Closed huangwb8 closed 3 years ago
PoisonAlien
commented
3 years ago Hi,
The function uses synonymous variants (in all_maf@maf.silent) as well.
It is hard to guess the issue, but maybe the problematic row is coming from one of the chrM or unknown contigs?
huangwb8
commented
3 years ago Hi, The function uses synonymous variants (in
all_maf@maf.silent) as well. It is hard to guess the issue, but maybe the problematic row is coming from one of thechrMorunknowncontigs?
Hi~Sorry for late reply. I found it suddenly works and I don't know why. I also test chromosome of the MAF object
table(all_maf@maf.silent$Chromosome)
chr1 chr10 chr11 chr12 chr13 chr14 chr15 chr16 chr17 chr18
26988 12838 15202 15771 5726 9180 9070 10659 14067 4886
chr19 chr2 chr20 chr21 chr22 chr3 chr4 chr5 chr6 chr7
16917 20024 7117 3540 5907 15202 11732 11984 12421 13618
chr8 chr9 chrX chrY
8867 11209 5018 31 It seems that no abnormal issues like chrM.
I generate the MAF object via annovar results. Here's the example:
# merge
all.maf <- annovarToMaf(
annovar = c("raw/ST1.snp.vcf.gz.hg38_multianno.txt",
"raw/ST1.indel.vcf.gz.hg38_multianno.txt",
"raw/ST1_BA.snp.vcf.gz.hg38_multianno.txt",
"raw/ST1_BA.indel.vcf.gz.hg38_multianno.txt"),
refBuild = 'hg38',
table = 'refGene'
)
# clinic data
clinicalData <- data.frame(
Tumor_Sample_Barcode = c('ST1', 'ST1_BA'),
Group = c('tissue', 'organoid'),
stringsAsFactors = F
)
all_maf <- read.maf(maf = all.maf,
clinicalData = clinicalData)I hope it may help someone with similar problem.
Describe the issue Here is the code and error information:
I'm sure the reference of my data is hg38.
Actually, my data only have 13136 rows.
I don't know where is 'row 115044'.
Session info R version 4.0.3 (2020-10-10) Platform: x86_64-w64-mingw32/x64 (64-bit) Running under: Windows 10 x64 (build 18363)
Matrix products: default
locale: [1] LC_COLLATE=Chinese (Simplified)_China.936 [2] LC_CTYPE=Chinese (Simplified)_China.936
[3] LC_MONETARY=Chinese (Simplified)_China.936 [4] LC_NUMERIC=C
[5] LC_TIME=Chinese (Simplified)_China.936
attached base packages: [1] parallel stats4 stats graphics grDevices [6] utils datasets methods base
other attached packages: [1] BSgenome.Hsapiens.UCSC.hg19_1.4.3 [2] BSgenome.Hsapiens.UCSC.hg38_1.4.3 [3] BSgenome_1.56.0
[4] rtracklayer_1.48.0
[5] Biostrings_2.56.0
[6] XVector_0.28.0
[7] maftools_2.4.12
[8] pacman_0.5.1
[9] easyGgplot2_1.0.0.9000
[10] extrafont_0.17
[11] Rmisc_1.5
[12] lattice_0.20-41
[13] data.table_1.13.2
[14] clusterProfiler_3.16.1
[15] cowplot_1.1.0
[16] tableone_0.12.0
[17] SummarizedExperiment_1.18.2
[18] DelayedArray_0.14.1
[19] matrixStats_0.57.0
[20] Biobase_2.48.0
[21] GenomicRanges_1.40.0
[22] GenomeInfoDb_1.24.2
[23] IRanges_2.22.2
[24] S4Vectors_0.26.1
[25] BiocGenerics_0.34.0
[26] stringr_1.4.0
[27] circlize_0.4.10
[28] ggthemes_4.2.0
[29] ggpubr_0.4.0
[30] ggplot2_3.3.2
[31] dplyr_1.0.2
[32] plyr_1.8.6
[33] tidyr_1.1.2
[34] reshape2_1.4.4
[35] readr_1.4.0
[36] writexl_1.3.1
[37] readxl_1.3.1
[38] lucky_1.1.6
loaded via a namespace (and not attached): [1] ModelMetrics_1.2.2.2 exactRankTests_0.8-31
[3] bit64_4.0.5 knitr_1.30
[5] rpart_4.1-15 RCurl_1.98-1.2
[7] doParallel_1.0.16 generics_0.0.2
[9] preprocessCore_1.50.0 usethis_1.6.3
[11] RSQLite_2.2.1 europepmc_0.4
[13] bit_4.0.4 enrichplot_1.8.1
[15] xml2_1.3.2 lubridate_1.7.9
[17] viridis_0.5.1 gower_0.2.2
[19] xfun_0.18 hms_0.5.3
[21] progress_1.2.2 km.ci_0.5-2
[23] igraph_1.2.6 DBI_1.1.0
[25] geneplotter_1.66.0 htmlwidgets_1.5.2
[27] reshape_0.8.8 purrr_0.3.4
[29] ellipsis_0.3.1 backports_1.1.10
[31] survey_4.0 rmda_1.6
[33] annotate_1.66.0 vctrs_0.3.4
[35] abind_1.4-5 caret_6.0-86
[37] withr_2.3.0 ggforce_0.3.2
[39] minerva_1.5.8 triebeard_0.3.0
[41] checkmate_2.0.0 GenomicAlignments_1.24.0 [43] prettyunits_1.1.1 cluster_2.1.0
[45] DOSE_3.14.0 crayon_1.3.4
[47] genefilter_1.70.0 glmnet_4.0-2
[49] edgeR_3.30.3 recipes_0.1.14
[51] pkgconfig_2.0.3 tweenr_1.0.1
[53] nlme_3.1-149 nnet_7.3-14
[55] rlang_0.4.8 lifecycle_0.2.0
[57] downloader_0.4 extrafontdb_1.0
[59] cellranger_1.1.0 polyclip_1.10-0
[61] Matrix_1.2-18 urltools_1.7.3
[63] KMsurv_0.1-5 carData_3.0-4
[65] boot_1.3-25 zoo_1.8-8
[67] base64enc_0.1-3 ggridges_0.5.2
[69] GlobalOptions_0.1.2 png_0.1-7
[71] viridisLite_0.3.0 bitops_1.0-6
[73] pROC_1.16.2 pander_0.6.3
[75] blob_1.2.1 shape_1.4.5
[77] maxstat_0.7-25 qvalue_2.20.0
[79] jpeg_0.1-8.1 rstatix_0.6.0
[81] gridGraphics_0.5-0 ggsignif_0.6.0
[83] scales_1.1.1 memoise_1.1.0
[85] magrittr_1.5 zlibbioc_1.34.0
[87] compiler_4.0.3 scatterpie_0.1.5
[89] RColorBrewer_1.1-2 DESeq2_1.28.1
[91] Rsamtools_2.4.0 htmlTable_2.1.0
[93] Formula_1.2-4 MASS_7.3-53
[95] mgcv_1.8-33 WGCNA_1.69
[97] tidyselect_1.1.0 stringi_1.5.3
[99] forcats_0.5.0 mitools_2.4
[101] GOSemSim_2.14.2 locfit_1.5-9.4
[103] latticeExtra_0.6-29 ggrepel_0.8.2
[105] survMisc_0.5.5 grid_4.0.3
[107] fastmatch_1.1-0 tools_4.0.3
[109] rio_0.5.16 rstudioapi_0.11
[111] foreach_1.5.1 foreign_0.8-80
[113] gridExtra_2.3 prodlim_2019.11.13
[115] farver_2.0.3 ggraph_2.0.3
[117] digest_0.6.26 rvcheck_0.1.8
[119] BiocManager_1.30.10 lava_1.6.8
[121] Rcpp_1.0.5 car_3.0-10
[123] broom_0.7.2 org.Hs.eg.db_3.11.4
[125] httr_1.4.2 survminer_0.4.8
[127] AnnotationDbi_1.50.3 colorspace_1.4-1
[129] rvest_0.3.6 XML_3.99-0.5
[131] fs_1.5.0 splines_4.0.3
[133] graphlayouts_0.7.0 ggplotify_0.0.5
[135] xtable_1.8-4 jsonlite_1.7.1
[137] dynamicTreeCut_1.63-1 tidygraph_1.2.0
[139] timeDate_3043.102 UpSetR_1.4.0
[141] ipred_0.9-9 R6_2.4.1
[143] Hmisc_4.4-1 pillar_1.4.6
[145] htmltools_0.5.0 glue_1.4.2
[147] DT_0.16 BiocParallel_1.22.0
[149] class_7.3-17 codetools_0.2-16
[151] fgsea_1.14.0 mvtnorm_1.1-1
[153] tibble_3.0.4 sva_3.36.0
[155] curl_4.3 zip_2.1.1
[157] GO.db_3.11.4 openxlsx_4.2.2
[159] Rttf2pt1_1.3.8 survival_3.2-7
[161] limma_3.44.3 munsell_0.5.0
[163] DO.db_2.9 fastcluster_1.1.25
[165] GenomeInfoDbData_1.2.3 iterators_1.0.13
[167] impute_1.62.0 haven_2.3.1
[169] gtable_0.3.0