Please also consult my Github Page of powsimR made with pkgdown!
For the installation, the R package devtools is needed.
install.packages("devtools")
library(devtools)I recommend to install first the dependencies manually and then powsimR. If you plan to use MAGIC for imputation, then please follow their instruction to install the python implementation before installing powsimR.
ipak <- function(pkg, repository = c("CRAN", "Bioconductor", "github")) {
new.pkg <- pkg[!(pkg %in% installed.packages()[, "Package"])]
# new.pkg <- pkg
if (length(new.pkg)) {
if (repository == "CRAN") {
install.packages(new.pkg, dependencies = TRUE)
}
if (repository == "Bioconductor") {
if (strsplit(version[["version.string"]], " ")[[1]][3] > "4.0.0") {
if (!requireNamespace("BiocManager")) {
install.packages("BiocManager")
}
BiocManager::install(new.pkg, dependencies = TRUE, ask = FALSE)
}
if (strsplit(version[["version.string"]], " ")[[1]][3] < "3.6.0") {
stop(message("powsimR depends on packages and functions that are only available in R 4.0.0 and higher."))
}
}
if (repository == "github") {
devtools::install_github(new.pkg, build_vignettes = FALSE, force = FALSE,
dependencies = TRUE)
}
}
}
# CRAN PACKAGES
cranpackages <- c("broom", "cobs", "cowplot", "data.table", "doParallel", "dplyr",
"DrImpute", "fastICA", "fitdistrplus", "foreach", "future", "gamlss.dist", "ggplot2",
"ggpubr", "ggstance", "grDevices", "grid", "Hmisc", "kernlab", "MASS", "magrittr",
"MBESS", "Matrix", "matrixStats", "mclust", "methods", "minpack.lm", "moments",
"msir", "NBPSeq", "nonnest2", "parallel", "penalized", "plyr", "pscl", "reshape2",
"Rmagic", "rsvd", "Rtsne", "scales", "Seurat", "snow", "sctransform", "stats",
"tibble", "tidyr", "truncnorm", "VGAM", "ZIM", "zoo")
ipak(cranpackages, repository = "CRAN")
# BIOCONDUCTOR
biocpackages <- c("bayNorm", "baySeq", "BiocGenerics", "BiocParallel", "DESeq2",
"EBSeq", "edgeR", "IHW", "iCOBRA", "limma", "Linnorm", "MAST", "monocle", "NOISeq",
"qvalue", "ROTS", "RUVSeq", "S4Vectors", "scater", "scDD", "scde", "scone", "scran",
"SCnorm", "SingleCellExperiment", "SummarizedExperiment", "zinbwave")
ipak(biocpackages, repository = "Bioconductor")
# GITHUB
githubpackages <- c("cz-ye/DECENT", "nghiavtr/BPSC", "mohuangx/SAVER", "statOmics/zingeR",
"Vivianstats/scImpute")
ipak(githubpackages, repository = "github")To check whether all dependencies are installed, you can run the following lines:
powsimRdeps <- data.frame(Package = c(cranpackages,
biocpackages,
sapply(strsplit(githubpackages, "/"), "[[", 2)),
stringsAsFactors = F)
ip <- as.data.frame(installed.packages()[,c(1,3:4)], stringsAsFactors = F)
ip.check <- cbind(powsimRdeps,
Version = ip[match(powsimRdeps$Package, rownames(ip)),"Version"])
table(is.na(ip.check$Version)) # all should be FALSEAfter installing the dependencies, powsimR can be installed by using devtools as well.
devtools::install_github("bvieth/powsimR", build_vignettes = TRUE, dependencies = FALSE)
library("powsimR")Alternative, you can try to install powsimR and its dependencies directly using devtools:
devtools::install_github("bvieth/powsimR")For examples and tips on using the package, please consult the vignette after successful installation by
browseVignettes("powsimR")Some users have experienced issues installing powsimR due to vignette compilation errors or because they are missing the necessary R packages to build the vignette, i.e. knitr and rmdformats. If that is the case, you can either install these dependencies or leave out building the vignette (by setting build_vignettes to FALSE) and read it on my Github Page of powsimR or download it as a html file here.
Note that the error “maximal number of DLLs reached…” might occur due to
the loading of many shared objects by Bioconductor packages. Restarting
the R session after installing dependencies / powsimR will help.
Starting with R version 3.4.0, one can set the environmental variable
‘R_MAX_NUM_DLLS’ to a higher number. See ?Startup() for more
information. I recommend to increase the maximum number of DLLs that can
be loaded to 500. The environmental variable R_MAX_NUM_DLLS can be set
in R_HOME/etc/Renviron prior to starting R. For that locate the Renviron
file and add the following line: R_MAX_NUM_DLLS=xy where xy is the
number of DLLs. On my Ubuntu machine, the Renviron file is in
/usr/lib/R/etc/ and I can set it to 500.
In addition, the user limits for open files (unix: ulimit) might have to be set to a higher number to accomodate the increase in DLLs. Please check out the help pages for MACs and Linux for guidance.
Please use the following entry for citing powsimR.
citation("powsimR")powsimR is published in Bioinformatics. A preprint paper is also on bioRxiv.
Please send bug reports and feature requests by opening a new issue on this page. I try to keep up to date with new developments / changes of methods implemented in powsimR, but if you encounter run errors while using a certain tool (e.g. for imputation), then I appreciate if you can post this as an issue.
library(powsimR)
#> Loading required package: gamlss.dist
#> Loading required package: MASS
#> Registered S3 method overwritten by 'gdata':
#> method from
#> reorder.factor gplots
#> Warning: replacing previous import 'DECENT::lrTest' by 'MAST::lrTest' when
#> loading 'powsimR'
#> Warning: replacing previous import 'penalized::predict' by 'stats::predict' when
#> loading 'powsimR'
#> Warning: replacing previous import 'zinbwave::glmWeightedF' by
#> 'zingeR::glmWeightedF' when loading 'powsimR'
sessionInfo()
#> R version 4.1.2 (2021-11-01)
#> Platform: x86_64-pc-linux-gnu (64-bit)
#> Running under: Ubuntu 18.04.6 LTS
#>
#> Matrix products: default
#> BLAS: /usr/lib/x86_64-linux-gnu/openblas/libblas.so.3
#> LAPACK: /usr/lib/x86_64-linux-gnu/libopenblasp-r0.2.20.so
#>
#> locale:
#> [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
#> [3] LC_TIME=de_DE.UTF-8 LC_COLLATE=en_US.UTF-8
#> [5] LC_MONETARY=de_DE.UTF-8 LC_MESSAGES=en_US.UTF-8
#> [7] LC_PAPER=de_DE.UTF-8 LC_NAME=C
#> [9] LC_ADDRESS=C LC_TELEPHONE=C
#> [11] LC_MEASUREMENT=de_DE.UTF-8 LC_IDENTIFICATION=C
#>
#> attached base packages:
#> [1] stats graphics grDevices utils datasets methods base
#>
#> other attached packages:
#> [1] powsimR_1.2.3 gamlss.dist_6.0-1 MASS_7.3-54
#>
#> loaded via a namespace (and not attached):
#> [1] mixtools_1.2.0 softImpute_1.4-1
#> [3] minpack.lm_1.2-1 lattice_0.20-45
#> [5] vctrs_0.3.8 fastICA_1.2-3
#> [7] mgcv_1.8-38 penalized_0.9-51
#> [9] blob_1.2.2 survival_3.2-13
#> [11] prodlim_2019.11.13 Rmagic_2.0.3
#> [13] later_1.3.0 nloptr_1.2.2.3
#> [15] DBI_1.1.1 R.utils_2.11.0
#> [17] rappdirs_0.3.3 SingleCellExperiment_1.16.0
#> [19] Linnorm_2.18.0 dqrng_0.3.0
#> [21] jpeg_0.1-9 zlibbioc_1.40.0
#> [23] MatrixModels_0.5-0 htmlwidgets_1.5.4
#> [25] mvtnorm_1.1-3 future_1.23.0
#> [27] UpSetR_1.4.0 parallel_4.1.2
#> [29] scater_1.22.0 irlba_2.3.3
#> [31] DEoptimR_1.0-9 Rcpp_1.0.7
#> [33] KernSmooth_2.23-20 DT_0.20
#> [35] promises_1.2.0.1 gdata_2.18.0
#> [37] DDRTree_0.1.5 DelayedArray_0.20.0
#> [39] limma_3.50.0 vegan_2.5-7
#> [41] Hmisc_4.6-0 ShortRead_1.52.0
#> [43] apcluster_1.4.8 RSpectra_0.16-0
#> [45] msir_1.3.3 mnormt_2.0.2
#> [47] digest_0.6.28 png_0.1-7
#> [49] bluster_1.4.0 qlcMatrix_0.9.7
#> [51] sctransform_0.3.2 cowplot_1.1.1
#> [53] pkgconfig_2.0.3 docopt_0.7.1
#> [55] DelayedMatrixStats_1.16.0 gower_0.2.2
#> [57] ggbeeswarm_0.6.0 iterators_1.0.13
#> [59] minqa_1.2.4 lavaan_0.6-9
#> [61] reticulate_1.22 SummarizedExperiment_1.24.0
#> [63] spam_2.7-0 beeswarm_0.4.0
#> [65] modeltools_0.2-23 xfun_0.28
#> [67] zoo_1.8-9 tidyselect_1.1.1
#> [69] ZIM_1.1.0 reshape2_1.4.4
#> [71] purrr_0.3.4 kernlab_0.9-29
#> [73] EDASeq_2.28.0 viridisLite_0.4.0
#> [75] snow_0.4-4 rtracklayer_1.54.0
#> [77] rlang_0.4.12 hexbin_1.28.2
#> [79] glue_1.5.0 RColorBrewer_1.1-2
#> [81] fpc_2.2-9 matrixStats_0.61.0
#> [83] MatrixGenerics_1.6.0 stringr_1.4.0
#> [85] lava_1.6.10 fields_13.3
#> [87] ggsignif_0.6.3 DESeq2_1.34.0
#> [89] recipes_0.1.17 SparseM_1.81
#> [91] httpuv_1.6.3 class_7.3-19
#> [93] BPSC_0.99.2 BiocNeighbors_1.12.0
#> [95] annotate_1.72.0 jsonlite_1.7.2
#> [97] XVector_0.34.0 tmvnsim_1.0-2
#> [99] bit_4.0.4 mime_0.12
#> [101] gridExtra_2.3 gplots_3.1.1
#> [103] Rsamtools_2.10.0 zingeR_0.1.0
#> [105] stringi_1.7.5 gmodels_2.18.1
#> [107] rhdf5filters_1.6.0 bitops_1.0-7
#> [109] maps_3.4.0 RSQLite_2.2.8
#> [111] tidyr_1.1.4 pheatmap_1.0.12
#> [113] data.table_1.14.2 rstudioapi_0.13
#> [115] GenomicAlignments_1.30.0 nlme_3.1-153
#> [117] qvalue_2.26.0 scran_1.22.1
#> [119] fastcluster_1.2.3 locfit_1.5-9.4
#> [121] scone_1.18.0 listenv_0.8.0
#> [123] cobs_1.3-4 R.oo_1.24.0
#> [125] prabclus_2.3-2 segmented_1.3-4
#> [127] dbplyr_2.1.1 BiocGenerics_0.40.0
#> [129] lifecycle_1.0.1 timeDate_3043.102
#> [131] ROTS_1.22.0 munsell_0.5.0
#> [133] hwriter_1.3.2 R.methodsS3_1.8.1
#> [135] moments_0.14 caTools_1.18.2
#> [137] codetools_0.2-18 coda_0.19-4
#> [139] Biobase_2.54.0 GenomeInfoDb_1.30.0
#> [141] vipor_0.4.5 htmlTable_2.3.0
#> [143] bayNorm_1.12.0 rARPACK_0.11-0
#> [145] xtable_1.8-4 SAVER_1.1.2
#> [147] ROCR_1.0-11 diptest_0.76-0
#> [149] formatR_1.11 lpsymphony_1.22.0
#> [151] abind_1.4-5 FNN_1.1.3
#> [153] parallelly_1.29.0 RANN_2.6.1
#> [155] sparsesvd_0.2 CompQuadForm_1.4.3
#> [157] BiocIO_1.4.0 GenomicRanges_1.46.1
#> [159] tibble_3.1.6 ggdendro_0.1.22
#> [161] cluster_2.1.2 future.apply_1.8.1
#> [163] Matrix_1.3-4 ellipsis_0.3.2
#> [165] prettyunits_1.1.1 shinyBS_0.61
#> [167] lubridate_1.8.0 NOISeq_2.38.0
#> [169] shinydashboard_0.7.2 mclust_5.4.8
#> [171] igraph_1.2.9 ggstance_0.3.5
#> [173] slam_0.1-49 testthat_3.1.0
#> [175] doSNOW_1.0.19 htmltools_0.5.2
#> [177] BiocFileCache_2.2.0 GenomicFeatures_1.46.1
#> [179] yaml_2.2.1 utf8_1.2.2
#> [181] XML_3.99-0.8 ModelMetrics_1.2.2.2
#> [183] ggpubr_0.4.0 DrImpute_1.0
#> [185] foreign_0.8-81 withr_2.4.2
#> [187] scuttle_1.4.0 fitdistrplus_1.1-6
#> [189] BiocParallel_1.28.2 aroma.light_3.24.0
#> [191] bit64_4.0.5 foreach_1.5.1
#> [193] robustbase_0.93-9 outliers_0.14
#> [195] Biostrings_2.62.0 combinat_0.0-8
#> [197] rsvd_1.0.5 ScaledMatrix_1.2.0
#> [199] iCOBRA_1.22.1 memoise_2.0.1
#> [201] evaluate_0.14 VGAM_1.1-5
#> [203] nonnest2_0.5-5 geneplotter_1.72.0
#> [205] permute_0.9-5 caret_6.0-90
#> [207] curl_4.3.2 fdrtool_1.2.17
#> [209] fansi_0.5.0 conquer_1.2.1
#> [211] edgeR_3.36.0 checkmate_2.0.0
#> [213] cachem_1.0.6 truncnorm_1.0-8
#> [215] tensorA_0.36.2 DECENT_1.1.0
#> [217] ellipse_0.4.2 rjson_0.2.20
#> [219] metapod_1.2.0 ggplot2_3.3.5
#> [221] rstatix_0.7.0 ggrepel_0.9.1
#> [223] scDD_1.18.0 tools_4.1.2
#> [225] sandwich_3.0-1 magrittr_2.0.1
#> [227] RCurl_1.98-1.5 car_3.0-12
#> [229] pbivnorm_0.6.0 bayesm_3.1-4
#> [231] xml2_1.3.2 EBSeq_1.34.0
#> [233] httr_1.4.2 assertthat_0.2.1
#> [235] rmarkdown_2.11 Rhdf5lib_1.16.0
#> [237] boot_1.3-28 globals_0.14.0
#> [239] R6_2.5.1 nnet_7.3-16
#> [241] progress_1.2.2 genefilter_1.76.0
#> [243] KEGGREST_1.34.0 gtools_3.9.2
#> [245] statmod_1.4.36 beachmat_2.10.0
#> [247] BiocSingular_1.10.0 rhdf5_2.38.0
#> [249] splines_4.1.2 carData_3.0-4
#> [251] colorspace_2.0-2 amap_0.8-18
#> [253] generics_0.1.1 stats4_4.1.2
#> [255] NBPSeq_0.3.0 compositions_2.0-2
#> [257] base64enc_0.1-3 baySeq_2.28.0
#> [259] pillar_1.6.4 HSMMSingleCell_1.14.0
#> [261] GenomeInfoDbData_1.2.7 plyr_1.8.6
#> [263] dotCall64_1.0-1 gtable_0.3.0
#> [265] SCnorm_1.16.0 monocle_2.22.0
#> [267] restfulr_0.0.13 knitr_1.36
#> [269] RcppArmadillo_0.10.7.3.0 latticeExtra_0.6-29
#> [271] biomaRt_2.50.1 IRanges_2.28.0
#> [273] fastmap_1.1.0 doParallel_1.0.16
#> [275] pscl_1.5.5 flexmix_2.3-17
#> [277] quantreg_5.86 AnnotationDbi_1.56.2
#> [279] broom_0.7.10 filelock_1.0.2
#> [281] scales_1.1.1 arm_1.12-2
#> [283] backports_1.4.0 plotrix_3.8-2
#> [285] IHW_1.22.0 S4Vectors_0.32.3
#> [287] densityClust_0.3 ipred_0.9-12
#> [289] lme4_1.1-27.1 hms_1.1.1
#> [291] Rtsne_0.15 dplyr_1.0.7
#> [293] shiny_1.7.1 grid_4.1.2
#> [295] Formula_1.2-4 blockmodeling_1.0.5
#> [297] crayon_1.4.2 MAST_1.20.0
#> [299] RUVSeq_1.28.0 pROC_1.18.0
#> [301] sparseMatrixStats_1.6.0 viridis_0.6.2
#> [303] rpart_4.1-15 zinbwave_1.16.0
#> [305] compiler_4.1.2