Last updated: 2024-05-24
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library(viper)dset <- readRDS("output/viper/expr_processed/kirc.RDS")
signature <- rowTtest(dset, "description", "tumor", "normal")
signature <- (qnorm(signature$p.value/2, lower.tail = FALSE) * sign(signature$statistic))[, 1]nullmodel <- ttestNull(dset, "description", "tumor", "normal", per = 1000, repos = TRUE, verbose = FALSE)regulon <- readRDS("output/viper/regul_object/kirc.RDS")
regulonObject of class regulon with 3045 regulators, 9693 targets and 107201 interactionsmrs <- msviper(signature, regulon, nullmodel, verbose = FALSE)
summary(mrs) Regulon Size NES p.value FDR
EGF EGF 43 -3.66 2.48e-04 0.0307
STAP1 STAP1 35 -3.67 2.39e-04 0.0307
DMRT2 DMRT2 101 -3.68 2.33e-04 0.0307
FGF9 FGF9 53 -3.69 2.26e-04 0.0307
TMPRSS2 TMPRSS2 59 -3.74 1.85e-04 0.0307
KNG1 KNG1 57 -3.82 1.33e-04 0.0307
GCGR GCGR 65 -3.84 1.23e-04 0.0307
FOXI1 FOXI1 83 -3.92 8.99e-05 0.0307
PIK3C2G PIK3C2G 32 -3.97 7.13e-05 0.0307
OXGR1 OXGR1 89 -4.04 5.34e-05 0.0307msVIPER infers the relative activity of a regulatory gene based on
the enrichment of its most closely regulated targets on a given GES, but
does not identify which are the target genes enriched in the GES.
Subramanian et al. proposed a method called leading-edge analysis to
identify the genes driving the enrichment of a gene-set on a GES based
on Gene Set Enrichment Analysis (GSEA). We implemented the leading-edge
analysis in the ledge function of the viper package. The function only
has a
msviper' class object as argument and generates an updatedmsviper’
object that now includes a `ledge’ slot.
mrs <- ledge(mrs)
summary(mrs) Regulon Size NES p.value FDR
EGF EGF 43 -3.66 2.48e-04 0.0307
STAP1 STAP1 35 -3.67 2.39e-04 0.0307
DMRT2 DMRT2 101 -3.68 2.33e-04 0.0307
FGF9 FGF9 53 -3.69 2.26e-04 0.0307
TMPRSS2 TMPRSS2 59 -3.74 1.85e-04 0.0307
KNG1 KNG1 57 -3.82 1.33e-04 0.0307
GCGR GCGR 65 -3.84 1.23e-04 0.0307
FOXI1 FOXI1 83 -3.92 8.99e-05 0.0307
PIK3C2G PIK3C2G 32 -3.97 7.13e-05 0.0307
OXGR1 OXGR1 89 -4.04 5.34e-05 0.0307
Ledge
EGF DCLK1, SKA3, GNG7, TTR, + 34 genes
STAP1 KCNE1, LY9, CHAC1, KIT, + 25 genes
DMRT2 PART1, TPD52L1, CKMT2, C6orf52, + 88 genes
FGF9 ADRB1, TUBB2A, PRR15L, LINC01587, + 44 genes
TMPRSS2 C1orf168, GRHL1, WSCD2, RPF2, + 48 genes
KNG1 TDRD5, SNX33, ZCCHC16, PTPN13, + 49 genes
GCGR HELZ2, NDRG2, TMSB15B, COL28A1, + 56 genes
FOXI1 COQ3, PTPN3, APOBEC3F, C1orf168, + 61 genes
PIK3C2G ALDH1A2, CLUL1, STAP1, CEP55, + 27 genes
OXGR1 C9orf84, LZTS1, ADRB1, LINC01587, + 77 genessignature <- bootstrapTtest(dset, "description", "tumor", "normal", per = 1000, verbose = FALSE)
mrs <- msviper(signature, regulon, nullmodel, verbose = FALSE)mrs <- bootstrapmsviper(mrs, "mode")
summary(mrs) Regulon Size NES p.value FDR
STAP1 STAP1 35 -3.65 2.57e-04 0.0325
DMRT2 DMRT2 101 -3.66 2.57e-04 0.0325
EGF EGF 43 -3.66 2.52e-04 0.0325
FGF9 FGF9 53 -3.67 2.40e-04 0.0325
TMPRSS2 TMPRSS2 59 -3.70 2.17e-04 0.0325
KNG1 KNG1 57 -3.81 1.40e-04 0.0325
GCGR GCGR 65 -3.81 1.37e-04 0.0325
FOXI1 FOXI1 83 -3.89 9.89e-05 0.0325
PIK3C2G PIK3C2G 32 -3.96 7.39e-05 0.0325
OXGR1 OXGR1 89 -4.02 5.75e-05 0.0325mrshadow <- shadow(mrs, regulators = 25, verbose = FALSE)summary(mrshadow)$msviper.results
Regulon Size NES p.value FDR
EGF EGF 36 -3.63 2.83e-04 0.0124
NRL NRL 63 -3.67 2.43e-04 0.0124
STAP1 STAP1 28 -3.70 2.12e-04 0.0124
ASB5 ASB5 30 -3.72 2.02e-04 0.0124
KNG1 KNG1 39 -3.72 1.97e-04 0.0124
TMPRSS2 TMPRSS2 45 -3.79 1.48e-04 0.0124
OXGR1 OXGR1 70 -3.81 1.37e-04 0.0124
GCGR GCGR 53 -3.87 1.10e-04 0.0124
FOXI1 FOXI1 67 -3.88 1.04e-04 0.0124
PIK3C2G PIK3C2G 29 -3.98 7.02e-05 0.0124
$Shadow.pairs
[1] "OXGR1 -> FGF9" "OXGR1 -> DMRT2" "OXGR1 -> NR0B2" "PIK3C2G -> DMRT2"
[5] "FOXI1 -> KNG1" "GCGR -> DMRT2" mrs <- msviperCombinatorial(mrs, verbose = FALSE)
mrs <- msviperSynergy(mrs, verbose = FALSE)
summary(mrs) Regulon Size NES p.value FDR Synergy
VGLL1--ABCG1 VGLL1--ABCG1 30 12.71 5.42e-37 7.39e-34 4.18e-25
PDLIM1--SRGAP3 PDLIM1--SRGAP3 27 12.46 1.23e-35 8.39e-33 3.49e-07
DMRT2--BMP7 DMRT2--BMP7 30 4.57 4.94e-06 2.24e-03 7.32e-04
PAK6--VGLL1 PAK6--VGLL1 30 4.46 8.31e-06 2.83e-03 2.94e-04
BMP7--VGLL1 BMP7--VGLL1 41 4.32 1.53e-05 3.82e-03 1.87e-06
PRDM16--VGLL1 PRDM16--VGLL1 29 4.28 1.87e-05 3.82e-03 5.95e-04
GPC3--VGLL1 GPC3--VGLL1 25 4.27 1.96e-05 3.82e-03 7.52e-04
ADGRF1--SRGAP3 ADGRF1--SRGAP3 27 4.03 5.65e-05 8.37e-03 4.18e-05
TFAP2B--VGLL1 TFAP2B--VGLL1 39 4.01 6.14e-05 8.37e-03 3.22e-06
OXGR1 OXGR1 89 -4.02 5.75e-05 8.37e-03 NAvpres <- viper(dset, regulon, verbose = FALSE)tmp <- rowTtest(vpres, "description", "tumor", "normal")
data.frame(Gene = rownames(tmp$p.value), t = round(tmp$statistic, 2), "p-value" = signif(tmp$p.value, 3))[order(tmp$p.value)[1:10], ] Gene t p.value
KNG1 KNG1 -60.76 1.25e-118
FOXI1 FOXI1 -59.71 2.25e-117
DMRT2 DMRT2 -59.57 3.28e-117
TMPRSS2 TMPRSS2 -58.73 3.41e-116
FGF9 FGF9 -58.31 1.12e-115
GCGR GCGR -57.31 1.91e-114
OXGR1 OXGR1 -57.27 2.12e-114
PIK3C2G PIK3C2G -56.97 5.15e-114
EGF EGF -56.94 5.62e-114
PAK5 PAK5 -56.59 1.53e-113
sessionInfo()R version 4.4.0 (2024-04-24)
Platform: aarch64-apple-darwin20
Running under: macOS Sonoma 14.5
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRlapack.dylib; LAPACK version 3.12.0
locale:
[1] en_US.UTF-8/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] stats graphics grDevices utils datasets methods base
other attached packages:
[1] viper_1.38.0 Biobase_2.64.0 BiocGenerics_0.50.0
[4] workflowr_1.7.1
loaded via a namespace (and not attached):
[1] gtable_0.3.5 xfun_0.44 bslib_0.7.0 ggplot2_3.5.1
[5] htmlwidgets_1.6.4 processx_3.8.4 lattice_0.22-6 callr_3.7.6
[9] vctrs_0.6.5 tools_4.4.0 ps_1.7.6 generics_0.1.3
[13] parallel_4.4.0 tibble_3.2.1 proxy_0.4-27 fansi_1.0.6
[17] pkgconfig_2.0.3 Matrix_1.7-0 KernSmooth_2.23-24 data.table_1.15.4
[21] lifecycle_1.0.4 compiler_4.4.0 stringr_1.5.1 git2r_0.33.0
[25] mixtools_2.0.0 munsell_0.5.1 getPass_0.2-4 httpuv_1.6.15
[29] htmltools_0.5.8.1 class_7.3-22 sass_0.4.9 yaml_2.3.8
[33] lazyeval_0.2.2 plotly_4.10.4 later_1.3.2 pillar_1.9.0
[37] jquerylib_0.1.4 whisker_0.4.1 tidyr_1.3.1 MASS_7.3-60.2
[41] cachem_1.1.0 nlme_3.1-164 tidyselect_1.2.1 digest_0.6.35
[45] stringi_1.8.4 kernlab_0.9-32 dplyr_1.1.4 purrr_1.0.2
[49] splines_4.4.0 rprojroot_2.0.4 fastmap_1.2.0 grid_4.4.0
[53] colorspace_2.1-0 cli_3.6.2 magrittr_2.0.3 survival_3.6-4
[57] utf8_1.2.4 e1071_1.7-14 scales_1.3.0 promises_1.3.0
[61] segmented_2.1-0 rmarkdown_2.27 httr_1.4.7 evaluate_0.23
[65] knitr_1.46 viridisLite_0.4.2 rlang_1.1.3 Rcpp_1.0.12
[69] glue_1.7.0 rstudioapi_0.16.0 jsonlite_1.8.8 R6_2.5.1
[73] fs_1.6.4