MultifeatureGrid Visualization

MultifeatureGrid Visualization

This vignette demonstrates how to use the MultifeatureGrid class from the MultiModalGraphics package to create comprehensive heatmaps integrating various data features such as z-scores, p-values, and counts.

Example Data Preparation

First, we create a sample dataset representing biological data across different tissues and signaling pathways, with associated p-values and activation z-scores.

data <- get_multifeature_grid_df()

Creating a MultifeatureGrid Object

We initialize the MultifeatureGrid object with the data prepared above.

mg <- MultifeatureGrid(data)

Plotting the Heatmap

We then plot the heatmap, specifying ‘tissue’ as the independent variable for faceting.

plot_heatmap(mg, independantVariable = "tissue")
#> Warning: Removed 32 rows containing missing values or values outside the scale range
#> (`geom_point()`).

This plot provides a visual summary of the signaling activity and the statistical significance across different tissues, utilizing a color gradient to represent activation z-scores and the size of points to indicate the number of genes involved.

Session info

#> R Under development (unstable) (2025-01-20 r87609)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 24.04.1 LTS
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#> Matrix products: default
#> BLAS:   /home/biocbuild/bbs-3.21-bioc/R/lib/libRblas.so 
#> LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.12.0  LAPACK version 3.12.0
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#> time zone: America/New_York
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#> attached base packages:
#> [1] grid      stats4    stats     graphics  grDevices utils     datasets 
#> [8] methods   base     
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#> other attached packages:
#>  [1] reshape_0.8.9               GetoptLong_1.0.5           
#>  [3] RColorBrewer_1.1-3          wesanderson_0.3.7          
#>  [5] ggplot2_3.5.1               gridtext_0.1.5             
#>  [7] circlize_0.4.16             seriation_1.5.7            
#>  [9] ComplexHeatmap_2.23.0       ggthemes_5.1.0             
#> [11] paletteer_1.6.0             dplyr_1.1.4                
#> [13] MultiAssayExperiment_1.33.9 SummarizedExperiment_1.37.0
#> [15] Biobase_2.67.0              GenomicRanges_1.59.1       
#> [17] GenomeInfoDb_1.43.4         IRanges_2.41.2             
#> [19] S4Vectors_0.45.2            BiocGenerics_0.53.6        
#> [21] generics_0.1.3              MatrixGenerics_1.19.1      
#> [23] matrixStats_1.5.0           MultiModalGraphics_0.99.0  
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#> [43] gclus_1.3.2             clue_0.3-66             magick_2.8.5           
#> [46] foreach_1.5.2           limma_3.63.3            jquerylib_0.1.4        
#> [49] glue_1.8.0              rematch2_2.1.2          codetools_0.2-20       
#> [52] stringi_1.8.4           shape_1.4.6.1           gtable_0.3.6           
#> [55] UCSC.utils_1.3.1        munsell_0.5.1           tibble_3.2.1           
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#> [70] pkgconfig_2.0.3         GlobalOptions_0.1.2