AMCalejandro
commented
2 years ago RajLabMSSM/echolocatoR#96
Closed Captain-Pam closed 2 years ago
AMCalejandro
commented
2 years ago RajLabMSSM/echolocatoR#96
bschilder
commented
2 years ago Fixed thanks to PR by @AMCalejandro. See here for details: https://github.com/RajLabMSSM/echolocatoR/issues/96
1. Bug description
I run the example, it success, but when it plot, it throw "Error in dplyr::slice_max(., order_by = c(symbol, width), n = max_transcripts" Problem while computing indices. ℹ The error occurred in group 1: symbol = "BST1". Caused by error: !
order_bymust have size 4, not size 8. (A clear and concise description of what the bug is.)I find the function "PLOT.locus.R", and 1128 lines in this function,
dplyr::slice_max(order_by = c(symbol,width), n = max_transcripts, with_ties = F) as far as I know order_by in slice_max() must be a single variable
Another question:
[1] "+ NOTT_2019:: Getting interactome data." Error in eval(e, x, parent.frame()) : object 'Coordinates' not found [1] "+ Adding vertical lines to highlight SNP groups..."
Console output
Error in dplyr::slice_max(., order_by = c(symbol, width), n = max_transcripts, : Problem while computing indices. ℹ The error occurred in group 1: symbol = "BST1". Caused by error: !
order_bymust have size 4, not size 8. In addition: Warning message: In susie_func(bhat = subset_DT$Effect, shat = subset_DT$StdErr, : IBSS algorithm did not converge in 100 iterations! Please check consistency between summary statistics and LD matrix.Expected behaviour
(A clear and concise description of what you expected to happen.)
2. Reproducible example
Code
step1
top_SNPs <- import_topSNPs(
topSS = "~/Desktop/Fine_Mapping/Data/GWAS/Nalls23andMe_2019/Nalls2019_TableS2.xlsx",
topSS = Nalls_top_SNPs, munge = FALSE, ref_genome = "GRCH37", chrom_col = "CHR", position_col = "BP", pval_col="P, all studies", effect_col="Beta, all studies", gene_col="Nearest Gene", locus_col = "Nearest Gene", grouping_vars = c("Locus"), remove_variants = "rs34637584")
fullSS_path <- example_fullSS(munged = TRUE)
step2
Nalls23andMe_2019.results <- finemap_loci(
GENERAL ARGUMENTS
top_SNPs = top_SNPs,
It's best to give absolute paths
results_dir = file.path(fullSS_path,"results2"),
loci = c("BST1"),
loci = c("BST1","MEX3C"),
top_SNPs$Locus,
dataset_name = "Nalls23andMe_2019", dataset_type = "GWAS",
force_new_subset = TRUE, force_new_LD = FALSE, force_new_finemap = TRUE, remove_tmps = FALSE,
Munge full sumstats first
munged = FALSE, freq_col = "MAF",
MAF_col = "MAF",
sample_size = 1460059,
SUMMARY STATS ARGUMENTS
chrom_col = "CHR", position_col = "BP", snp_col = "SNP", effect_col = "BETA", stderr_col = "SE",
proportion_cases ="calculate",
fullSS_path = fullSS_path, fullSS_genome_build = "hg19", query_by ="tabix",
MAF_col = "calculate",
proportion_cases = 49053/1411006,
FILTERING ARGUMENTS
It's often desirable to use a larger window size
(e.g. 2Mb which is bp_distance=500000*2),
but we use a small window here to speed up the process.
bp_distance = 10000,#500000*2, min_MAF = 0.001,
trim_gene_limits = FALSE,
FINE-MAPPING ARGUMENTS
General
finemap_methods = c("ABF","FINEMAP","SUSIE","POLYFUN_SUSIE","PAINTOR","COJO"),
finemap_methods = c("ABF","FINEMAP","SUSIE","POLYFUN_SUSIE"), n_causal = 5, PP_threshold = .95,
LD ARGUMENTS
LD_reference = "1KGphase3",#"UKB", superpopulation = "EUR", download_method = "axel",
PLOT ARGUMENTS
general
plot.types = c("fancy"),
Generate multiple plots of different window sizes;
all SNPs, 4x zoomed-in, and a 50000bp window
plot.zoom = c("all","4x","10x"),
XGR
plot.XGR_libnames=c("ENCODE_TFBS_ClusteredV3_CellTypes"),
Roadmap
plot.Roadmap = FALSE, plot.Roadmap_query = NULL,
Nott et al. (2019)
plot.Nott_epigenome = TRUE, plot.Nott_show_placseq = TRUE,
verbose = TRUE )
(Please add the steps to reproduce the bug here. See here for an intro to making a reproducible example (i.e. reprex) and why they're important! This will help us to help you much faster.)
Data
(If possible, upload a small sample of your data so that we can reproduce the bug on our end. If that's not possible, please at least include a screenshot of your data and other relevant details.)
3. Session info
R version 4.0.5 (2021-03-31) Platform: x86_64-conda-linux-gnu (64-bit) Running under: CentOS Linux 7 (Core)
Matrix products: default BLAS/LAPACK: /xtdisk/liufan_group/pansy/software/anaconda3/envs/echoR/lib/libopenblasp-r0.3.20.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 parallel stats graphics grDevices utils datasets [8] methods base
other attached packages: [1] EnsDb.Hsapiens.v75_2.99.0 ensembldb_2.14.1
[3] AnnotationFilter_1.14.0 GenomicFeatures_1.42.3
[5] AnnotationDbi_1.52.0 Biobase_2.50.0
[7] GenomicRanges_1.42.0 GenomeInfoDb_1.26.7
[9] IRanges_2.24.1 S4Vectors_0.28.1
[11] BiocGenerics_0.36.1 echolocatoR_0.2.3
[13] reprex_2.0.1
loaded via a namespace (and not attached): [1] backports_1.4.1 Hmisc_4.7-0
[3] BiocFileCache_1.14.0 coloc_5.1.0
[5] plyr_1.8.7 lazyeval_0.2.2
[7] splines_4.0.5 BiocParallel_1.24.1
[9] crosstalk_1.2.0 ggplot2_3.3.6
[11] digest_0.6.29 htmltools_0.5.2
[13] viridis_0.6.2 fansi_1.0.3
[15] magrittr_2.0.3 checkmate_2.1.0
[17] memoise_2.0.1 BSgenome_1.58.0
[19] cluster_2.1.3 Biostrings_2.58.0
[21] matrixStats_0.62.0 R.utils_2.11.0
[23] ggbio_1.38.0 askpass_1.1
[25] prettyunits_1.1.1 jpeg_0.1-9
[27] colorspace_2.0-3 blob_1.2.3
[29] rappdirs_0.3.3 ggrepel_0.9.1
[31] xfun_0.31 dplyr_1.0.9
[33] crayon_1.5.1 RCurl_1.98-1.6
[35] jsonlite_1.8.0 graph_1.68.0
[37] survival_3.3-1 VariantAnnotation_1.36.0
[39] glue_1.6.2 gtable_0.3.0
[41] zlibbioc_1.36.0 XVector_0.30.0
[43] DelayedArray_0.16.3 clipr_0.8.0
[45] scales_1.2.0 DBI_1.1.2
[47] GGally_2.1.2 Rcpp_1.0.8.3
[49] viridisLite_0.4.0 progress_1.2.2
[51] htmlTable_2.4.0 reticulate_1.25
[53] foreign_0.8-82 bit_4.0.4
[55] OrganismDbi_1.32.0 Formula_1.2-4
[57] DT_0.23 htmlwidgets_1.5.4
[59] httr_1.4.3 RColorBrewer_1.1-3
[61] ellipsis_0.3.2 pkgconfig_2.0.3
[63] reshape_0.8.9 XML_3.99-0.9
[65] R.methodsS3_1.8.1 farver_2.1.0
[67] seqminer_8.4 nnet_7.3-17
[69] sass_0.4.1 dbplyr_2.1.1
[71] utf8_1.2.2 tidyselect_1.1.2
[73] labeling_0.4.2 rlang_1.0.2
[75] reshape2_1.4.4 munsell_0.5.0
[77] tools_4.0.5 cachem_1.0.6
[79] cli_3.3.0 generics_0.1.2
[81] RSQLite_2.2.14 stringr_1.4.0
[83] fastmap_1.1.0 knitr_1.39
[85] bit64_4.0.5 fs_1.5.2
[87] purrr_0.3.4 RBGL_1.66.0
[89] R.oo_1.24.0 xml2_1.3.3
[91] biomaRt_2.46.3 compiler_4.0.5
[93] rstudioapi_0.13 curl_4.3.2
[95] susieR_0.11.92 png_0.1-7
[97] tibble_3.1.7 bslib_0.3.1
[99] stringi_1.7.6 lattice_0.20-45
[101] ProtGenerics_1.22.0 Matrix_1.4-1
[103] vctrs_0.4.1 pillar_1.7.0
[105] lifecycle_1.0.1 BiocManager_1.30.18
[107] jquerylib_0.1.4 snpStats_1.40.0
[109] data.table_1.14.2 bitops_1.0-7
[111] irlba_2.3.5 patchwork_1.1.1
[113] rtracklayer_1.50.0 R6_2.5.1
[115] latticeExtra_0.6-29 gridExtra_2.3
[117] dichromat_2.0-0.1 assertthat_0.2.1
[119] SummarizedExperiment_1.20.0 openssl_2.0.2
[121] withr_2.5.0 GenomicAlignments_1.26.0
[123] Rsamtools_2.6.0 GenomeInfoDbData_1.2.4
[125] hms_1.1.1 grid_4.0.5
[127] rpart_4.1.16 tidyr_1.2.0
[129] MatrixGenerics_1.2.1 biovizBase_1.38.0
[131] mixsqp_0.3-43 base64enc_0.1-3 (Add output of the R function
utils::sessionInfo()below. This helps us assess version/OS conflicts which could be causing bugs.)