SCZ 2018 Enrichment
shengqian
2022-05-16
Last updated: 2022-05-23
Checks: 6 1
Knit directory: cTWAS_analysis/
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library(gseasusie)
library(tidyverse)Warning in system("timedatectl", intern = TRUE): running command 'timedatectl'
had status 1Warning: Your system is mis-configured: '/etc/localtime' is not a symlinkWarning: It is strongly recommended to set envionment variable TZ to 'America/
Chicago' (or equivalent)genesets <- gseasusie::load_gene_sets(c('gobp'))library(data.table)
library("AnnotationDbi")
library("org.Hs.eg.db")
data <- fread(file = "data/magma.genes.out")
data$entrez = mapIds(org.Hs.eg.db,
keys=data$GENE, #Column containing Ensembl gene ids
column="ENTREZID",
keytype="ENSEMBL",
multiVals="first")
data <- na.omit(data)
data$beta <- 1
data$se <- 1
data <- data[,c("GENE","entrez","P","beta","se","ZSTAT")]
colnames(data) <- c("ENSEMBL","ENTREZID","pvalue","beta","se","threshold.on")db <- 'gobp'
thresh = 2 # threshold for binarizing the data
bin.data <- gseasusie::prep_binary_data(genesets[[db]], data, thresh)
X <- bin.data$X
y <- bin.data$y# fit logistic susie
logistic.fit <- gseasusie::fit_logistic_susie_veb_boost(X, y, L=20)ELBO: -8075.22
21.905 sec elapsed# fit linear susie
linear.fit <- susieR::susie(X, y)
# compute odds ratios, and pvalues under hypergeometric (one-sided) and fishers exact (two-sided) tests
ora <- gseasusie::fit_ora(X, y)14.182 sec elapsedgseasusie::enrichment_volcano(logistic.fit, ora)
gseasusie::interactive_table(logistic.fit, ora)
sessionInfo()R version 4.1.0 (2021-05-18)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Scientific Linux 7.4 (Nitrogen)
Matrix products: default
BLAS/LAPACK: /software/openblas-0.3.13-el7-x86_64/lib/libopenblas_haswellp-r0.3.13.so
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[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
attached base packages:
[1] stats4 stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] Matrix_1.3-3 org.Hs.eg.db_3.14.0 AnnotationDbi_1.56.1
[4] IRanges_2.28.0 S4Vectors_0.32.3 Biobase_2.54.0
[7] BiocGenerics_0.40.0 data.table_1.14.0 forcats_0.5.1
[10] stringr_1.4.0 dplyr_1.0.9 purrr_0.3.4
[13] readr_2.1.2 tidyr_1.2.0 tibble_3.1.7
[16] ggplot2_3.3.6 tidyverse_1.3.1 gseasusie_0.0.0.9000
[19] workflowr_1.7.0
loaded via a namespace (and not attached):
[1] colorspace_2.0-3 ellipsis_0.3.2 rprojroot_2.0.3
[4] XVector_0.34.0 fs_1.5.0 rstudioapi_0.13
[7] farver_2.1.0 bit64_4.0.5 fansi_1.0.3
[10] mvtnorm_1.1-3 lubridate_1.7.10 xml2_1.3.2
[13] cachem_1.0.5 knitr_1.33 jsonlite_1.8.0
[16] broom_0.7.8 dbplyr_2.1.1 png_0.1-7
[19] data.tree_1.0.0 compiler_4.1.0 httr_1.4.3
[22] tictoc_1.0.1 backports_1.2.1 assertthat_0.2.1
[25] fastmap_1.1.0 cli_3.3.0 later_1.2.0
[28] htmltools_0.5.1.1 tools_4.1.0 gtable_0.3.0
[31] glue_1.6.2 GenomeInfoDbData_1.2.7 Rcpp_1.0.8.3
[34] mr.ash.alpha_0.1-42 cellranger_1.1.0 jquerylib_0.1.4
[37] vctrs_0.4.1 Biostrings_2.62.0 crosstalk_1.1.1
[40] xfun_0.24 ps_1.6.0 rvest_1.0.0
[43] lifecycle_1.0.1 irlba_2.3.5 getPass_0.2-2
[46] zlibbioc_1.40.0 scales_1.2.0 hms_1.1.1
[49] promises_1.2.0.1 spatstat.utils_2.3-1 parallel_4.1.0
[52] emulator_1.2-21 susieR_0.11.92 yaml_2.2.1
[55] memoise_2.0.0 sass_0.4.0 reshape_0.8.9
[58] stringi_1.7.6 RSQLite_2.2.8 highr_0.9
[61] VEB.Boost_0.0.0.9037 GenomeInfoDb_1.30.0 rlang_1.0.2
[64] pkgconfig_2.0.3 bitops_1.0-7 matrixStats_0.62.0
[67] evaluate_0.14 lattice_0.20-44 htmlwidgets_1.5.3
[70] labeling_0.4.2 bit_4.0.4 processx_3.5.2
[73] tidyselect_1.1.2 plyr_1.8.7 magrittr_2.0.3
[76] R6_2.5.1 generics_0.1.2 DBI_1.1.1
[79] pillar_1.7.0 haven_2.4.1 whisker_0.4
[82] withr_2.5.0 KEGGREST_1.34.0 RCurl_1.98-1.5
[85] mixsqp_0.3-43 reactable_0.2.3 modelr_0.1.8
[88] crayon_1.5.1 utf8_1.2.2 tzdb_0.3.0
[91] rmarkdown_2.9 grid_4.1.0 readxl_1.4.0
[94] reactR_0.4.4 blob_1.2.1 callr_3.7.0
[97] git2r_0.28.0 reprex_2.0.0 digest_0.6.29
[100] httpuv_1.6.1 munsell_0.5.0 bslib_0.2.5.1