Mixed: Genes that are not assigned to any of the above 5 groups.
Enrichment of specific tissues in the query set (with respect to tissue-specific gene expression) is performed with TissueEnrich
oe_tissue_category_df <- onc_enrich_report[['data']][['cell_tissue']][['tissue_overview']][['category_df']] oe_tissue_category_plot <- ggplot2::ggplot(oe_tissue_category_df, ggplot2::aes(x = category, y = pct)) + ggplot2::geom_col(ggplot2::aes(color = group, fill = group), position = ggplot2::position_dodge(0.8), width = 0.7) + ggsci::scale_color_locuszoom() + ggsci::scale_fill_locuszoom() + ggplot2::ylab("Percent") + ggplot2::xlab("") + ggplot2::ylim(0,100) + ggplot2::theme_classic() + ggplot2::theme( legend.position = "top", legend.title = ggplot2::element_blank(), axis.text.x = ggplot2::element_text(angle = 30, size = 11, vjust = 0.5), legend.text = ggplot2::element_text(face="bold", family = "Helvetica", size = 12), axis.text.y = ggplot2::element_text(family = "Helvetica", size = 12), axis.title.x = ggplot2::element_text(family = "Helvetica", size = 12), axis.title.y = ggplot2::element_text(family = "Helvetica", size = 12) ) rm(oe_tissue_category_df) plotly::ggplotly(oe_tissue_category_plot, height = 500, width = 900) |> plotly::layout(legend = list(orientation = "h", x = 0.2, y = -0.45)) rm(oe_tissue_category_plot)
tissue_specs_per_gene <- crosstalk::SharedData$new( onc_enrich_report[['data']][['cell_tissue']][['tissue_enrichment']][['per_gene']] |> dplyr::select(-cancer_max_rank)) crosstalk::bscols( list( crosstalk::filter_select("category", "Category", tissue_specs_per_gene, ~category) ), list( crosstalk::filter_select("tissue", "Tissues", tissue_specs_per_gene, ~tissue) ) ) htmltools::br() DT::datatable( tissue_specs_per_gene, escape = F, extensions=c("Buttons","Responsive"), width = "100%", style = 'bootstrap', rownames = F, options=list(buttons = c('csv','excel'), pageLength = 10, bPaginate = T, dom = 'Bfrtip') ) |> DT::formatStyle( 'category', color = "white", backgroundColor = DT::styleEqual( onc_enrich_report[["config"]][["cell_tissue"]][["tissue_enrichment_levels"]], onc_enrich_report[["config"]][["cell_tissue"]][["enrichment_colors"]] ) )
num_significant_tissues <- onc_enrich_report[['data']][['cell_tissue']][['tissue_enrichment']]$per_type |> dplyr::filter(log10_pvalue >= 1.3) |> nrow()
htmltools::br() cat("<ul><li>") cat('<i><font style="font-size: 100%">Considering the tissue specificities of members of the query set, <b>NO TISSUES</b> are enriched (adjusted p-value < 0.05) compared to the background set.</i></font>', sep='\n') cat('</ul></li>') htmltools::br() htmltools::br() htmltools::br() htmltools::br()
enriched_tissues_df <- onc_enrich_report[['data']][['cell_tissue']][['tissue_enrichment']]$per_type |> dplyr::filter(log10_pvalue >= 1.3) DT::datatable( enriched_tissues_df, escape = F, extensions=c("Buttons","Responsive"), width = "100%", style = 'bootstrap', rownames = F, options=list(buttons = c('csv','excel'), pageLength = 10, bPaginate = T, dom = 'Bfrtip') ) htmltools::br()
Mixed: Genes that are not assigned to any of the above 5 groups.
Enrichment of specific cell types in the query set (with respect to cell type-specific gene expression) is performed with TissueEnrich
oe_ctype_category_df <- onc_enrich_report[['data']][['cell_tissue']][['scRNA_overview']][['category_df']] oe_ctype_category_plot <- ggplot2::ggplot(oe_ctype_category_df, ggplot2::aes(x = category, y = pct)) + ggplot2::geom_col(ggplot2::aes(color = group, fill = group), position = ggplot2::position_dodge(0.8), width = 0.7) + ggsci::scale_color_locuszoom() + ggsci::scale_fill_locuszoom() + ggplot2::ylab("Percent") + ggplot2::xlab("") + ggplot2::ylim(0,100) + ggplot2::theme_classic() + ggplot2::theme( legend.position = "top", legend.title = ggplot2::element_blank(), axis.text.x = ggplot2::element_text(angle = 30, size = 11, vjust = 0.5), legend.text = ggplot2::element_text(face="bold", family = "Helvetica", size = 12), axis.text.y = ggplot2::element_text(family = "Helvetica", size = 12), axis.title.x = ggplot2::element_text(family = "Helvetica", size = 12), axis.title.y = ggplot2::element_text(family = "Helvetica", size = 12) ) rm(oe_ctype_category_df) plotly::ggplotly(oe_ctype_category_plot, height = 500, width = 900) |> plotly::layout(legend = list(orientation = "h", x = 0.2, y = -0.45)) rm(oe_ctype_category_plot)
celltype_specs_per_gene <- crosstalk::SharedData$new( onc_enrich_report[['data']][['cell_tissue']][['scRNA_enrichment']][['per_gene']] |> dplyr::select(-cancer_max_rank)) crosstalk::bscols( list( crosstalk::filter_select("category", "Category", celltype_specs_per_gene, ~category) ), list( crosstalk::filter_select("cell_type", "Cell type", celltype_specs_per_gene, ~cell_type) ) ) htmltools::br() DT::datatable( celltype_specs_per_gene, escape = F, extensions=c("Buttons","Responsive"), width = "100%", style = 'bootstrap', rownames = F, options=list(buttons = c('csv','excel'), pageLength = 10, bPaginate = T, dom = 'Bfrtip') ) |> DT::formatStyle( 'category', color = "white", backgroundColor = DT::styleEqual( onc_enrich_report[["config"]][["cell_tissue"]][["ctype_enrichment_levels"]], onc_enrich_report[["config"]][["cell_tissue"]][["enrichment_colors"]] ) )
num_significant_celltypes <- onc_enrich_report[['data']][['cell_tissue']][['scRNA_enrichment']]$per_type |> dplyr::filter(log10_pvalue >= 1.3) |> nrow()
htmltools::br() cat("<ul><li>") cat('<i><font style="font-size: 100%">Considering the cell-type specificities of members of the query set, <b>NO CELL TYPES</b> are enriched (adjusted p-value < 0.05) compared to the background set.</i></font>', sep='\n') cat('</ul></li>') htmltools::br() htmltools::br() htmltools::br() htmltools::br()
enriched_celltypes_df <- onc_enrich_report[['data']][['cell_tissue']][['scRNA_enrichment']]$per_type |> dplyr::filter(log10_pvalue >= 1.3) DT::datatable( enriched_celltypes_df, escape = F, extensions=c("Buttons","Responsive"), width = "100%", style = 'bootstrap', rownames = F, options=list(buttons = c('csv','excel'), pageLength = 10, bPaginate = T, dom = 'Bfrtip') ) htmltools::br() # p <- ggplot2::ggplot(expression_dist, ggplot2::aes(cell_type, symbol)) + # ggplot2::geom_tile(aes(fill = exp), # colour = "white") + # ggplot2::scale_fill_gradient(low = "white", # high = "steelblue") + # ggplot2::labs(x='', y = '') + # ggplot2::theme_bw()+ # ggplot2::guides(fill = ggplot2::guide_legend(title = "Log2(TPM)"))+ # #theme(legend.position="none")+ # ggplot2::theme(plot.title = ggplot2::element_text( # hjust = 0.5,size = 20), # axis.title = ggplot2::element_text(size=15)) + # ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 45, # vjust = 1, hjust = 1), # panel.grid.major= ggplot2::element_blank(), # panel.grid.minor = ggplot2::element_blank())
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