RNAG6

RNAseq Barplot

---

Setup: Import, Filter and Normalize

This page can be used as a template of how to use the available hermes functions to produce a barplot of the dichotomized gene expression counts into two or three categories based on custom defined percentiles.

The draw_barplot() function used below needs HermesData as input. See RNAG1 for details on how to import, filter and normalize HermesData.

library(hermes)
object <- hermes_data %>%
  add_quality_flags() %>%
  filter() %>%
  normalize()

Barplot for a Single Gene

We can create a barplot for gene expression counts of single genes by specifying a gene in the gene_spec (gene specification) as follows. This function creates a scatterplot with the default percentiles (0.33, 0.67), to specify custom percentiles, please use the percentile argument.

draw_barplot(
  object = object,
  assay_name = "counts",
  x_spec = gene_spec(genes(object)[1])
)

Barplot for Gene Signatures

We can also specify a gene summary function for multiple genes, thereby using the corresponding gene signature. Note that here we just want to use the first 3 genes from the object without explicitly specifying the gene IDs, and this can be done through the genes() function in hermes.

draw_barplot(
  object = object,
  assay_name = "counts",
  x_spec = gene_spec(
    genes(object)[1:3],
    fun = colMeans,
    fun_name = "Median"
  )
)

Additional Options for Customization

It is also possible to pass additional arguments to the function draw_barplot(), ex. if we wish to specify an optional faceting variable or optional fill variable. See ?hermes::draw_barplot() for details about the additional parameters available.

draw_barplot(
  object = object,
  assay_name = "counts",
  x_spec = gene_spec(
    genes(object)[1:3],
    fun = colMeans,
    fun_name = "Median"
  ),
  facet_var = "SEX",
  fill_var = "AGE18"
)

draw_barplot(
  object = object,
  assay_name = "counts",
  x_spec = gene_spec(
    genes(object)[1:3],
    fun = colMeans,
    fun_name = "Median"
  ),
  facet_var = "SEX",
  fill_var = "AGE18",
  percentiles = c(0.1, 0.9)
)

Teal Module for Barplot

We start by importing a MultiAssayExperiment; here we use the example multi_assay_experiment available in hermes. It is wrapped as a teal::dataset. We can then use the provided teal module tm_g_barplot to have add a barplot module in our teal app.

library(teal.modules.hermes)

data <- teal_data()
data <- within(data, {
  library(hermes)
  MAE <- multi_assay_experiment
})
datanames(data) <- "MAE"

app <- init(
  data = data,
  modules = modules(
    tm_g_barplot(
      label = "barplot",
      mae_name = "MAE"
    )
  )
)

[INFO] 2024-09-14 17:30:45.9351 pid:4552 token:[] teal.modules.hermes Initializing tm_g_barplot

shinyApp(app$ui, app$server)

[INFO] 2024-09-14 17:30:47.4320 pid:4552 token:[5971e78a] teal Initializing reporter_previewer_module

Warning: 'experiments' dropped; see 'drops()'

Session Info

sessionInfo()

R version 4.4.1 (2024-06-14)
Platform: x86_64-pc-linux-gnu
Running under: Ubuntu 22.04.4 LTS

Matrix products: default
BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 
LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [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       

time zone: Etc/UTC
tzcode source: system (glibc)

attached base packages:
[1] stats4    stats     graphics  grDevices utils     datasets  methods  
[8] base     

other attached packages:
 [1] teal.modules.hermes_0.1.6   teal_0.15.2                
 [3] teal.slice_0.5.1            teal.data_0.6.0            
 [5] teal.code_0.5.0             shiny_1.9.1                
 [7] hermes_1.8.1                SummarizedExperiment_1.34.0
 [9] Biobase_2.64.0              GenomicRanges_1.56.1       
[11] GenomeInfoDb_1.40.1         IRanges_2.38.1             
[13] S4Vectors_0.42.1            BiocGenerics_0.50.0        
[15] MatrixGenerics_1.16.0       matrixStats_1.4.1          
[17] ggfortify_0.4.17            ggplot2_3.5.1              

loaded via a namespace (and not attached):
  [1] RColorBrewer_1.1-3          jsonlite_1.8.8             
  [3] shape_1.4.6.1               MultiAssayExperiment_1.30.3
  [5] magrittr_2.0.3              farver_2.1.2               
  [7] rmarkdown_2.28              GlobalOptions_0.1.2        
  [9] zlibbioc_1.50.0             vctrs_0.6.5                
 [11] memoise_2.0.1               webshot_0.5.5              
 [13] BiocBaseUtils_1.7.3         htmltools_0.5.8.1          
 [15] S4Arrays_1.4.1              forcats_1.0.0              
 [17] progress_1.2.3              curl_5.2.2                 
 [19] SparseArray_1.4.8           sass_0.4.9                 
 [21] bslib_0.8.0                 fontawesome_0.5.2          
 [23] htmlwidgets_1.6.4           testthat_3.2.1.1           
 [25] httr2_1.0.4                 cachem_1.1.0               
 [27] teal.widgets_0.4.2          mime_0.12                  
 [29] lifecycle_1.0.4             iterators_1.0.14           
 [31] pkgconfig_2.0.3             webshot2_0.1.1             
 [33] Matrix_1.7-0                R6_2.5.1                   
 [35] fastmap_1.2.0               GenomeInfoDbData_1.2.12    
 [37] rbibutils_2.2.16            clue_0.3-65                
 [39] digest_0.6.37               colorspace_2.1-1           
 [41] shinycssloaders_1.1.0       ps_1.8.0                   
 [43] AnnotationDbi_1.66.0        DESeq2_1.44.0              
 [45] RSQLite_2.3.7               filelock_1.0.3             
 [47] labeling_0.4.3              fansi_1.0.6                
 [49] httr_1.4.7                  abind_1.4-8                
 [51] compiler_4.4.1              bit64_4.0.5                
 [53] withr_3.0.1                 doParallel_1.0.17          
 [55] backports_1.5.0             BiocParallel_1.38.0        
 [57] DBI_1.2.3                   logger_0.3.0               
 [59] biomaRt_2.60.1              rappdirs_0.3.3             
 [61] DelayedArray_0.30.1         rjson_0.2.22               
 [63] chromote_0.3.1              tools_4.4.1                
 [65] httpuv_1.6.15               glue_1.7.0                 
 [67] callr_3.7.6                 promises_1.3.0             
 [69] grid_4.4.1                  checkmate_2.3.2            
 [71] cluster_2.1.6               generics_0.1.3             
 [73] gtable_0.3.5                websocket_1.4.2            
 [75] tidyr_1.3.1                 hms_1.1.3                  
 [77] xml2_1.3.6                  utf8_1.2.4                 
 [79] XVector_0.44.0              ggrepel_0.9.6              
 [81] foreach_1.5.2               pillar_1.9.0               
 [83] stringr_1.5.1               limma_3.60.4               
 [85] later_1.3.2                 circlize_0.4.16            
 [87] dplyr_1.1.4                 BiocFileCache_2.12.0       
 [89] lattice_0.22-6              bit_4.0.5                  
 [91] tidyselect_1.2.1            ComplexHeatmap_2.20.0      
 [93] locfit_1.5-9.10             Biostrings_2.72.1          
 [95] knitr_1.48                  gridExtra_2.3              
 [97] teal.logger_0.2.0           edgeR_4.2.1                
 [99] xfun_0.47                   statmod_1.5.0              
[101] brio_1.1.5                  stringi_1.8.4              
[103] UCSC.utils_1.0.0            yaml_2.3.10                
[105] shinyWidgets_0.8.6          evaluate_0.24.0            
[107] codetools_0.2-20            tibble_3.2.1               
[109] cli_3.6.3                   xtable_1.8-4               
[111] Rdpack_2.6.1                processx_3.8.4             
[113] jquerylib_0.1.4             munsell_0.5.1              
[115] teal.reporter_0.3.1         Rcpp_1.0.13                
[117] dbplyr_2.5.0                png_0.1-8                  
[119] parallel_4.4.1              assertthat_0.2.1           
[121] blob_1.2.4                  prettyunits_1.2.0          
[123] scales_1.3.0                purrr_1.0.2                
[125] crayon_1.5.3                GetoptLong_1.0.5           
[127] rlang_1.1.4                 formatR_1.14               
[129] KEGGREST_1.44.1             shinyjs_2.1.0