RNAG9

RNAseq Kaplan-Meier Graph

---

Setup: Import & Data Manipulation

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.

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.

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).

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()

Kaplan-Meier Graph

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.

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().

Teal Module for Kaplan-Meier Graph

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.

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")
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()'

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] random.cdisc.data_0.3.15.9009  teal.modules.hermes_0.1.6.9020
 [3] teal_0.15.2.9061               teal.slice_0.5.1.9011         
 [5] teal.data_0.6.0.9010           teal.code_0.5.0.9009          
 [7] shiny_1.9.1                    hermes_1.7.2.9002             
 [9] SummarizedExperiment_1.34.0    Biobase_2.64.0                
[11] GenomicRanges_1.56.1           GenomeInfoDb_1.40.1           
[13] IRanges_2.38.1                 S4Vectors_0.42.1              
[15] BiocGenerics_0.50.0            MatrixGenerics_1.16.0         
[17] matrixStats_1.4.1              ggfortify_0.4.17              
[19] ggplot2_3.5.1                  dplyr_1.1.4                   
[21] tern_0.9.5.9022                rtables_0.6.9.9014            
[23] magrittr_2.0.3                 formatters_0.5.9.9001         

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] farver_2.1.2                rmarkdown_2.28             
  [7] GlobalOptions_0.1.2         zlibbioc_1.50.0            
  [9] vctrs_0.6.5                 memoise_2.0.1              
 [11] webshot_0.5.5               BiocBaseUtils_1.7.3        
 [13] htmltools_0.5.8.1           S4Arrays_1.4.1             
 [15] forcats_1.0.0               progress_1.2.3             
 [17] curl_5.2.2                  broom_1.0.6                
 [19] SparseArray_1.4.8           sass_0.4.9                 
 [21] parallelly_1.38.0           bslib_0.8.0                
 [23] fontawesome_0.5.2           htmlwidgets_1.6.4          
 [25] httr2_1.0.3                 cachem_1.1.0               
 [27] teal.widgets_0.4.2.9020     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               future_1.34.0              
 [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] 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             DBI_1.2.3                  
 [57] logger_0.3.0                biomaRt_2.60.1             
 [59] rappdirs_0.3.3              DelayedArray_0.30.1        
 [61] rjson_0.2.22                chromote_0.3.1             
 [63] tools_4.4.1                 httpuv_1.6.15              
 [65] glue_1.7.0                  callr_3.7.6                
 [67] promises_1.3.0              grid_4.4.1                 
 [69] checkmate_2.3.2             cluster_2.1.6              
 [71] generics_0.1.3              gtable_0.3.5               
 [73] websocket_1.4.2             tidyr_1.3.1                
 [75] hms_1.1.3                   xml2_1.3.6                 
 [77] utf8_1.2.4                  XVector_0.44.0             
 [79] ggrepel_0.9.6               foreach_1.5.2              
 [81] pillar_1.9.0                stringr_1.5.1              
 [83] later_1.3.2                 circlize_0.4.16            
 [85] splines_4.4.1               BiocFileCache_2.12.0       
 [87] lattice_0.22-6              survival_3.7-0             
 [89] bit_4.0.5                   tidyselect_1.2.1           
 [91] ComplexHeatmap_2.20.0       Biostrings_2.72.1          
 [93] knitr_1.48                  gridExtra_2.3              
 [95] teal.logger_0.2.0.9009      xfun_0.47                  
 [97] stringi_1.8.4               UCSC.utils_1.0.0           
 [99] yaml_2.3.10                 shinyWidgets_0.8.6         
[101] evaluate_0.24.0             codetools_0.2-20           
[103] tibble_3.2.1                cli_3.6.3                  
[105] xtable_1.8-4                Rdpack_2.6.1               
[107] jquerylib_0.1.4             processx_3.8.4             
[109] munsell_0.5.1               teal.reporter_0.3.1.9015   
[111] Rcpp_1.0.13                 globals_0.16.3             
[113] dbplyr_2.5.0                png_0.1-8                  
[115] parallel_4.4.1              assertthat_0.2.1           
[117] blob_1.2.4                  prettyunits_1.2.0          
[119] listenv_0.9.1               scales_1.3.0               
[121] purrr_1.0.2                 crayon_1.5.3               
[123] GetoptLong_1.0.5            rlang_1.1.4                
[125] formatR_1.14                cowplot_1.1.3              
[127] KEGGREST_1.44.1             shinyjs_2.1.0