powsimR
Version 1.2.4
powsimR
¡Consulte también mi página Github de Powsimr hecha con PKGDOWN!
Para la instalación, se necesita el paquete R devtools .
install.packages( " devtools " )
library( devtools )Recomiendo instalar primero las dependencias manualmente y luego PowsImr. Si planea usar magia para la imputación, siga sus instrucciones para instalar la implementación de Python antes de instalar 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 " )Para verificar si todas las dependencias están instaladas, puede ejecutar las siguientes líneas:
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 FALSEDespués de instalar las dependencias, PowsImr también se puede instalar utilizando DevTools.
devtools :: install_github( " bvieth/powsimR " , build_vignettes = TRUE , dependencies = FALSE )
library( " powsimR " )Alternativa, puede intentar instalar PowsImr y sus dependencias directamente usando DevTools:
devtools :: install_github( " bvieth/powsimR " )Para ver ejemplos y consejos sobre el uso del paquete, consulte la viñeta después de la instalación exitosa por
browseVignettes( " powsimR " )Algunos usuarios han experimentado problemas para instalar PowsImr debido a errores de compilación de viñetas o porque están perdiendo los paquetes R necesarios para construir la viñeta, es decir, Knitr y RMDFormats. Si ese es el caso, puede instalar estas dependencias o dejar de lado la construcción de la viñeta (configurando build_vignettes en falso) y leerla en mi página GitHub de Powsimr o descargarla como un archivo HTML aquí.
Tenga en cuenta que el error "El número máximo de DLL alcanzados ..." podría ocurrir debido a la carga de muchos objetos compartidos por paquetes de bioconductores. Reiniciar la sesión R después de instalar dependencias / POWSIMR ayudará. Comenzando con R versión 3.4.0, se puede establecer la variable ambiental 'r_max_num_dlls' en un número más alto. Ver ?Startup() para obtener más información. Recomiendo aumentar el número máximo de DLL que se pueden cargar a 500. La variable ambiental r_max_num_dlls se puede configurar en r_home/etc/renviron antes de comenzar R. para que localice el archivo renviron y agregue la siguiente línea: r_max_num_dlls = xy donde xy es el número de dlls. En mi máquina Ubuntu, el archivo Renviron está en/usr/lib/r/etc/y puedo configurarlo en 500.
Además, los límites del usuario para archivos abiertos (UNIX: ULIMIT) podrían tener que establecerse en un número más alto para acomodar el aumento en las DLL. Consulte las páginas de ayuda para Mac y Linux para obtener orientación.
Utilice la siguiente entrada para citar Powsimr.
citation( " powsimR " )POSIMR se publica en bioinformática. Un papel de preimpresión también está en Biorxiv.
Envíe informes de errores y solicitudes de funciones abriendo un nuevo problema en esta página. Trato de mantenerte al día con los nuevos desarrollos / cambios de métodos implementados en POWSIMR, pero si encuentra errores de ejecución mientras usa una determinada herramienta (por ejemplo, para la imputación), entonces aprecio si puede publicar esto como un problema.
R Información de la sesión 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
