Blasso： Integrating LASSO regression and bootstrapping algorithm to find best prognostic or predictive feature
The package is not yet on CRAN. You can install from Github:
if (!requireNamespace("devtools", quietly = TRUE)) install.packages("devtools")
if (!requireNamespace("Blasso", quietly = TRUE)) devtools::install_github("DongqiangZeng0808/Blasso")Loading packages and main function in the package:
library(Blasso)
help("best_predictor_cox")
help("best_predictor_binomial")Supplementary data
data("target")
head(target)
#> ID status time
#> 1 SAM00b9e5c52da9 1 1.9055441
#> 2 SAM0257bbbbd388 1 15.6386037
#> 3 SAM025b45c27e05 1 8.7720739
#> 4 SAM032c642382a7 1 2.4969199
#> 5 SAM04c589eb3fb3 0 0.6899384
#> 6 SAM0571f17f4045 1 4.5338809
data("features")
features[1:5,1:5]
#> ID Glycosphosphatidylinositol_PCA Macrophage_M1_cibersort
#> 1 SAM00b9e5c52da9 -0.3791156 -0.9651426
#> 2 SAM0257bbbbd388 1.3471887 -0.8690076
#> 3 SAM025b45c27e05 -0.1356366 -0.9915367
#> 4 SAM032c642382a7 -1.5168052 0.8050212
#> 5 SAM04c589eb3fb3 -3.0750685 0.6753930
#> GO_CATECHOLAMINE_TRANSPORT GO_DOPAMINE_TRANSPORT
#> 1 -0.05571328 -0.2575771
#> 2 -0.27535773 -0.3974832
#> 3 0.74345430 0.5631046
#> 4 -1.69420060 -1.4923073
#> 5 -1.58320438 -1.3433413res<-best_predictor_cox(target_data = target,
features = features,
status = "status",
time = "time",
nfolds = 10,
permutation = 300,
show_progress = FALSE,
palette = "Greys")
head(res$res, n = 10)
#> res Freq
#> 1 Macrophage M1 cibersort 272
#> 2 GO RESPONSE TO COBALT ION 235
#> 3 GO REGULATION OF CHOLESTEROL HOMEOSTASIS 218
#> 4 GO NEUROTRANSMITTER RECEPTOR ACTIVITY 213
#> 5 GO IMIDAZOLE CONTAINING COMPOUND METABOLIC PROCESS 198
#> 6 Dendritic cell activated cibersort 191
#> 7 Glycosphosphatidylinositol PCA 182
#> 8 GO CELL CELL ADHESION VIA PLASMA MEMBRANE ADHESION MOLECULES 182
#> 9 GO REGULATION OF DEFENSE RESPONSE TO BACTERIUM 164
#> 10 T cell CD4 posi memory activated cibersort 162res<-best_predictor_binomial(target_data = target,
features = features,
response = "status",
nfolds = 10,
permutation = 300,
show_progress = FALSE,
palette = "Blues")
head(res$res, n = 10)
#> res Freq
#> 2 Macrophage M1 cibersort 280
#> 3 GO RESPONSE TO COBALT ION 274
#> 4 GO SOMATIC STEM CELL POPULATION MAINTENANCE 252
#> 5 GO M BAND 241
#> 6 GO NEUROTRANSMITTER RECEPTOR ACTIVITY 236
#> 7 GO REGULATION OF CHOLESTEROL HOMEOSTASIS 236
#> 8 Dendritic cell activated cibersort 233
#> 9 GO RECEPTOR SIGNALING PROTEIN SERINE THREONINE KINASE ACTIVITY 224
#> 10 GO CENTROSOME LOCALIZATION 215
#> 11 Glycosphosphatidylinositol PCA 207sessionInfo()
#> R version 3.6.3 (2020-02-29)
#> Platform: x86_64-w64-mingw32/x64 (64-bit)
#> Running under: Windows 10 x64 (build 19041)
#>
#> Matrix products: default
#>
#> locale:
#> [1] LC_COLLATE=Chinese (Simplified)_China.936
#> [2] LC_CTYPE=Chinese (Simplified)_China.936
#> [3] LC_MONETARY=Chinese (Simplified)_China.936
#> [4] LC_NUMERIC=C
#> [5] LC_TIME=Chinese (Simplified)_China.936
#>
#> attached base packages:
#> [1] stats graphics grDevices utils datasets methods base
#>
#> other attached packages:
#> [1] Blasso_0.1.0 stringr_1.4.0 progress_1.2.2 RColorBrewer_1.1-2
#> [5] survival_3.2-3 tibble_3.0.3 ggplot2_3.3.3 glmnet_4.0-2
#> [9] Matrix_1.2-18
#>
#> loaded via a namespace (and not attached):
#> [1] shape_1.4.5 tidyselect_1.1.0 xfun_0.16 remotes_2.2.0
#> [5] purrr_0.3.4 splines_3.6.3 lattice_0.20-41 generics_0.1.0
#> [9] colorspace_1.4-1 vctrs_0.3.2 testthat_2.3.2 usethis_2.0.0
#> [13] htmltools_0.5.0 yaml_2.2.1 rlang_0.4.8 pkgbuild_1.2.0
#> [17] pillar_1.4.7 glue_1.4.2 withr_2.3.0 sessioninfo_1.1.1
#> [21] foreach_1.5.1 lifecycle_0.2.0 munsell_0.5.0 gtable_0.3.0
#> [25] devtools_2.3.2 codetools_0.2-18 memoise_1.1.0 evaluate_0.14
#> [29] labeling_0.4.2 knitr_1.30 callr_3.5.1 ps_1.5.0
#> [33] fansi_0.4.1 scales_1.1.1 desc_1.2.0 pkgload_1.1.0
#> [37] farver_2.0.3 fs_1.4.2 hms_0.5.3 digest_0.6.25
#> [41] stringi_1.5.3 processx_3.4.5 dplyr_1.0.2 grid_3.6.3
#> [45] rprojroot_2.0.2 cli_2.2.0 tools_3.6.3 magrittr_2.0.1
#> [49] crayon_1.3.4 pkgconfig_2.0.3 ellipsis_0.3.1 prettyunits_1.1.1
#> [53] assertthat_0.2.1 rmarkdown_2.6 iterators_1.0.13 R6_2.5.0
#> [57] compiler_3.6.3Zeng D, Ye Z, Wu J, Zhou R, Fan X, Wang G, Huang Y, Wu J, Sun H, Wang M, Bin J, Liao Y, Li N, Shi M, Liao W. Macrophage correlates with immunophenotype and predicts anti-PD-L1 response of urothelial cancer. Theranostics 2020; 10(15):7002-7014. doi:10.7150/thno.46176
Contact: E-mail any questions to [email protected]
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