Tutorial: Getting started

This is a quick tour of some basic commands and usage patterns, just to get you started.

# Load packages
library(GAMBLR.data)
library(GAMBLR.helpers)
library(GAMBLR.viz)
library(tidyverse)

This tutorial explores how to generate some basic and most common plots, commonly occurring arguments across different functions, best practices and recommendations in the scope of visualizing data.

What are standartized colours?

First, let’s explore the standartized color pallettes in the GAMBLR.viz. They are stored as list in one of the GAMBLR.viz dependencies (GAMBLR.helpers) and are an integral part of visualizations. For demonstration purposes, we will obtain all of the standartized colours:

all_c <- get_gambl_colours(
    as_dataframe = TRUE
)

What are the colours available?

str(all_c)
'data.frame':   268 obs. of  3 variables:
 $ group : chr  "seq_type" "seq_type" "seq_type" "type" ...
 $ name  : chr  "mrna" "genome" "capture" "gain" ...
 $ colour: chr  "#E41A1C" "#377EB8" "#4DAF4A" "#0000FF" ...

What are the colour groups?

table(all_c$group)

               BL             blood chapuy_classifier          clinical 
                7                15                 6                47 
           cohort               coo       copy_number               EBV 
               16                12                17                 4 
               FL  genetic_subgroup              hmrn            indels 
                3                24                 8                 2 
  lacy_classifier          lymphgen    lymphgenerator          mutation 
                8                14                10                13 
        pathology           pos_neg          rainfall          seq_type 
               31                11                 7                 3 
              sex               svs              type 
                6                 2                 2

Many of these colours are conviniently provided for you to ensure consistency that is independent of formatting and case: for example, when the color for DLBCL COO is returned, the same color will be used for UNCLASS, U, UNC, Unclassified etc.

Just for the purpose of this guide, we will define a simple function to display some of these colour pallettes:

show_col <- function(data, group){
    data %>%
        filter(
            !!sym("group") == {{group}}
        ) %>%
        ggplot(
            aes(
                x = name,
                y = 0,
                fill = colour,
                label = name
            )
        ) +
        geom_tile(width = 0.9, height = 1) +
        geom_text(color = "white", fontface="bold") +
        scale_fill_identity(guide = "none") +
        coord_flip() +
        theme_void() +
        labs(title = toupper(group)) +
        theme(plot.title = element_text(lineheight = 0.9,hjust=0.5,face="bold"))
}

Hex codes for B-cell lymphomas

show_col(all_c, "pathology")

Hex codes for genetic subgroups

show_col(all_c, "genetic_subgroup")

Hex codes for clinical variables

show_col(all_c, "clinical")

Hex codes for Mutation types

show_col(all_c, "mutation")

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