The underlying cell viability data comes from the integration of data in n = 912 cell lines, using both DepMap (2020_Q2) and Project Score (20210311), as published in Pacini et al., Nat Commun, 2021
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r onc_enrich_report[['config']][['fitness']][['max_BF_score']]
targets_wide <- as.data.frame( onc_enrich_report[['data']][['fitness']][['fitness_scores']][['targets']] |> dplyr::group_by(.data$symbol, .data$n_gene_tissue, .data$symbol_link_ps, .data$n_gene, .data$tissue) |> dplyr::summarise(cell_lines = paste(.data$model_name, collapse = ", "), ps_link = paste(.data$model_link_ps, collapse = ", "), .groups = "drop") |> dplyr::ungroup() |> dplyr::mutate(cell_lines = stringr::str_replace_all(.data$cell_lines, "\\.", "-")) ) fitness_lof_oe_plot <- targets_wide |> dplyr::mutate(symbol = forcats::fct_reorder(symbol, n_gene)) |> ggplot2::ggplot(ggplot2::aes(x = symbol, y = n_gene_tissue, fill = tissue)) + ggplot2::geom_bar(stat = "identity") + ggplot2::coord_flip() + ggplot2::ylab("Number of cell lines with significant loss-of-fitness") + ggplot2::xlab("") + ggplot2::scale_fill_manual(values = oncoEnrichR::tissue_colors) + ggplot2::theme( panel.grid.minor = ggplot2::element_blank(), axis.text = ggplot2::element_text(family = "Helvetica", size = 11), legend.text = ggplot2::element_text(family = "Helvetica", size = 11), axis.title.x = ggplot2::element_text(family = "Helvetica", size = 12), legend.title = ggplot2::element_blank(), #set thickness of axis ticks axis.ticks = ggplot2::element_line(size = 0.2), #remove plot background plot.background = ggplot2::element_blank() ) plotly::ggplotly(fitness_lof_oe_plot) rm(fitness_lof_oe_plot)
cat('<br>\n <ul><li> <i> <span style="font-size: 105%; padding: 3px; background-color:#989898; color:white"> <b>NO GENES</b> in the query set found with loss-of-fitness effect from CRISPR/Cas9 screens in DepMap/Project Score. </span></i></li></ul>',sep='\n') cat('\n') cat('<br><br>')
htmltools::br() fitness_lof_oe_df <- onc_enrich_report[['data']][['fitness']][['fitness_scores']][['targets']] |> dplyr::group_by(.data$symbol, .data$n_gene_tissue, .data$symbol_link_ps, .data$n_gene, .data$tissue) |> dplyr::summarise(ps_link = paste(.data$model_link_ps, collapse = ", "), .groups = "drop") |> dplyr::ungroup() |> dplyr::select(.data$symbol_link_ps, .data$tissue, .data$ps_link) |> dplyr::rename(cell_lines = .data$ps_link, symbol = .data$symbol_link_ps) |> dplyr::mutate(loss_of_fitness = T) |> head(2000) fitness_lof_oe_table <- crosstalk::SharedData$new(fitness_lof_oe_df) rm(fitness_lof_oe_df) crosstalk::bscols( list( crosstalk::filter_select("tissue", "Tissue", fitness_lof_oe_table, ~tissue) ) ) DT::datatable( fitness_lof_oe_table, escape = F, extensions = c("Buttons","Responsive"), width = "100%", options = list(buttons = c('csv','excel'), dom = 'Bfrtip', pagelength = 20) )
cat('<br>\n <ul><li> <i> <span style="font-size: 105%; padding: 3px; background-color:#989898; color:white"> <b>NO GENES</b> in the query set found with loss-of-fitness effect from CRISPR/Cas9 screens in DepMap/Project Score. </span></i></li></ul>',sep='\n') cat('\n') cat('<br><br>')
oe_priority_matrix <- as.data.frame( onc_enrich_report[['data']][['fitness']][['target_priority_scores']][['targets']] |> dplyr::arrange(symbol) |> tidyr::pivot_wider(names_from = tumor_type, values_from = priority_score) ) rownames(oe_priority_matrix) <- oe_priority_matrix$symbol oe_priority_matrix$symbol <- NULL oe_priority_matrix <- as.matrix(oe_priority_matrix) if(NROW(oe_priority_matrix) > 100){ oe_priority_matrix <- oe_priority_matrix[1:100,] } oe_priority_matrix <- oe_priority_matrix[nrow(oe_priority_matrix):1, ] oe_target_priority_fig <- plotly::plot_ly( colors = "YlGn", width = 800, height = 400 + (14.67 * NROW(oe_priority_matrix))) |> plotly::add_heatmap( y = rownames(oe_priority_matrix), x = colnames(oe_priority_matrix), z = oe_priority_matrix, hovertext = "Priority score", yaxis = "y") |> plotly::layout( title = 'Target priority scores', xaxis = list(tickfont = list(size = 13, family = "Helvetica"), tickangle = -50), yaxis = list(tickfont = list(size = 12, family = "Helvetica")), margin = list(l = 75, r = 20, b = 150, t = 30, pad = 4) ) |> plotly::colorbar( nticks = 10, title = list(text = "Priority score",side = "bottom"), limits = c(0, plyr::round_any(max(oe_priority_matrix), 10, ceiling))) rm(oe_priority_matrix) oe_target_priority_fig
cat('<br>\n <ul><li> <i> <span style="font-size: 105%; padding: 3px; background-color:#989898; color:white"> <b>NO / LIMITED GENES</b> in the query set nominated as prioritized targets in DepMap/Project Score. </span></i></li></ul>',sep='\n') cat('\n')
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