Open moontreegy opened 1 month ago
As a quick fix please try to install and call a BSgenome.UCSC.hg19 package first:
The package could be fetched on https://bioconductor.org/packages/release/data/annotation/html/BSgenome.Hsapiens.UCSC.hg19.html . You can download it and install with R CMD BSgenome.Hsapiens.UCSC.hg19_1.4.3.tar.gz
etc. Then use library(BSgenome.Hsapiens.UCSC.hg19)
before running the EpiTrace_Convergence code.
We would plan to fix this in the next cycle.
As a quick fix please try to install and call a BSgenome.UCSC.hg19 package first:
The package could be fetched on https://bioconductor.org/packages/release/data/annotation/html/BSgenome.Hsapiens.UCSC.hg19.html . You can download it and install with
R CMD BSgenome.Hsapiens.UCSC.hg19_1.4.3.tar.gz
etc. Then uselibrary(BSgenome.Hsapiens.UCSC.hg19)
before running the EpiTrace_Convergence code.We would plan to fix this in the next cycle.
I used library(BSgenome.Hsapiens.UCSC.hg19)
and library(BSgenome.Mmusculus.UCSC.mm10)
, but it still shows the same error.
Below is my env.
R version 4.2.2 (2022-10-31)
Platform: x86_64-conda-linux-gnu (64-bit)
Running under: CentOS Linux 7 (Core)
Matrix products: default
BLAS/LAPACK: /opt/conda/lib/libopenblasp-r0.3.21.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] parallel grid stats4 stats graphics grDevices utils
[8] datasets methods base
other attached packages:
[1] ChIPseeker_1.34.1 rhdf5_2.42.0
[3] SummarizedExperiment_1.28.0 Biobase_2.58.0
[5] MatrixGenerics_1.10.0 Rcpp_1.0.11
[7] matrixStats_1.2.0 data.table_1.14.8
[9] stringr_1.5.1 plyr_1.8.9
[11] magrittr_2.0.3 gtable_0.3.4
[13] gtools_3.9.5 gridExtra_2.3
[15] ArchR_1.0.2 patchwork_1.1.3
[17] ggtree_3.6.0 SeuratObject_4.1.3
[19] Seurat_4.3.0 EpiTrace_0.0.1.3
[21] Matrix_1.5-3 ggplot2_3.4.4
[23] openxlsx_4.2.5.2 reshape2_1.4.4
[25] readr_2.1.4 tidyr_1.3.0
[27] dplyr_1.1.4 BSgenome.Hsapiens.UCSC.hg19_1.4.3
[29] BSgenome_1.66.3 rtracklayer_1.58.0
[31] Biostrings_2.66.0 XVector_0.38.0
[33] GenomicRanges_1.50.2 GenomeInfoDb_1.34.9
[35] IRanges_2.32.0 S4Vectors_0.36.2
[37] BiocGenerics_0.44.0
loaded via a namespace (and not attached):
[1] rappdirs_0.3.3
[2] pbdZMQ_0.3-9
[3] scattermore_0.8
[4] easyLift_0.2.1
[5] bit64_4.0.5
[6] knitr_1.42
[7] irlba_2.3.5.1
[8] DelayedArray_0.24.0
[9] rpart_4.1.19
[10] KEGGREST_1.38.0
[11] RCurl_1.98-1.13
[12] doParallel_1.0.17
[13] generics_0.1.3
[14] GenomicFeatures_1.50.4
[15] preprocessCore_1.60.2
[16] cowplot_1.1.1
[17] RSQLite_2.3.4
[18] shadowtext_0.1.2
[19] RANN_2.6.1
[20] future_1.32.0
[21] enrichplot_1.18.0
[22] bit_4.0.5
[23] tzdb_0.4.0
[24] xml2_1.3.3
[25] spatstat.data_3.0-1
[26] httpuv_1.6.9
[27] viridis_0.6.2
[28] xfun_0.38
[29] hms_1.1.3
[30] evaluate_0.20
[31] promises_1.2.0.1
[32] fansi_1.0.6
[33] restfulr_0.0.15
[34] progress_1.2.3
[35] caTools_1.18.2
[36] dbplyr_2.3.2
[37] igraph_1.4.2
[38] DBI_1.2.0
[39] htmlwidgets_1.6.2
[40] spatstat.geom_3.1-0
[41] purrr_1.0.2
[42] ellipsis_0.3.2
[43] backports_1.4.1
[44] biomaRt_2.54.1
[45] deldir_1.0-6
[46] vctrs_0.6.5
[47] ROCR_1.0-11
[48] abind_1.4-5
[49] cachem_1.0.8
[50] withr_2.5.2
[51] ggforce_0.4.1
[52] HDO.db_0.99.1
[53] progressr_0.13.0
[54] vroom_1.6.5
[55] checkmate_2.1.0
[56] sctransform_0.3.5
[57] GenomicAlignments_1.34.1
[58] treeio_1.22.0
[59] prettyunits_1.2.0
[60] goftest_1.2-3
[61] DOSE_3.24.0
[62] cluster_2.1.4
[63] ape_5.7
[64] IRdisplay_1.1
[65] lazyeval_0.2.2
[66] crayon_1.5.2
[67] spatstat.explore_3.1-0
[68] pkgconfig_2.0.3
[69] tweenr_2.0.2
[70] nlme_3.1-162
[71] nnet_7.3-18
[72] rlang_1.1.2
[73] globals_0.16.2
[74] lifecycle_1.0.4
[75] miniUI_0.1.1.1
[76] filelock_1.0.2
[77] BiocFileCache_2.6.0
[78] polyclip_1.10-4
[79] lmtest_0.9-40
[80] aplot_0.1.10
[81] IRkernel_1.3.2
[82] boot_1.3-28.1
[83] Rhdf5lib_1.20.0
[84] zoo_1.8-12
[85] base64enc_0.1-3
[86] ggridges_0.5.4
[87] png_0.1-8
[88] viridisLite_0.4.2
[89] rjson_0.2.21
[90] bitops_1.0-7
[91] KernSmooth_2.23-20
[92] rhdf5filters_1.10.0
[93] blob_1.2.4
[94] qvalue_2.30.0
[95] parallelly_1.35.0
[96] spatstat.random_3.1-4
[97] gridGraphics_0.5-1
[98] scales_1.3.0
[99] memoise_2.0.1
[100] ica_1.0-3
[101] gplots_3.1.3
[102] zlibbioc_1.44.0
[103] scatterpie_0.1.8
[104] compiler_4.2.2
[105] BiocIO_1.8.0
[106] RColorBrewer_1.1-3
[107] plotrix_3.8-2
[108] fitdistrplus_1.1-8
[109] Rsamtools_2.14.0
[110] cli_3.6.2
[111] listenv_0.9.0
[112] pbapply_1.7-0
[113] htmlTable_2.4.1
[114] Formula_1.2-5
[115] MASS_7.3-58.2
[116] WGCNA_1.72-1
[117] tidyselect_1.2.0
[118] stringi_1.8.3
[119] GOSemSim_2.24.0
[120] yaml_2.3.7
[121] ggrepel_0.9.3
[122] fastmatch_1.1-3
[123] tools_4.2.2
[124] future.apply_1.10.0
[125] rstudioapi_0.14
[126] uuid_1.1-0
[127] TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2
[128] foreach_1.5.2
[129] foreign_0.8-84
[130] farver_2.1.1
[131] plyranges_1.18.0
[132] Rtsne_0.16
[133] ggraph_2.1.0
[134] digest_0.6.33
[135] shiny_1.7.4
[136] later_1.3.0
[137] RcppAnnoy_0.0.20
[138] httr_1.4.7
[139] AnnotationDbi_1.60.2
[140] colorspace_2.1-0
[141] XML_3.99-0.14
[142] fs_1.6.3
[143] tensor_1.5
[144] reticulate_1.28
[145] splines_4.2.2
[146] uwot_0.1.14
[147] yulab.utils_0.1.2
[148] RcppRoll_0.3.0
[149] tidytree_0.4.2
[150] spatstat.utils_3.0-2
[151] graphlayouts_0.8.4
[152] sp_1.6-0
[153] ggplotify_0.1.2
[154] plotly_4.10.1
[155] xtable_1.8-4
[156] jsonlite_1.8.8
[157] tidygraph_1.2.3
[158] dynamicTreeCut_1.63-1
[159] ggfun_0.0.9
[160] R6_2.5.1
[161] Hmisc_5.0-1
[162] pillar_1.9.0
[163] htmltools_0.5.5
[164] mime_0.12
[165] nnls_1.4
[166] glue_1.6.2
[167] fastmap_1.1.1
[168] BiocParallel_1.32.6
[169] codetools_0.2-19
[170] fgsea_1.24.0
[171] Signac_1.9.0
[172] utf8_1.2.4
[173] lattice_0.20-45
[174] spatstat.sparse_3.0-1
[175] tibble_3.2.1
[176] curl_5.2.0
[177] leiden_0.4.3
[178] zip_2.2.2
[179] GO.db_3.16.0
[180] survival_3.5-3
[181] rmarkdown_2.21
[182] repr_1.1.6
[183] munsell_0.5.0
[184] fastcluster_1.2.3
[185] GenomeInfoDbData_1.2.9
[186] iterators_1.0.14
[187] impute_1.72.0
Initial ideas (untested since we have the connection here...) could be:
ref_genome = "hg19"
into ref_genome = ref_genome
seqinfo
seqinfo
in EpiTrace as
EpiTraceAge_Convergence(peakSet = init_gr, matrix = init_mm, ref_genome = seqinfo , clock_gr = mouse_clock_by_MM285, iterative_time = 5, min.cutoff = 0, non_standard_clock = T, qualnum = 10, ncore_lim = 48, mean_error_limit = 0.1)
See if this can handle the problem? It seems to be a known issue in GenomeInfoDb without internet connection such as in : https://github.com/stuart-lab/signac/issues/249.
Hi,
I modified the script EpiTrace.R, generated a mm10 seqInfo using the below code, and replaced the parameters with ref_genome = seqinfo
genomeBuild = "mm10"
genomeStyle = "UCSC"
library(GenomeInfoDb)
bsg <- paste0("BSgenome.Mmusculus.UCSC.", genomeBuild)
if (!require(bsg, character.only=TRUE, quietly=TRUE, warn.conflicts=FALSE)) {
seqinfo <- Seqinfo(genome=genomeBuild)
} else {
seqinfo <- seqinfo(get(bsg))
}
seqlevelsStyle(seqinfo) <- genomeStyle
seqinfo <- keepSeqlevels(seqinfo, value = paste0("chr",c(1:19,"X")))
saveRDS(seqinfo, file = "/data/work/2024-05-15_EpiTrace/seqinfo_mm10_ucsc.rds")
but it shows another error
Error in match(x, table, nomatch = 0L): 'match' requires vector arguments
Traceback:
1. EpiTraceAge_Convergence(peakSet = init_gr, matrix = init_mm,
. ref_genome = seqinfo, clock_gr = mouse_clock_by_MM285, iterative_time = 5,
. min.cutoff = 0, non_standard_clock = T, qualnum = 10, ncore_lim = 48,
. mean_error_limit = 0.1)
2. ref_genome %in% "hg38"
3. ref_genome %in% "hg38"
Could you please assist in testing it without an internet connection?
Many thanks
Hi,
I encountered a problem when running EpiTraceAge_Convergence in the step 4 of the tutorial. It seems to be caused by some functions attempting to download chromosome information. Given that our server cannot connect to the network, is it possible to pre-fetch the seqinfo object? Could you advise on which code I should modify to load the object?
Thank you in advance.
Below is the traceback: