inst/shiny/v1.2/gene_set_expression/info.R

In romanhaa/cerebroApp: Interactive visualization of scRNA-seq data with Cerebro

##----------------------------------------------------------------------------##
## Panel: Gene set expression
##----------------------------------------------------------------------------##

geneSetExpression_projection_info <- list(
  title = "Dimensional reduction",
  text = p(
    "Interactive projection of cells into 2-dimensional space based on their expression profile.",
    tags$ul(
      tags$li("Both tSNE and UMAP are frequently used algorithms for dimensional reduction in single cell transcriptomics. While they generally allow to make similar conclusions, some differences exist between the two (please refer to Google and/or literature, such as Becht E. et al., Dimensionality reduction for visualizing single-cell data using UMAP. Nature Biotechnology, 2018, 37, 38-44)."),
      tags$li("For human and murine data sets, all organism-specific gene sets from the MSigDB can be selected. If the experiment was performed in another organism, the murine gene sets will be available."),
      tags$li("Cell color reflects the average log-normalised expression of the genes in the selected gene set. Reported below the projection are the genes that are present and absent in this data set. Absent genes could either have been annotated with a different name or were not expressed in any of the cells. Matching of gene names is case-insensitive, that means Myc/MYC/myc are treated equally."),
      tags$li("Samples and clusters can be removed from the plot individually to highlight a contrast of interest."),
      tags$li("Cells can be plotted either randomly (which a more unbiased image) or in the order of expression (with highest expression plotted last), sometimes resulting in a more appealing figure."),
      tags$li("By default, the dot size is set to 15 without any transparency but both these attributes can be changed using the sliders on the left."),
      tags$li("The last 2 slider elements on the left can be used to resize the projection axes. This can be particularly useful when a projection contains a population of cell that is very far away from the rest and therefore creates a big empty space (which is not uncommon for UMAPs).")
    ),
    "The plot is interactive (drag and zoom) but depending on the computer of the user and the number of cells displayed it can become very slow."
  )
)

geneSetExpression_details_selected_cells_info <- list(
  title = "Details of selected cells",
  text = p("Table containing average expression values of genes in the selected gene set as well as selected meta data (sample, cluster, number of transcripts, number of expressed genes) for cells selected in the plot using the box or lasso selection tool. If you want the table to contain all cells in the data set, you must select all cells in the plot. The table can be saved to disk in CSV or Excel format for further analysis.")
)

geneSetExpression_in_selected_cells_info <- list(
  title = "Expression levels in selected cells",
  text = p("This plot shows the average log-normalised expression of genes in the selected gene set for cells grouped by whether they were selected using the box or lasso selection tool.")
)

geneSetExpression_by_sample_info <- list(
  title = "Expression levels by sample",
  text = p("Average log-normalised expression of genes in selected gene set by sample.")
)

geneSetExpression_by_cluster_info <- list(
  title = "Expression levels by cluster",
  text = p("Average log-normalised expression of genes in selected gene set by cluster.")
)

geneSetExpression_by_gene_info <- list(
  title = "Expression levels by gene",
  text = p("Log-normalised expression of 50 highest expressed genes inserted above. Shows mean across all cells.")
)