if (!require("BiocManager"))
install.packages("BiocManager")
BiocManager::install("glmSparseNet")
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())
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 <- curatedTCGAData(diseaseCode = "BRCA", assays = "RNASeq2GeneNorm",
version = "1.1.38", dry.run = FALSE)
brca <- TCGAutils::TCGAsplitAssays(brca, c('01','11'))
xdata.raw <- t(cbind(assay(brca[[1]]), assay(brca[[2]])))
# Get matches between survival and assay data
class.v <- TCGAbiospec(rownames(xdata.raw))$sample_definition %>% factor
names(class.v) <- rownames(xdata.raw)
# keep features with standard deviation > 0
xdata.raw <- xdata.raw %>%
{ (apply(., 2, sd) != 0) } %>%
{ xdata.raw[, .] } %>%
scale
set.seed(params$seed)
small.subset <- c('CD5', 'CSF2RB', 'HSF1', 'IRGC', 'LRRC37A6P', 'NEUROG2',
'NLRC4', 'PDE11A', 'PIK3CB', 'QARS', 'RPGRIP1L', 'SDC1',
'TMEM31', 'YME1L1', 'ZBTB11',
sample(colnames(xdata.raw), 100))
xdata <- xdata.raw[, small.subset[small.subset %in% colnames(xdata.raw)]]
ydata <- class.v
Fit model model penalizing by the hubs using the cross-validation function by
cv.glmHub
.
fitted <- cv.glmHub(xdata, ydata,
family = 'binomial',
network = 'correlation',
nlambda = 1000,
network.options = networkOptions(cutoff = .6,
min.degree = .2))
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.
plot(fitted)
Taking the best model described by lambda.min
coefs.v <- coef(fitted, s = 'lambda.min')[,1] %>% { .[. != 0]}
coefs.v %>% {
data.frame(ensembl.id = names(.),
gene.name = geneNames(names(.))$external_gene_name,
coefficient = .,
stringsAsFactors = FALSE)
} %>%
arrange(gene.name) %>%
knitr::kable()
ensembl.id | gene.name | coefficient | |
---|---|---|---|
(Intercept) | (Intercept) | (Intercept) | -6.8189813 |
CD5 | CD5 | AMOTL1 | -1.1200445 |
NLRC4 | NLRC4 | ATR | -1.4434578 |
PIK3CB | PIK3CB | B3GALT2 | -0.3880002 |
ZBTB11 | ZBTB11 | BAG2 | -0.3325729 |
ATR | ATR | C16orf82 | 1.2498304 |
IL2 | IL2 | CD5 | 0.6327083 |
GDF11 | GDF11 | CIITA | -0.2676642 |
DCP1A | DCP1A | DCP1A | 0.2994599 |
AMOTL1 | AMOTL1 | FAM86B1 | 0.4430643 |
BAG2 | BAG2 | FNIP2 | -0.1841676 |
C16orf82 | C16orf82 | GDF11 | 0.0396368 |
FAM86B1 | FAM86B1 | GNG11 | 0.2025463 |
FNIP2 | FNIP2 | GREM2 | 0.6101759 |
MS4A4A | MS4A4A | GZMB | 1.1614779 |
B3GALT2 | B3GALT2 | HAX1 | -0.0867011 |
GNG11 | GNG11 | IL2 | 3.0659066 |
NDRG2 | NDRG2 | MMP28 | 1.1142519 |
HAX1 | HAX1 | MS4A4A | -0.1516837 |
GREM2 | GREM2 | NDRG2 | -0.2014884 |
CIITA | CIITA | NLRC4 | 0.4256103 |
GZMB | GZMB | PIK3CB | -2.7663574 |
MMP28 | MMP28 | ZBTB11 | -0.8438024 |
geneNames(names(coefs.v)) %>% { hallmarks(.$external_gene_name)$heatmap }
## Error in curl::curl_fetch_memory(url, handle = handle): Failed to connect to chat.lionproject.net port 443 after 157 ms: Couldn't connect to server
## Request failed [ERROR]. Retrying in 1.1 seconds...
## Error in curl::curl_fetch_memory(url, handle = handle): Failed to connect to chat.lionproject.net port 443 after 118 ms: Couldn't connect to server
## Request failed [ERROR]. Retrying in 1.1 seconds...
## Cannot call Hallmark API, please try again later.
## NULL
## [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.
sessionInfo()
## R version 4.3.0 RC (2023-04-13 r84257)
## Platform: x86_64-apple-darwin20 (64-bit)
## Running under: macOS Monterey 12.6.4
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.3-x86_64/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.3-x86_64/Resources/lib/libRlapack.dylib; LAPACK version 3.11.0
##
## locale:
## [1] C/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## time zone: America/New_York
## tzcode source: internal
##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] glmSparseNet_1.18.0 glmnet_4.1-7
## [3] Matrix_1.5-4 TCGAutils_1.20.0
## [5] curatedTCGAData_1.22.1 MultiAssayExperiment_1.26.0
## [7] SummarizedExperiment_1.30.1 Biobase_2.60.0
## [9] GenomicRanges_1.52.0 GenomeInfoDb_1.36.0
## [11] IRanges_2.34.0 S4Vectors_0.38.1
## [13] BiocGenerics_0.46.0 MatrixGenerics_1.12.0
## [15] matrixStats_0.63.0 futile.logger_1.4.3
## [17] survival_3.5-5 ggplot2_3.4.2
## [19] dplyr_1.1.2 BiocStyle_2.28.0
##
## loaded via a namespace (and not attached):
## [1] jsonlite_1.8.4 shape_1.4.6
## [3] magrittr_2.0.3 magick_2.7.4
## [5] GenomicFeatures_1.52.0 farver_2.1.1
## [7] rmarkdown_2.21 BiocIO_1.10.0
## [9] zlibbioc_1.46.0 vctrs_0.6.2
## [11] memoise_2.0.1 Rsamtools_2.16.0
## [13] RCurl_1.98-1.12 htmltools_0.5.5
## [15] S4Arrays_1.0.4 forcats_1.0.0
## [17] progress_1.2.2 AnnotationHub_3.8.0
## [19] lambda.r_1.2.4 curl_5.0.0
## [21] pROC_1.18.2 sass_0.4.6
## [23] bslib_0.4.2 plyr_1.8.8
## [25] futile.options_1.0.1 cachem_1.0.8
## [27] GenomicAlignments_1.36.0 mime_0.12
## [29] lifecycle_1.0.3 iterators_1.0.14
## [31] pkgconfig_2.0.3 R6_2.5.1
## [33] fastmap_1.1.1 GenomeInfoDbData_1.2.10
## [35] shiny_1.7.4 digest_0.6.31
## [37] colorspace_2.1-0 AnnotationDbi_1.62.1
## [39] ExperimentHub_2.8.0 RSQLite_2.3.1
## [41] filelock_1.0.2 labeling_0.4.2
## [43] fansi_1.0.4 httr_1.4.6
## [45] compiler_4.3.0 bit64_4.0.5
## [47] withr_2.5.0 BiocParallel_1.34.1
## [49] DBI_1.1.3 highr_0.10
## [51] biomaRt_2.56.0 rappdirs_0.3.3
## [53] DelayedArray_0.26.2 rjson_0.2.21
## [55] tools_4.3.0 interactiveDisplayBase_1.38.0
## [57] httpuv_1.6.11 glue_1.6.2
## [59] restfulr_0.0.15 promises_1.2.0.1
## [61] grid_4.3.0 generics_0.1.3
## [63] gtable_0.3.3 tzdb_0.4.0
## [65] hms_1.1.3 xml2_1.3.4
## [67] utf8_1.2.3 XVector_0.40.0
## [69] BiocVersion_3.17.1 foreach_1.5.2
## [71] pillar_1.9.0 stringr_1.5.0
## [73] later_1.3.1 splines_4.3.0
## [75] BiocFileCache_2.8.0 lattice_0.21-8
## [77] rtracklayer_1.60.0 bit_4.0.5
## [79] tidyselect_1.2.0 Biostrings_2.68.0
## [81] knitr_1.42 bookdown_0.34
## [83] xfun_0.39 stringi_1.7.12
## [85] yaml_2.3.7 evaluate_0.21
## [87] codetools_0.2-19 tibble_3.2.1
## [89] BiocManager_1.30.20 cli_3.6.1
## [91] xtable_1.8-4 munsell_0.5.0
## [93] jquerylib_0.1.4 Rcpp_1.0.10
## [95] GenomicDataCommons_1.24.0 dbplyr_2.3.2
## [97] png_0.1-8 XML_3.99-0.14
## [99] parallel_4.3.0 ellipsis_0.3.2
## [101] readr_2.1.4 blob_1.2.4
## [103] prettyunits_1.1.1 bitops_1.0-7
## [105] scales_1.2.1 purrr_1.0.1
## [107] crayon_1.5.2 rlang_1.1.1
## [109] KEGGREST_1.40.0 rvest_1.0.3
## [111] formatR_1.14