Compiled date: 2020-04-27

Last edited: 2018-03-08

License: MIT + file LICENSE

1 Background

iSEE coordinates the coloration in every plot via the ExperimentColorMap class (Rue-Albrecht et al. 2018). Colors for samples or features are defined from column or row metadata or assay values using “colormaps”. Each colormap is a function that takes a single integer argument and returns that number of distinct colors. The ExperimentColorMap is a container that stores these functions for use within the iSEE() function. Users can define their own colormaps to customize coloration for specific assays or covariates.

2 Defining colormaps

2.1 Colormaps for continuous variables

For continuous variables, the function will be asked to generate a number of colors (21, by default). Interpolation will then be performed internally to generate a color gradient. Users can use existing color scales like viridis::viridis or heat.colors:

# Coloring for log-counts:
logcounts_color_fun <- viridis::viridis

It is also possible to use a function that completely ignores any arguments, and simply returns a fixed number of interpolation points:

# Coloring for FPKMs:
fpkm_color_fun <- function(n){
    c("black","brown","red","orange","yellow")
}

2.2 Colormaps for categorical variables

For categorical variables, the function should accept the number of levels and return a color per level. Colors are automatically assigned to factor levels in the specified order of the levels.

# Coloring for the 'driver' metadata variable.
driver_color_fun <- function(n){
    RColorBrewer::brewer.pal(n, "Set2")
}

Alternatively, the function can ignore its arguments and simply return a named vector of colors if users want to specify the color for each level explicitly It is the user’s responsibility to ensure that all levels are accounted for1 Needless to say, these functions should not be used as shared or global colormaps.. For instance, the following colormap function will only be compatible with factors of two levels, namely "Y" and "N":

# Coloring for the QC metadata variable:
qc_color_fun <- function(n){
    qc_colors <- c("forestgreen", "firebrick1")
    names(qc_colors) <- c("Y", "N")
    qc_colors
}

3 The colormap hierarchy

3.1 Specific and shared colormaps

Colormaps can be defined by users at three different levels:

  • Each individual assay, column data field, and row data field can be assigned its own distinct colormap. Those colormaps are stored as named lists of functions in the assays, colData, and rowData slots, respectively, of the ExperimentColorMap. This can be useful to easily remember which assay is currently shown; to apply different color scale limits to assays that vary on different ranges of values; or display boolean information in an intuitive way, among many other scenarios.
  • Shared colormaps can be defined for all assays, all column data, and all row data. These colormaps are stored in the all_discrete and all_continuous slots of the ExperimentColorMap, as lists of functions named assays, colData, and rowData.
  • Global colormaps can be defined for all categorical or continuous data. Those two colormaps are stored in the global_discrete and global_continuous slots of the ExperimentColorMap.

3.2 Searching for colors

When queried for a specific colormap of any type (assay, column data, or row data), the following process takes place:

  • A specific individual colormap is looked up in the appropriate slot of the ExperimentColorMap.
  • If it is not found, the shared colormap of the appropriate slot is looked up, according to whether the data are categorical or continuous.
  • If it is not found, the global colormap is looked up, according to whether the data are categorical or continuous.
  • If none of the above colormaps were defined, the ExperimentColorMap will revert to the default colormaps.

By default, viridis is used as the default continuous colormap, and hcl is used as the default categorical colormap.

4 Creating the ExperimentColorMap

We store the set of colormap functions in an instance of the ExperimentColorMap class. Named functions passed as assays, colData, or rowData arguments will be used for coloring data in those slots, respectively.

library(iSEE)
ecm <- ExperimentColorMap(
    assays = list(
        counts = heat.colors,
        logcounts = logcounts_color_fun,
        cufflinks_fpkm = fpkm_color_fun
    ),
    colData = list(
        passes_qc_checks_s = qc_color_fun,
        driver_1_s = driver_color_fun
    ),
    all_continuous = list(
        assays = viridis::plasma
    )
)
ecm
#> Class: ExperimentColorMap
#> assays(3): counts logcounts cufflinks_fpkm
#> colData(2): passes_qc_checks_s driver_1_s
#> rowData(0):
#> all_discrete(0):
#> all_continuous(1): assays

Users can change the defaults for all assays or column data by modifying the shared colormaps. Similarly, users can modify the defaults for all continuous or categorical data by modifying the global colormaps. This is demonstrated below for the continuous variables:

ExperimentColorMap(
    all_continuous=list( # shared
        assays=viridis::plasma,
        colData=viridis::inferno
    ),
    global_continuous=viridis::magma # global
)
#> Class: ExperimentColorMap
#> assays(0):
#> colData(0):
#> rowData(0):
#> all_discrete(0):
#> all_continuous(2): assays colData
#> global_continuous(1)

5 Benefits

The ExperimentColorMap class offers the following major features:

  • A single place to define flexible and lightweight sets of colormaps, that may be saved and reused across sessions and projects outside the app, to apply consistent coloring schemes across entire projects
  • A simple interface through accessors colDataColorMap(colormap, "coldata_name") and setters assayColorMap(colormap, "assay_name") <- colormap_function
  • An elegant fallback mechanism to consistently return a colormap, even for undefined covariates, including a default categorical and continuous colormap, respectively.
  • Three levels of colormaps override: individual, shared within slot (i.e., assays, colData, rowData), or shared globally between all categorical or continuous data scales.

Detailed examples on the use of ExperimentColorMap objects are available in the documentation ?ExperimentColorMap, as well as below.

6 Demonstration

Here, we use the allen single-cell RNA-seq data set to demonstrate the use of the ExperimentColorMap class. Using the sce object that we created previously, we create an iSEE app with the SingleCellExperiment object and the colormap generated above.

app <- iSEE(sce, colormap = ecm)

We run this using runApp to open the app on our browser.

shiny::runApp(app)

Now, choose to color cells by Column data and select passes_qc_checks_s. We will see that all cells that passed QC (Y) are colored “forestgreen”, while the ones that didn’t pass are colored firebrick.

If we color any plot by gene expression, we see that use of counts follows the heat.colors coloring scheme; use of log-counts follows the viridis coloring scheme; and use of FPKMs follows the black-to-yellow scheme we defined in fpkm_color_fun.

Session Info

sessionInfo()
#> R version 4.0.0 (2020-04-24)
#> Platform: x86_64-pc-linux-gnu (64-bit)
#> Running under: Ubuntu 18.04.4 LTS
#> 
#> Matrix products: default
#> BLAS:   /home/biocbuild/bbs-3.11-bioc/R/lib/libRblas.so
#> LAPACK: /home/biocbuild/bbs-3.11-bioc/R/lib/libRlapack.so
#> 
#> locale:
#>  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
#>  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=C              
#>  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
#>  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
#>  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
#> [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
#> 
#> attached base packages:
#> [1] parallel  stats4    stats     graphics  grDevices utils     datasets 
#> [8] methods   base     
#> 
#> other attached packages:
#>  [1] scater_1.16.0               ggplot2_3.3.0              
#>  [3] scRNAseq_2.1.7              iSEE_2.0.0                 
#>  [5] SingleCellExperiment_1.10.0 SummarizedExperiment_1.18.0
#>  [7] DelayedArray_0.14.0         matrixStats_0.56.0         
#>  [9] Biobase_2.48.0              GenomicRanges_1.40.0       
#> [11] GenomeInfoDb_1.24.0         IRanges_2.22.0             
#> [13] S4Vectors_0.26.0            BiocGenerics_0.34.0        
#> [15] BiocStyle_2.16.0           
#> 
#> loaded via a namespace (and not attached):
#>   [1] Rtsne_0.15                    ggbeeswarm_0.6.0             
#>   [3] colorspace_1.4-1              rjson_0.2.20                 
#>   [5] ellipsis_0.3.0                circlize_0.4.8               
#>   [7] XVector_0.28.0                GlobalOptions_0.1.1          
#>   [9] BiocNeighbors_1.6.0           clue_0.3-57                  
#>  [11] DT_0.13                       bit64_0.9-7                  
#>  [13] interactiveDisplayBase_1.26.0 AnnotationDbi_1.50.0         
#>  [15] splines_4.0.0                 knitr_1.28                   
#>  [17] jsonlite_1.6.1                cluster_2.1.0                
#>  [19] dbplyr_1.4.3                  png_0.1-7                    
#>  [21] shinydashboard_0.7.1          shiny_1.4.0.2                
#>  [23] BiocManager_1.30.10           compiler_4.0.0               
#>  [25] httr_1.4.1                    assertthat_0.2.1             
#>  [27] Matrix_1.2-18                 fastmap_1.0.1                
#>  [29] later_1.0.0                   BiocSingular_1.4.0           
#>  [31] htmltools_0.4.0               tools_4.0.0                  
#>  [33] rsvd_1.0.3                    igraph_1.2.5                 
#>  [35] gtable_0.3.0                  glue_1.4.0                   
#>  [37] GenomeInfoDbData_1.2.3        dplyr_0.8.5                  
#>  [39] rappdirs_0.3.1                Rcpp_1.0.4.6                 
#>  [41] vctrs_0.2.4                   nlme_3.1-147                 
#>  [43] ExperimentHub_1.14.0          DelayedMatrixStats_1.10.0    
#>  [45] rintrojs_0.2.2                xfun_0.13                    
#>  [47] stringr_1.4.0                 irlba_2.3.3                  
#>  [49] mime_0.9                      miniUI_0.1.1.1               
#>  [51] lifecycle_0.2.0               shinyAce_0.4.1               
#>  [53] AnnotationHub_2.20.0          zlibbioc_1.34.0              
#>  [55] scales_1.1.0                  colourpicker_1.0             
#>  [57] promises_1.1.0                RColorBrewer_1.1-2           
#>  [59] ComplexHeatmap_2.4.0          yaml_2.2.1                   
#>  [61] curl_4.3                      gridExtra_2.3                
#>  [63] memoise_1.1.0                 stringi_1.4.6                
#>  [65] RSQLite_2.2.0                 BiocVersion_3.11.1           
#>  [67] BiocParallel_1.22.0           shape_1.4.4                  
#>  [69] rlang_0.4.5                   pkgconfig_2.0.3              
#>  [71] bitops_1.0-6                  evaluate_0.14                
#>  [73] lattice_0.20-41               purrr_0.3.4                  
#>  [75] htmlwidgets_1.5.1             bit_1.1-15.2                 
#>  [77] tidyselect_1.0.0              magrittr_1.5                 
#>  [79] bookdown_0.18                 R6_2.4.1                     
#>  [81] DBI_1.1.0                     pillar_1.4.3                 
#>  [83] withr_2.2.0                   mgcv_1.8-31                  
#>  [85] RCurl_1.98-1.2                tibble_3.0.1                 
#>  [87] crayon_1.3.4                  shinyWidgets_0.5.1           
#>  [89] BiocFileCache_1.12.0          rmarkdown_2.1                
#>  [91] viridis_0.5.1                 GetoptLong_0.1.8             
#>  [93] grid_4.0.0                    blob_1.2.1                   
#>  [95] digest_0.6.25                 xtable_1.8-4                 
#>  [97] httpuv_1.5.2                  munsell_0.5.0                
#>  [99] beeswarm_0.2.3                viridisLite_0.3.0            
#> [101] vipor_0.4.5                   shinyjs_1.1
# devtools::session_info()

References

Rue-Albrecht, K., F. Marini, C. Soneson, and A. T. L. Lun. 2018. “ISEE: Interactive Summarizedexperiment Explorer.” F1000Research 7 (June):741.