RNAG9

RNAseq Kaplan-Meier Graph

RNAG

This page can be used as a template of how to produce Kaplan-Meier graphs for RNA-seq gene expression analysis using available tern and hermes functions, and to create an interactive Kaplan-Meier graph for RNA-seq gene expression analysis using teal.modules.hermes.

The code below needs both RNA-seq data (in HermesData format) and time-to-event data (in ADTTE format) as input.

We first prepare the time-to-event data. We define an event indicator variable, transform the time to months and filter down to the overall survival subset.

Code

library(tern)
library(dplyr)

adtte_f <- random.cdisc.data::cadtte %>%
  dplyr::mutate(
    is_event = .data$CNSR == 0,
    AVAL = day2month(.data$AVAL),
    AVALU = "Months"
  ) %>%
  dplyr::filter(.data$PARAMCD == "OS")

Then we prepare the RNA-seq data. See RNAG1 for basic details on how to import, filter and normalize HermesData. We use col_data_with_genes() to extract the sample variables (colData) from the object, together with a single specified gene or a specified gene signature. See ?hermes::gene_spec for details on how to do this. Then we use inner_join_cdisc() to join this genetic data with the ADTTE data from above. See the help page for more details, in particular how the join keys can be customized if needed - here we just join based on USUBJID by default.

Code

library(hermes)

gene_data <- col_data_with_genes(
  object = hermes_data,
  assay_name = "counts",
  genes = gene_spec("GeneID:1820")
)
joined_data <- inner_join_cdisc(
  gene_data = gene_data,
  cdisc_data = adtte_f
)

We can then cut the resulting gene column (we figure out the column name and save it in arm_name below) in the joined_data into quantile bins (in this example we want three equally sized groups).

Code

arm_name <- attr(gene_data, "gene_cols")
binned_data <- joined_data %>%
  dplyr::mutate(
    gene_factor = cut_quantile_bins(
      joined_data[[arm_name]],
      probs = c(1 / 3, 2 / 3)
    )
  ) %>%
  df_explicit_na()

It is now simple to create the Kaplan-Meier graph by providing the data set created above with the variable specification. Note that we specify the above created gene_factor as arm variable here.

Code

variables <- list(
  tte = "AVAL",
  is_event = "is_event",
  arm = "gene_factor"
)
g_km(
  binned_data,
  variables = variables,
  annot_coxph = TRUE
)

See KG1 to KG5 for additional customization options for the Kaplan-Meier graphs or the help page ?g_km().

We start by importing a MultiAssayExperiment and sample ADTTE data; here we use the example multi_assay_experiment available in hermes and example ADTTE data from random.cdisc.data. We can then use the provided teal module tm_g_km to include the corresponding interactive Kaplan-Meier analysis in our teal app. Note that by default the counts assay is excluded via the exclude_assays argument, but we can include it by just saying that we don’t want to exclude any assays. In case that we have different non-standard column names in our ADTTE data set we could also specify them via the adtte_vars argument, see the documentation ?tm_g_km for more details.

Code

library(teal.modules.hermes)

data <- teal_data()
data <- within(data, {
  library(random.cdisc.data)
  library(dplyr)
  library(hermes)
  MAE <- multi_assay_experiment
  ADTTE <- random.cdisc.data::cadtte %>%
    mutate(is_event = .data$CNSR == 0)
})
datanames(data) <- c("MAE", "ADTTE")

Warning: `datanames<-()` was deprecated in teal.data 0.7.0.
ℹ invalid to use `datanames()<-` or `names()<-` on an object of class
  `teal_data`. See ?names.teal_data

Code

join_keys(data)["ADTTE", "ADTTE"] <- c("STUDYID", "USUBJID", "PARAMCD")

app <- init(
  data = data,
  modules = modules(
    tm_g_km(
      label = "Kaplan-Meier Graph",
      adtte_name = "ADTTE",
      mae_name = "MAE",
      exclude_assays = character()
    )
  )
)
shinyApp(app$ui, app$server)

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

Code

sessionInfo()

R version 4.5.2 (2025-10-31)
Platform: x86_64-pc-linux-gnu
Running under: Ubuntu 24.04.4 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-r0.3.26.so;  LAPACK version 3.12.0

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] random.cdisc.data_0.3.16    teal.modules.hermes_0.2.0  
 [3] teal_1.1.0                  teal.slice_0.7.1           
 [5] teal.data_0.8.0             teal.code_0.7.1            
 [7] shiny_1.13.0                hermes_1.14.0              
 [9] SummarizedExperiment_1.40.0 Biobase_2.70.0             
[11] GenomicRanges_1.62.1        Seqinfo_1.0.0              
[13] IRanges_2.44.0              S4Vectors_0.48.0           
[15] BiocGenerics_0.56.0         generics_0.1.4             
[17] MatrixGenerics_1.22.0       matrixStats_1.5.0          
[19] ggfortify_0.4.19            ggplot2_4.0.2              
[21] dplyr_1.2.1                 tern_0.9.10                
[23] rtables_0.6.15              magrittr_2.0.5             
[25] formatters_0.5.12          

loaded via a namespace (and not attached):
  [1] RColorBrewer_1.1-3          jsonlite_2.0.0             
  [3] shape_1.4.6.1               nestcolor_0.1.3            
  [5] MultiAssayExperiment_1.36.2 farver_2.1.2               
  [7] rmarkdown_2.31              fs_2.0.1                   
  [9] ragg_1.5.2                  GlobalOptions_0.1.4        
 [11] vctrs_0.7.2                 memoise_2.0.1              
 [13] webshot_0.5.5               htmltools_0.5.9            
 [15] S4Arrays_1.10.1             forcats_1.0.1              
 [17] BiocBaseUtils_1.13.0        progress_1.2.3             
 [19] curl_7.0.0                  broom_1.0.12               
 [21] SparseArray_1.10.10         sass_0.4.10                
 [23] bslib_0.10.0                fontawesome_0.5.3          
 [25] htmlwidgets_1.6.4           httr2_1.2.2                
 [27] cachem_1.1.0                teal.widgets_0.6.0         
 [29] mime_0.13                   lifecycle_1.0.5            
 [31] iterators_1.0.14            pkgconfig_2.0.3            
 [33] webshot2_0.1.2              Matrix_1.7-5               
 [35] R6_2.6.1                    fastmap_1.2.0              
 [37] rbibutils_2.4.1             clue_0.3-68                
 [39] digest_0.6.39               colorspace_2.1-2           
 [41] shinycssloaders_1.1.0       ps_1.9.2                   
 [43] AnnotationDbi_1.72.0        textshaping_1.0.5          
 [45] RSQLite_2.4.6               filelock_1.0.3             
 [47] labeling_0.4.3              httr_1.4.8                 
 [49] abind_1.4-8                 compiler_4.5.2             
 [51] bit64_4.6.0-1               withr_3.0.2                
 [53] doParallel_1.0.17           S7_0.2.1                   
 [55] backports_1.5.1             bsicons_0.1.2              
 [57] DBI_1.3.0                   logger_0.4.1               
 [59] biomaRt_2.66.2              rappdirs_0.3.4             
 [61] DelayedArray_0.36.1         rjson_0.2.23               
 [63] chromote_0.5.1              tools_4.5.2                
 [65] otel_0.2.0                  httpuv_1.6.17              
 [67] glue_1.8.0                  callr_3.7.6                
 [69] promises_1.5.0              grid_4.5.2                 
 [71] checkmate_2.3.4             cluster_2.1.8.2            
 [73] gtable_0.3.6                websocket_1.4.4            
 [75] tidyr_1.3.2                 hms_1.1.4                  
 [77] XVector_0.50.0              ggrepel_0.9.8              
 [79] foreach_1.5.2               pillar_1.11.1              
 [81] stringr_1.6.0               later_1.4.8                
 [83] circlize_0.4.18             splines_4.5.2              
 [85] BiocFileCache_3.0.0         lattice_0.22-9             
 [87] survival_3.8-6              bit_4.6.0                  
 [89] tidyselect_1.2.1            ComplexHeatmap_2.26.1      
 [91] Biostrings_2.78.0           knitr_1.51                 
 [93] gridExtra_2.3               teal.logger_0.4.1          
 [95] xfun_0.57                   stringi_1.8.7              
 [97] yaml_2.3.12                 shinyWidgets_0.9.1         
 [99] evaluate_1.0.5              codetools_0.2-20           
[101] tibble_3.3.1                cli_3.6.5                  
[103] systemfonts_1.3.2           xtable_1.8-8               
[105] Rdpack_2.6.6                jquerylib_0.1.4            
[107] processx_3.8.7              dichromat_2.0-0.1          
[109] Rcpp_1.1.1                  teal.reporter_0.6.1        
[111] dbplyr_2.5.2                png_0.1-9                  
[113] parallel_4.5.2              assertthat_0.2.1           
[115] blob_1.3.0                  prettyunits_1.2.0          
[117] scales_1.4.0                purrr_1.2.1                
[119] crayon_1.5.3                GetoptLong_1.1.0           
[121] rlang_1.2.0                 formatR_1.14               
[123] cowplot_1.2.0               KEGGREST_1.50.0            
[125] shinyjs_2.1.1

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