RNAG2

RNAseq Top Genes Plot

RNAG

This page can be used as a template of how to use the available hermes functions to derive and plot top genes for RNA-seq RNA-seq gene expression data and to create an interactive top genes plot using teal.modules.hermes.

Derivation of top genes uses HermesData as input. See RNAG1 for details on how to import, filter and normalize HermesData.

Code

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

We can derive top most expressed genes in HermesData using the top_genes() function. By default, this function uses the counts assay and summary statistic function rowMeans() with no minimum threshold to derive the 10 most expressed genes. You can change this number, or instead work with a minimum threshold on the derived statistic.

Code

result <- top_genes(object)

If we want to derive only the 5 most expressed genes, we can specify this via n_top.

Code

result <- top_genes(object, n_top = 5)

Alternatively, we can also set a minimum threshold of average counts (across samples).

Code

result <- top_genes(object, min_threshold = 50000)

If we want to derive top genes for a specific assay, we can also specify this via assay_name. By default, top_genes() function uses the counts assay. It is important to ensure that the same normalization method is applied on the HermesData object prior to deriving top genes, otherwise the assay would not be available here.

Code

result <- top_genes(object, assay_name = "tpm")

You can also change which summary statistic function is used. Most important here is that this function needs to work on the matrix (genes in rows, samples in columns) and return back a value for each gene, thus summarizing the values from all samples for this gene. Thus all row-wise statistic functions (like rowMeans() from package stats, rowMaxs(), rowSds(), … from package matrixStats) are well suited.

Here, the statistic chosen is the maximum count across samples:

Code

result <- top_genes(object, summary_fun = rowMaxs)

We can then create a simple bar plot for these top genes using autoplot() function.

Code

autoplot(result)

By default, y-axis label is set to summary function name and assay name used to generate the object with top genes using top_genes() function. It is up to the user to optionally adjust the y-axis label via the y_lab argument, and also the x-axis label and title can be adjusted.

Code

autoplot(result, y_lab = "Maximum Count per Sample", x_lab = "Genes", title = "Top Genes")

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 select the top genes plot under the plot type menu. We can then use the provided teal module tm_g_quality to include a QC module in our teal app.

Code

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_quality(
      label = "quality",
      mae_name = "MAE"
    )
  )
)
shinyApp(app$ui, app$server)

[INFO] 2024-09-14 17:26:06.5636 pid:2548 token:[5cc6387f] teal Initializing reporter_previewer_module

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

Code

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              ragg_1.3.3                 
  [9] GlobalOptions_0.1.2         zlibbioc_1.50.0            
 [11] vctrs_0.6.5                 memoise_2.0.1              
 [13] webshot_0.5.5               BiocBaseUtils_1.7.3        
 [15] htmltools_0.5.8.1           S4Arrays_1.4.1             
 [17] forcats_1.0.0               progress_1.2.3             
 [19] curl_5.2.2                  SparseArray_1.4.8          
 [21] sass_0.4.9                  bslib_0.8.0                
 [23] fontawesome_0.5.2           htmlwidgets_1.6.4          
 [25] testthat_3.2.1.1            httr2_1.0.4                
 [27] cachem_1.1.0                teal.widgets_0.4.2         
 [29] mime_0.12                   lifecycle_1.0.4            
 [31] iterators_1.0.14            pkgconfig_2.0.3            
 [33] webshot2_0.1.1              Matrix_1.7-0               
 [35] R6_2.5.1                    fastmap_1.2.0              
 [37] GenomeInfoDbData_1.2.12     rbibutils_2.2.16           
 [39] clue_0.3-65                 digest_0.6.37              
 [41] colorspace_2.1-1            shinycssloaders_1.1.0      
 [43] ps_1.8.0                    AnnotationDbi_1.66.0       
 [45] DESeq2_1.44.0               textshaping_0.4.0          
 [47] RSQLite_2.3.7               filelock_1.0.3             
 [49] labeling_0.4.3              fansi_1.0.6                
 [51] httr_1.4.7                  abind_1.4-8                
 [53] compiler_4.4.1              bit64_4.0.5                
 [55] withr_3.0.1                 doParallel_1.0.17          
 [57] backports_1.5.0             BiocParallel_1.38.0        
 [59] DBI_1.2.3                   logger_0.3.0               
 [61] biomaRt_2.60.1              rappdirs_0.3.3             
 [63] DelayedArray_0.30.1         rjson_0.2.22               
 [65] chromote_0.3.1              tools_4.4.1                
 [67] httpuv_1.6.15               glue_1.7.0                 
 [69] callr_3.7.6                 promises_1.3.0             
 [71] grid_4.4.1                  checkmate_2.3.2            
 [73] cluster_2.1.6               generics_0.1.3             
 [75] gtable_0.3.5                websocket_1.4.2            
 [77] tidyr_1.3.1                 hms_1.1.3                  
 [79] xml2_1.3.6                  utf8_1.2.4                 
 [81] XVector_0.44.0              ggrepel_0.9.6              
 [83] foreach_1.5.2               pillar_1.9.0               
 [85] stringr_1.5.1               limma_3.60.4               
 [87] later_1.3.2                 circlize_0.4.16            
 [89] dplyr_1.1.4                 BiocFileCache_2.12.0       
 [91] lattice_0.22-6              bit_4.0.5                  
 [93] tidyselect_1.2.1            ComplexHeatmap_2.20.0      
 [95] locfit_1.5-9.10             Biostrings_2.72.1          
 [97] knitr_1.48                  gridExtra_2.3              
 [99] teal.logger_0.2.0           edgeR_4.2.1                
[101] xfun_0.47                   statmod_1.5.0              
[103] brio_1.1.5                  stringi_1.8.4              
[105] UCSC.utils_1.0.0            yaml_2.3.10                
[107] shinyWidgets_0.8.6          evaluate_0.24.0            
[109] codetools_0.2-20            tibble_3.2.1               
[111] cli_3.6.3                   systemfonts_1.1.0          
[113] xtable_1.8-4                Rdpack_2.6.1               
[115] processx_3.8.4              jquerylib_0.1.4            
[117] munsell_0.5.1               teal.reporter_0.3.1        
[119] Rcpp_1.0.13                 dbplyr_2.5.0               
[121] png_0.1-8                   parallel_4.4.1             
[123] assertthat_0.2.1            blob_1.2.4                 
[125] prettyunits_1.2.0           scales_1.3.0               
[127] purrr_1.0.2                 crayon_1.5.3               
[129] GetoptLong_1.0.5            rlang_1.1.4                
[131] formatR_1.14                KEGGREST_1.44.1            
[133] shinyjs_2.1.0

Reuse