Quickly Analyze Cancer Data with Data from UCSCXenaShiny

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Shixiang Wang

2025-09-22

library(bregr)
library(dplyr)
#> 
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag
#> The following objects are masked from 'package:base':
#> 
#>     intersect, setdiff, setequal, union

if (!requireNamespace("UCSCXenaShiny")) {
  install.packages("UCSCXenaShiny")
}
#> Loading required namespace: UCSCXenaShiny
library(UCSCXenaShiny)
#> =========================================================================================
#> UCSCXenaShiny version 2.1.0
#> Project URL: https://github.com/openbiox/UCSCXenaShiny
#> Usages: https://openbiox.github.io/UCSCXenaShiny/
#> 
#> If you use it in published research, please cite:
#>   Shixiang Wang, Yi Xiong, Longfei Zhao, Kai Gu, Yin Li, Fei Zhao, Jianfeng Li,
#>   Mingjie Wang, Haitao Wang, Ziyu Tao, Tao Wu, Yichao Zheng, Xuejun Li, Xue-Song Liu,
#>   UCSCXenaShiny: An R/CRAN Package for Interactive Analysis of UCSC Xena Data, 
#>   Bioinformatics, 2021;, btab561, https://doi.org/10.1093/bioinformatics/btab561.
#> =========================================================================================
#>                               --Enjoy it--

UCSCXenaShiny offers extensive builtin cancer datasets and data query functions to facilitate analysis and visualization.

Obtain Data

data <- inner_join(
  tcga_clinical_fine,
  tcga_surv |> select(sample, OS, OS.time),
  by = c("Sample" = "sample")
) |> filter(!is.na(Stage_ajcc), !is.na(Gender))
head(data)
#> # A tibble: 6 × 10
#>   Sample Cancer   Age Code  Gender Stage_ajcc Stage_clinical Grade    OS OS.time
#>   <chr>  <chr>  <dbl> <chr> <chr>  <chr>      <chr>          <chr> <dbl>   <dbl>
#> 1 TCGA-… ACC       58 TP    MALE   Stage II   <NA>           <NA>      1    1355
#> 2 TCGA-… ACC       44 TP    FEMALE Stage IV   <NA>           <NA>      1    1677
#> 3 TCGA-… ACC       23 TP    FEMALE Stage III  <NA>           <NA>      0    2091
#> 4 TCGA-… ACC       30 TP    MALE   Stage III  <NA>           <NA>      1     365
#> 5 TCGA-… ACC       29 TP    FEMALE Stage II   <NA>           <NA>      0    2703
#> 6 TCGA-… ACC       30 TP    FEMALE Stage III  <NA>           <NA>      1     490

Execute bregr Pipeline

Assessing the influence of AJCC Stage on overall survival can be done by analyzing data grouped by gender.

m <- br_pipeline(
  data = data,
  y = c("OS.time", "OS"),
  x = "Stage_ajcc", x2 = "Age",
  group_by = "Gender",
  method = "coxph"
)
#> exponentiate estimates of model(s) constructed from coxph method at default
m
#> an object of <breg> class with slots:
#> • y (response variable): OS.time and OS
#> • x (focal term): Stage_ajcc
#> • x2 (control term): Age
#> • group_by: Gender
#> • data: <tibble[,11]>
#> • config: <list: method = "coxph", extra = "">
#> • models: <list: FEMALE_Stage_ajcc = <coxph>, MALE_Stage_ajcc = <coxph>, and
#> All_Stage_ajcc = <coxph>>
#> • results: <tibble[,22]> with colnames Group_variable, Focal_variable, term,
#> variable, var_label, var_class, var_type, var_nlevels, contrasts,
#> contrasts_type, reference_row, label, n_obs, n_ind, n_event, exposure,
#> estimate, std.error, …, conf.low, and conf.high
#> • results_tidy: <tibble[,9]> with colnames Group_variable, Focal_variable,
#> term, estimate, std.error, statistic, p.value, conf.low, and conf.high
#> 
#> Focal term(s) are injected into the model one by one,
#> while control term(s) remain constant across all models in the batch.

br_get_results(m, tidy = TRUE) |>
  knitr::kable()

Group_variable	Focal_variable	term	estimate	std.error	statistic	p.value	conf.low	conf.high
FEMALE	Stage_ajcc	Stage_ajccStage II	1.273682	0.0959397	2.521499	0.0116856	1.055351	1.537181
FEMALE	Stage_ajcc	Stage_ajccStage III	2.149478	0.0967063	7.912875	0.0000000	1.778347	2.598062
FEMALE	Stage_ajcc	Stage_ajccStage IV	5.178744	0.1081300	15.209118	0.0000000	4.189710	6.401251
FEMALE	Stage_ajcc	Age	1.035787	0.0025991	13.528430	0.0000000	1.030524	1.041077
MALE	Stage_ajcc	Stage_ajccStage II	1.768884	0.0833656	6.841537	0.0000000	1.502237	2.082861
MALE	Stage_ajcc	Stage_ajccStage III	2.235855	0.0811756	9.912137	0.0000000	1.906983	2.621443
MALE	Stage_ajcc	Stage_ajccStage IV	3.379213	0.0849960	14.325886	0.0000000	2.860664	3.991759
MALE	Stage_ajcc	Age	1.029710	0.0023311	12.559176	0.0000000	1.025016	1.034425
All	Stage_ajcc	Stage_ajccStage II	1.468986	0.0628836	6.115624	0.0000000	1.298647	1.661668
All	Stage_ajcc	Stage_ajccStage III	2.193520	0.0621347	12.642015	0.0000000	1.942014	2.477597
All	Stage_ajcc	Stage_ajccStage IV	4.014838	0.0665694	20.880417	0.0000000	3.523741	4.574377
All	Stage_ajcc	Age	1.033083	0.0017248	18.869895	0.0000000	1.029596	1.036581

Generate Visualizations

For example:

m <- br_rename_models(m, c("Female", "Male", "All"))
#> rename model names from "FEMALE_Stage_ajcc", "MALE_Stage_ajcc", and
#> "All_Stage_ajcc" to "Female", "Male", and "All"

br_show_forest_ggstats(m)

Explore Further

Besides, using tcga_surv_get(), you can efficiently retrieve values for a specified gene (c("mRNA", "miRNA", "methylation", "transcript", "protein", "mutation", "cnv")) from the TCGA cohort.

For more comprehensive guidance on querying various omics data from different databases/cohorts, refer to the Molecular Data Query section of the UCSCXenaShiny tutorial book.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.