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, stat_method = "wilcox.test"
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, my_paired = FALSE
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, stat_label = c("p.format", "p.signif") ){
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@ -129,77 +129,77 @@ lf_bp <- function(lf_df = lf_duet
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# TODO: plot_ly()
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#############################
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lf_bp_plotly <- function(lf_df
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, p_title = ""
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, colour_categ = ""
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, x_grp = mutation_info
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, y_var = param_value
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, facet_var = param_type
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, n_facet_row = 1
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, y_scales = "free_y"
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, colour_bp_strip = "khaki2"
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, dot_size = 3
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, dot_transparency = 0.3
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, violin_quantiles = c(0.25, 0.5, 0.75) # can be NULL
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, my_ats = 20 # axis text size
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, my_als = 18 # axis label size
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, my_fls = 18 # facet label size
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, my_pts = 22 # plot title size)
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#, make_boxplot = FALSE
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, bp_width = c("auto", 0.5)
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#, add_stats = FALSE
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#, stat_grp_comp = c("DM", "OM")
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#, stat_method = "wilcox.test"
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#, my_paired = FALSE
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#, stat_label = c("p.format", "p.signif")
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){
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OutPlotly = ggplot(lf_df, aes(x = eval(parse(text = x_grp))
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, y = eval(parse(text = y_var))
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, label1 = x_grp
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, label2 = y_var
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, lable3 = colour_categ) ) +
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facet_wrap(~ eval(parse(text = facet_var))
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, nrow = n_facet_row
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, scales = y_scales) +
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geom_violin(trim = T
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, scale = "width"
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, draw_quantiles = violin_quantiles) +
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geom_beeswarm(priority = "density"
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, size = dot_size
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, alpha = dot_transparency
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, show.legend = FALSE
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, cex = 0.8
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, aes(colour = factor(eval(parse(text = colour_categ) ) ) ) ) +
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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
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, hjust = 0.5
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, colour = "black"
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, face = "bold")
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, strip.background = element_rect(fill = colour_bp_strip)
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, strip.text.x = element_text(size = my_fls
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, colour = "black")
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, legend.title = element_text(color = "black"
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, size = my_als)
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, legend.text = element_text(size = my_ats)
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, legend.position = "none")+
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labs(title = p_title
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, x = ""
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, y = "")
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OutPlotly = ggplotly(OutPlotly
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#, tooltip = c("label")
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)
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return(OutPlotly)
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}
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# lf_bp_plotly <- function(lf_df
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# , p_title = ""
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# , colour_categ = ""
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# , x_grp = mutation_info
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# , y_var = param_value
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# , facet_var = param_type
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# , n_facet_row = 1
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# , y_scales = "free_y"
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# , colour_bp_strip = "khaki2"
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# , dot_size = 3
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# , dot_transparency = 0.3
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# , violin_quantiles = c(0.25, 0.5, 0.75) # can be NULL
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# , my_ats = 20 # axis text size
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# , my_als = 18 # axis label size
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# , my_fls = 18 # facet label size
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# , my_pts = 22 # plot title size)
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# #, make_boxplot = FALSE
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# , bp_width = c("auto", 0.5)
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# #, add_stats = FALSE
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# #, stat_grp_comp = c("DM", "OM")
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# #, stat_method = "wilcox.test"
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# #, my_paired = FALSE
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# #, stat_label = c("p.format", "p.signif")
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# ){
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#
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# OutPlotly = ggplot(lf_df, aes(x = eval(parse(text = x_grp))
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# , y = eval(parse(text = y_var))
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# , label1 = x_grp
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# , label2 = y_var
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# , lable3 = colour_categ) ) +
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#
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# facet_wrap(~ eval(parse(text = facet_var))
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# , nrow = n_facet_row
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# , scales = y_scales) +
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#
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# geom_violin(trim = T
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# , scale = "width"
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# , draw_quantiles = violin_quantiles) +
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#
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# geom_beeswarm(priority = "density"
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# , size = dot_size
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# , alpha = dot_transparency
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# , show.legend = FALSE
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# , cex = 0.8
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# , aes(colour = factor(eval(parse(text = colour_categ) ) ) ) ) +
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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
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# , hjust = 0.5
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# , colour = "black"
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# , face = "bold")
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# , strip.background = element_rect(fill = colour_bp_strip)
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# , strip.text.x = element_text(size = my_fls
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# , colour = "black")
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# , legend.title = element_text(color = "black"
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# , size = my_als)
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# , legend.text = element_text(size = my_ats)
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# , legend.position = "none")+
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#
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# labs(title = p_title
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# , x = ""
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# , y = "")
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#
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# OutPlotly = ggplotly(OutPlotly
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# #, tooltip = c("label")
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# )
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# return(OutPlotly)
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#
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# }
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@ -3,7 +3,7 @@ library(ggpubr)
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lf_unpaired_stats <- function(lf_data
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, lf_stat_value = "param_value"
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, lf_stat_group = "mutation_info"
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, lf_stat_group = "mutation_info_labels"
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, lf_col_statvars = "param_type"
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, my_paired = FALSE
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, stat_adj = "none"){
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@ -140,6 +140,9 @@ lf_consurf = all_dm_om_df[['lf_consurf']]
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wf_snap2 = all_dm_om_df[['wf_snap2']]
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lf_snap2 = all_dm_om_df[['lf_snap2']]
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wf_provean = all_dm_om_df[['wf_provean']]
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lf_provean = all_dm_om_df[['lf_provean']]
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if (tolower(gene)%in%geneL_na){
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wf_mcsm_na = all_dm_om_df[['wf_mcsm_na']]
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lf_mcsm_na = all_dm_om_df[['lf_mcsm_na']]
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