Contents

0.1 Instalation

if (!require("BiocManager"))
  install.packages("BiocManager")
BiocManager::install("glmSparseNet")

1 Required Packages

library(dplyr)
library(ggplot2)
library(survival)
library(futile.logger)
library(curatedTCGAData)
library(TCGAutils)
#
library(glmSparseNet)
#
# Some general options for futile.logger the debugging package
.Last.value <- flog.layout(layout.format('[~l] ~m'))
.Last.value <- glmSparseNet:::show.message(FALSE)
# Setting ggplot2 default theme as minimal
theme_set(ggplot2::theme_minimal())

2 Load data

The data is loaded from an online curated dataset downloaded from TCGA using curatedTCGAData bioconductor package and processed.

To accelerate the process we use a very reduced dataset down to 107 variables only (genes), which is stored as a data object in this package. However, the procedure to obtain the data manually is described in the following chunk.

brca <- curatedTCGAData(diseaseCode = "BRCA", assays = "RNASeq2GeneNorm",
                        version = "1.1.38", dry.run = FALSE)
# keep only solid tumour (code: 01)
brca.primary.solid.tumor <- TCGAutils::TCGAsplitAssays(brca, '01')
xdata.raw <- t(assay(brca.primary.solid.tumor[[1]]))

# Get survival information
ydata.raw <- colData(brca.primary.solid.tumor) %>% as.data.frame %>% 
  # Keep only data relative to survival or samples
  dplyr::select(patientID, vital_status, 
         Days.to.date.of.Death, Days.to.Date.of.Last.Contact, 
         days_to_death,         days_to_last_followup,
         Vital.Status) %>% 
  # Convert days to integer
  dplyr::mutate(Days.to.date.of.Death = as.integer(Days.to.date.of.Death)) %>%
  dplyr::mutate(
    Days.to.Last.Contact  = as.integer(Days.to.Date.of.Last.Contact)
  ) %>%
  # Find max time between all days (ignoring missings)
  dplyr::rowwise() %>%
  dplyr::mutate(
    time = max(days_to_last_followup, Days.to.date.of.Death, 
               Days.to.Last.Contact, days_to_death, na.rm = TRUE)
  ) %>%
  # Keep only survival variables and codes
  dplyr::select(patientID, status = vital_status, time) %>% 
  # Discard individuals with survival time less or equal to 0
  dplyr::filter(!is.na(time) & time > 0) %>% 
  as.data.frame()

# Set index as the patientID
rownames(ydata.raw) <- ydata.raw$patientID

# Get matches between survival and assay data
xdata.raw <- xdata.raw[TCGAbarcode(rownames(xdata.raw)) %in% 
                         rownames(ydata.raw),]
xdata.raw <- xdata.raw %>% 
  { (apply(., 2, sd) != 0) } %>% 
  { xdata.raw[, .] } %>% 
  scale

# Order ydata the same as assay
ydata.raw    <- ydata.raw[TCGAbarcode(rownames(xdata.raw)), ]

# Using only a subset of genes previously selected to keep this short example.
set.seed(params$seed)
small.subset <- c('CD5', 'CSF2RB', 'IRGC', 'NEUROG2', 'NLRC4', 'PDE11A',  
                  'PTEN', 'TP53', 'BRAF',
                  'PIK3CB', 'QARS', 'RFC3', 'RPGRIP1L', 'SDC1', 'TMEM31', 
                  'YME1L1', 'ZBTB11', sample(colnames(xdata.raw), 100)) %>% 
  unique

xdata <- xdata.raw[, small.subset[small.subset %in% colnames(xdata.raw)]]
ydata <- ydata.raw %>% dplyr::select(time, status)

3 Fit models

Fit model model penalizing by the hubs using the cross-validation function by cv.glmHub.

set.seed(params$seed)
fitted <- cv.glmHub(xdata, Surv(ydata$time, ydata$status),
                    family  = 'cox',
                    lambda = buildLambda(1),
                    network = 'correlation', 
                    network.options = networkOptions(cutoff = .6, 
                                                     min.degree = .2))
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values

## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values

## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values

## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values

## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values

## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values

## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values

## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values

## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values

## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values

4 Results of Cross Validation

Shows the results of 100 different parameters used to find the optimal value in 10-fold cross-validation. The two vertical dotted lines represent the best model and a model with less variables selected (genes), but within a standard error distance from the best.

plot(fitted)

4.1 Coefficients of selected model from Cross-Validation

Taking the best model described by lambda.min

coefs.v <- coef(fitted, s = 'lambda.min')[,1] %>% { .[. != 0]}
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
coefs.v %>% { 
  data.frame(gene.name   = names(.),
             coefficient = .,
             stringsAsFactors = FALSE)
  } %>%
  arrange(gene.name) %>%
  knitr::kable()
gene.name coefficient
CD5 CD5 -0.16632

4.2 Survival curves and Log rank test

separate2GroupsCox(as.vector(coefs.v), 
                    xdata[, names(coefs.v)], 
                    ydata, 
                    plot.title = 'Full dataset', legend.outside = FALSE)
## $pvalue
## [1] 0.001237802
## 
## $plot

## 
## $km
## Call: survfit(formula = survival::Surv(time, status) ~ group, data = prognostic.index.df)
## 
##             n events median 0.95LCL 0.95UCL
## Low risk  540     58   3959    3492      NA
## High risk 540     94   3738    3262    4456

5 Session Info

sessionInfo()
## R version 4.3.2 Patched (2023-11-13 r85521)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 22.04.3 LTS
## 
## Matrix products: default
## BLAS:   /home/biocbuild/bbs-3.18-bioc/R/lib/libRblas.so 
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=en_GB              LC_COLLATE=C              
##  [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: America/New_York
## tzcode source: system (glibc)
## 
## attached base packages:
##  [1] grid      parallel  stats4    stats     graphics  grDevices utils    
##  [8] datasets  methods   base     
## 
## other attached packages:
##  [1] VennDiagram_1.7.3           reshape2_1.4.4             
##  [3] forcats_1.0.0               glmSparseNet_1.20.1        
##  [5] glmnet_4.1-8                Matrix_1.6-5               
##  [7] TCGAutils_1.22.2            curatedTCGAData_1.24.0     
##  [9] MultiAssayExperiment_1.28.0 SummarizedExperiment_1.32.0
## [11] Biobase_2.62.0              GenomicRanges_1.54.1       
## [13] GenomeInfoDb_1.38.5         IRanges_2.36.0             
## [15] S4Vectors_0.40.2            BiocGenerics_0.48.1        
## [17] MatrixGenerics_1.14.0       matrixStats_1.2.0          
## [19] futile.logger_1.4.3         survival_3.5-7             
## [21] ggplot2_3.4.4               dplyr_1.1.4                
## [23] BiocStyle_2.30.0           
## 
## loaded via a namespace (and not attached):
##   [1] jsonlite_1.8.8                shape_1.4.6                  
##   [3] magrittr_2.0.3                magick_2.8.2                 
##   [5] GenomicFeatures_1.54.3        farver_2.1.1                 
##   [7] rmarkdown_2.25                BiocIO_1.12.0                
##   [9] zlibbioc_1.48.0               vctrs_0.6.5                  
##  [11] memoise_2.0.1                 Rsamtools_2.18.0             
##  [13] RCurl_1.98-1.14               rstatix_0.7.2                
##  [15] htmltools_0.5.7               S4Arrays_1.2.0               
##  [17] BiocBaseUtils_1.4.0           progress_1.2.3               
##  [19] AnnotationHub_3.10.0          lambda.r_1.2.4               
##  [21] curl_5.2.0                    broom_1.0.5                  
##  [23] pROC_1.18.5                   SparseArray_1.2.3            
##  [25] sass_0.4.8                    bslib_0.6.1                  
##  [27] plyr_1.8.9                    zoo_1.8-12                   
##  [29] futile.options_1.0.1          cachem_1.0.8                 
##  [31] GenomicAlignments_1.38.2      mime_0.12                    
##  [33] lifecycle_1.0.4               iterators_1.0.14             
##  [35] pkgconfig_2.0.3               R6_2.5.1                     
##  [37] fastmap_1.1.1                 GenomeInfoDbData_1.2.11      
##  [39] shiny_1.8.0                   digest_0.6.34                
##  [41] colorspace_2.1-0              AnnotationDbi_1.64.1         
##  [43] ExperimentHub_2.10.0          RSQLite_2.3.5                
##  [45] ggpubr_0.6.0                  filelock_1.0.3               
##  [47] labeling_0.4.3                km.ci_0.5-6                  
##  [49] fansi_1.0.6                   httr_1.4.7                   
##  [51] abind_1.4-5                   compiler_4.3.2               
##  [53] bit64_4.0.5                   withr_3.0.0                  
##  [55] backports_1.4.1               BiocParallel_1.36.0          
##  [57] carData_3.0-5                 DBI_1.2.1                    
##  [59] highr_0.10                    ggsignif_0.6.4               
##  [61] biomaRt_2.58.2                rappdirs_0.3.3               
##  [63] DelayedArray_0.28.0           rjson_0.2.21                 
##  [65] tools_4.3.2                   interactiveDisplayBase_1.40.0
##  [67] httpuv_1.6.14                 glue_1.7.0                   
##  [69] restfulr_0.0.15               promises_1.2.1               
##  [71] generics_0.1.3                gtable_0.3.4                 
##  [73] KMsurv_0.1-5                  tzdb_0.4.0                   
##  [75] tidyr_1.3.1                   survminer_0.4.9              
##  [77] data.table_1.15.0             hms_1.1.3                    
##  [79] car_3.1-2                     xml2_1.3.6                   
##  [81] utf8_1.2.4                    XVector_0.42.0               
##  [83] BiocVersion_3.18.1            foreach_1.5.2                
##  [85] pillar_1.9.0                  stringr_1.5.1                
##  [87] later_1.3.2                   splines_4.3.2                
##  [89] BiocFileCache_2.10.1          lattice_0.22-5               
##  [91] rtracklayer_1.62.0            bit_4.0.5                    
##  [93] tidyselect_1.2.0              Biostrings_2.70.2            
##  [95] knitr_1.45                    gridExtra_2.3                
##  [97] bookdown_0.37                 xfun_0.41                    
##  [99] stringi_1.8.3                 yaml_2.3.8                   
## [101] evaluate_0.23                 codetools_0.2-19             
## [103] tibble_3.2.1                  BiocManager_1.30.22          
## [105] cli_3.6.2                     xtable_1.8-4                 
## [107] munsell_0.5.0                 jquerylib_0.1.4              
## [109] survMisc_0.5.6                Rcpp_1.0.12                  
## [111] GenomicDataCommons_1.26.0     dbplyr_2.4.0                 
## [113] png_0.1-8                     XML_3.99-0.16.1              
## [115] ellipsis_0.3.2                readr_2.1.5                  
## [117] blob_1.2.4                    prettyunits_1.2.0            
## [119] bitops_1.0-7                  scales_1.3.0                 
## [121] purrr_1.0.2                   crayon_1.5.2                 
## [123] rlang_1.1.3                   KEGGREST_1.42.0              
## [125] rvest_1.0.3                   formatR_1.14