library(dplyr)
library(ggplot2)
library(survival)
library(futile.logger)
library(curatedTCGAData)
library(MultiAssayExperiment)
library(TCGAutils)
#
library(glmSparseNet)
#
# Some general options for futile.logger the debugging package
flog.layout(layout.format("[~l] ~m"))
options(
"glmSparseNet.show_message" = FALSE,
"glmSparseNet.base_dir" = withr::local_tempdir()
)
# Setting ggplot2 default theme as minimal
theme_set(ggplot2::theme_minimal())
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
)
brca <- TCGAutils::TCGAsplitAssays(brca, c("01", "11"))
xdataRaw <- t(cbind(assay(brca[[1]]), assay(brca[[2]])))
# Get matches between survival and assay data
classV <- TCGAbiospec(rownames(xdataRaw))$sample_definition |> factor()
names(classV) <- rownames(xdataRaw)
# keep features with standard deviation > 0
xdataRaw <- xdataRaw[, apply(xdataRaw, 2, sd) != 0] |>
scale()
set.seed(params$seed)
smallSubset <- c(
"CD5", "CSF2RB", "HSF1", "IRGC", "LRRC37A6P", "NEUROG2",
"NLRC4", "PDE11A", "PIK3CB", "QARS", "RPGRIP1L", "SDC1",
"TMEM31", "YME1L1", "ZBTB11",
sample(colnames(xdataRaw), 100)
)
xdata <- xdataRaw[, smallSubset[smallSubset %in% colnames(xdataRaw)]]
ydata <- classV
Fit model model penalizing by the hubs using the cross-validation
function by cv.glmHub
.
Shows the results of 1000
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.
Taking the best model described by lambda.min
coefsCV <- Filter(function(.x) .x != 0, coef(fitted, s = "lambda.min")[, 1])
data.frame(
ensembl.id = names(coefsCV),
gene.name = geneNames(names(coefsCV))$external_gene_name,
coefficient = coefsCV,
stringsAsFactors = FALSE
) |>
arrange(gene.name) |>
knitr::kable()
ensembl.id | gene.name | coefficient | |
---|---|---|---|
(Intercept) | (Intercept) | (Intercept) | -6.8189813 |
AMOTL1 | AMOTL1 | AMOTL1 | 0.4430643 |
ATR | ATR | ATR | 1.2498304 |
B3GALT2 | B3GALT2 | B3GALT2 | -0.0867011 |
BAG2 | BAG2 | BAG2 | -0.1841676 |
C16orf82 | C16orf82 | C16orf82 | 0.0396368 |
CD5 | CD5 | CD5 | -1.1200445 |
CIITA | CIITA | CIITA | 0.4256103 |
DCP1A | DCP1A | DCP1A | 0.2994599 |
FAM86B1 | FAM86B1 | FAM86B1 | 0.2025463 |
FNIP2 | FNIP2 | FNIP2 | 0.6101759 |
GDF11 | GDF11 | GDF11 | -0.2676642 |
GNG11 | GNG11 | GNG11 | 3.0659066 |
GREM2 | GREM2 | GREM2 | -0.2014884 |
GZMB | GZMB | GZMB | -2.7663574 |
HAX1 | HAX1 | HAX1 | -0.1516837 |
IL2 | IL2 | IL2 | 0.6327083 |
MMP28 | MMP28 | MMP28 | -0.8438024 |
MS4A4A | MS4A4A | MS4A4A | 1.1614779 |
NDRG2 | NDRG2 | NDRG2 | 1.1142519 |
NLRC4 | NLRC4 | NLRC4 | -1.4434578 |
PIK3CB | PIK3CB | PIK3CB | -0.3880002 |
ZBTB11 | ZBTB11 | ZBTB11 | -0.3325729 |
## [INFO] Misclassified (11)
## [INFO] * False primary solid tumour: 7
## [INFO] * False normal : 4
Histogram of predicted response
ROC curve
## Setting levels: control = Primary Solid Tumor, case = Solid Tissue Normal
## Setting direction: controls < cases
## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
## R version 4.4.2 (2024-10-31)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.1 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=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: Etc/UTC
## tzcode source: system (glibc)
##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] glmSparseNet_1.25.0 TCGAutils_1.27.0
## [3] curatedTCGAData_1.28.1 MultiAssayExperiment_1.33.1
## [5] SummarizedExperiment_1.37.0 Biobase_2.67.0
## [7] GenomicRanges_1.59.1 GenomeInfoDb_1.43.2
## [9] IRanges_2.41.1 S4Vectors_0.45.2
## [11] BiocGenerics_0.53.3 generics_0.1.3
## [13] MatrixGenerics_1.19.0 matrixStats_1.4.1
## [15] futile.logger_1.4.3 survival_3.7-0
## [17] ggplot2_3.5.1 dplyr_1.1.4
## [19] BiocStyle_2.35.0
##
## loaded via a namespace (and not attached):
## [1] sys_3.4.3 jsonlite_1.8.9
## [3] shape_1.4.6.1 magrittr_2.0.3
## [5] GenomicFeatures_1.59.1 farver_2.1.2
## [7] rmarkdown_2.29 BiocIO_1.17.1
## [9] zlibbioc_1.52.0 vctrs_0.6.5
## [11] memoise_2.0.1 Rsamtools_2.23.1
## [13] RCurl_1.98-1.16 htmltools_0.5.8.1
## [15] S4Arrays_1.7.1 BiocBaseUtils_1.9.0
## [17] progress_1.2.3 AnnotationHub_3.15.0
## [19] lambda.r_1.2.4 curl_6.0.1
## [21] pROC_1.18.5 SparseArray_1.7.2
## [23] sass_0.4.9 bslib_0.8.0
## [25] plyr_1.8.9 httr2_1.0.7
## [27] futile.options_1.0.1 cachem_1.1.0
## [29] buildtools_1.0.0 GenomicAlignments_1.43.0
## [31] mime_0.12 lifecycle_1.0.4
## [33] iterators_1.0.14 pkgconfig_2.0.3
## [35] Matrix_1.7-1 R6_2.5.1
## [37] fastmap_1.2.0 GenomeInfoDbData_1.2.13
## [39] digest_0.6.37 colorspace_2.1-1
## [41] AnnotationDbi_1.69.0 ExperimentHub_2.15.0
## [43] RSQLite_2.3.8 filelock_1.0.3
## [45] labeling_0.4.3 fansi_1.0.6
## [47] httr_1.4.7 abind_1.4-8
## [49] compiler_4.4.2 bit64_4.5.2
## [51] withr_3.0.2 backports_1.5.0
## [53] BiocParallel_1.41.0 DBI_1.2.3
## [55] biomaRt_2.63.0 rappdirs_0.3.3
## [57] DelayedArray_0.33.2 rjson_0.2.23
## [59] tools_4.4.2 glue_1.8.0
## [61] restfulr_0.0.15 grid_4.4.2
## [63] checkmate_2.3.2 gtable_0.3.6
## [65] tzdb_0.4.0 hms_1.1.3
## [67] xml2_1.3.6 utf8_1.2.4
## [69] XVector_0.47.0 BiocVersion_3.21.1
## [71] foreach_1.5.2 pillar_1.9.0
## [73] stringr_1.5.1 splines_4.4.2
## [75] BiocFileCache_2.15.0 lattice_0.22-6
## [77] rtracklayer_1.67.0 bit_4.5.0
## [79] tidyselect_1.2.1 maketools_1.3.1
## [81] Biostrings_2.75.1 knitr_1.49
## [83] xfun_0.49 stringi_1.8.4
## [85] UCSC.utils_1.3.0 yaml_2.3.10
## [87] evaluate_1.0.1 codetools_0.2-20
## [89] tibble_3.2.1 BiocManager_1.30.25
## [91] cli_3.6.3 munsell_0.5.1
## [93] jquerylib_0.1.4 Rcpp_1.0.13-1
## [95] GenomicDataCommons_1.31.0 dbplyr_2.5.0
## [97] png_0.1-8 XML_3.99-0.17
## [99] parallel_4.4.2 readr_2.1.5
## [101] blob_1.2.4 prettyunits_1.2.0
## [103] bitops_1.0-9 glmnet_4.1-8
## [105] scales_1.3.0 purrr_1.0.2
## [107] crayon_1.5.3 rlang_1.1.4
## [109] KEGGREST_1.47.0 rvest_1.0.4
## [111] formatR_1.14