Entering edit mode
I have 10 data sets that I've mapped to the Gencode transcriptome using salmon. I made a coldata table that shows where they are:
meta_sep
# A tibble: 10 x 3
names background files
<chr> <chr> <chr>
1 SRR4000492 FCRL5- ~/hpchome/b_cell/quants/SRR4000492/quant.sf
2 SRR4000493 FCRL5- ~/hpchome/b_cell/quants/SRR4000493/quant.sf
3 SRR4000494 FCRL5- ~/hpchome/b_cell/quants/SRR4000494/quant.sf
4 SRR4000495 FCRL5- ~/hpchome/b_cell/quants/SRR4000495/quant.sf
5 SRR4000496 FCRL5- ~/hpchome/b_cell/quants/SRR4000496/quant.sf
6 SRR4000497 FCRL5+ ~/hpchome/b_cell/quants/SRR4000497/quant.sf
7 SRR4000498 FCRL5+ ~/hpchome/b_cell/quants/SRR4000498/quant.sf
8 SRR4000499 FCRL5+ ~/hpchome/b_cell/quants/SRR4000499/quant.sf
9 SRR4000500 FCRL5+ ~/hpchome/b_cell/quants/SRR4000500/quant.sf
10 SRR4000501 FCRL5+ ~/hpchome/b_cell/quants/SRR4000501/quant.sf
I tried to import the count tables using tximeta and got the following error:
se <- tximeta(meta_sep)
importing quantifications
reading in files with read_tsv
1 2 3 4 5 6 7 8 9 10
Error in UseMethod("filter_") :
no applicable method for 'filter_' applied to an object of class "c('tbl_SQLiteConnection', 'tbl_dbi', 'tbl_sql', 'tbl_lazy', 'tbl')"
I poked around on-line and found that this could be because I am using an old version of R (3.6.1) and Bioconductor (3.10) where there may be a conflict between dplyr and biomaRt. Both R and Bioconductor are installed on a cluster, are used for class, and are nto easily upgraded. Is there another solution to this issue?
Thanks -
Miles
sessionInfo()
R version 3.6.1 (2019-07-05)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 18.04.5 LTS
Matrix products: default
BLAS: /opt/R/lib/R/lib/libRblas.so
LAPACK: /opt/R/lib/R/lib/libRlapack.so
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C LC_TIME=en_US.UTF-8
[4] LC_COLLATE=en_US.UTF-8 LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C LC_ADDRESS=C
[10] LC_TELEPHONE=C LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] parallel stats4 stats graphics grDevices utils datasets methods base
other attached packages:
[1] TxDb.Mmusculus.UCSC.mm10.knownGene_3.10.0 rjson_0.2.20
[3] ggpubr_0.4.0 qvalue_2.18.0
[5] viridis_0.6.0 viridisLite_0.4.0
[7] ReportingTools_2.26.0 knitr_1.32
[9] genefilter_1.68.0 apeglm_1.8.0
[11] PoiClaClu_1.0.2.1 RColorBrewer_1.1-2
[13] pheatmap_1.0.12 hexbin_1.28.2
[15] vsn_3.54.0 ensembldb_2.10.2
[17] AnnotationFilter_1.10.0 GenomicFeatures_1.38.2
[19] AnnotationDbi_1.48.0 rhdf5_2.30.1
[21] DESeq2_1.26.0 SummarizedExperiment_1.16.1
[23] DelayedArray_0.12.3 BiocParallel_1.20.1
[25] matrixStats_0.58.0 Biobase_2.46.0
[27] GenomicRanges_1.38.0 GenomeInfoDb_1.22.1
[29] IRanges_2.20.2 S4Vectors_0.24.4
[31] BiocGenerics_0.32.0 tximeta_1.4.5
[33] tximportData_1.14.0 tximport_1.14.2
[35] forcats_0.5.1 stringr_1.4.0
[37] dplyr_1.0.4 purrr_0.3.4
[39] readr_1.4.0 tidyr_1.1.3
[41] tibble_3.1.0 ggplot2_3.3.3
[43] tidyverse_1.3.1
loaded via a namespace (and not attached):
[1] utf8_1.2.1 R.utils_2.10.1 tidyselect_1.1.0 RSQLite_2.2.6
[5] htmlwidgets_1.5.3 grid_3.6.1 munsell_0.5.0 preprocessCore_1.48.0
[9] withr_2.4.1 colorspace_2.0-0 Category_2.52.1 OrganismDbi_1.28.0
[13] rstudioapi_0.13 ggsignif_0.6.1 labeling_0.4.2 bbmle_1.0.23.1
[17] GenomeInfoDbData_1.2.2 hwriter_1.3.2 farver_2.1.0 bit64_4.0.5
[21] coda_0.19-4 vctrs_0.3.7 generics_0.1.0 xfun_0.22
[25] biovizBase_1.34.1 BiocFileCache_1.10.2 R6_2.5.0 locfit_1.5-9.4
[29] bitops_1.0-6 cachem_1.0.4 reshape_0.8.8 assertthat_0.2.1
[33] scales_1.1.1 nnet_7.3-15 gtable_0.3.0 affy_1.64.0
[37] ggbio_1.34.0 rlang_0.4.10 splines_3.6.1 rstatix_0.7.0
[41] rtracklayer_1.46.0 lazyeval_0.2.2 dichromat_2.0-0 broom_0.7.6
[45] checkmate_2.0.0 abind_1.4-5 BiocManager_1.30.12 yaml_2.2.1
[49] reshape2_1.4.4 modelr_0.1.8 backports_1.2.1 Hmisc_4.5-0
[53] RBGL_1.62.1 tools_3.6.1 affyio_1.56.0 ellipsis_0.3.1
[57] Rcpp_1.0.6 plyr_1.8.6 base64enc_0.1-3 progress_1.2.2
[61] zlibbioc_1.32.0 RCurl_1.98-1.3 prettyunits_1.1.1 rpart_4.1-15
[65] openssl_1.4.3 haven_2.4.0 cluster_2.1.2 fs_1.5.0
[69] magrittr_2.0.1 data.table_1.14.0 openxlsx_4.2.3 reprex_2.0.0
[73] mvtnorm_1.1-1 ProtGenerics_1.18.0 evaluate_0.14 hms_1.0.0
[77] xtable_1.8-4 XML_3.99-0.3 rio_0.5.26 emdbook_1.3.12
[81] jpeg_0.1-8.1 readxl_1.3.1 gridExtra_2.3 compiler_3.6.1
[85] biomaRt_2.42.1 bdsmatrix_1.3-4 crayon_1.4.1 R.oo_1.24.0
[89] htmltools_0.5.1.1 GOstats_2.52.0 mgcv_1.8-34 Formula_1.2-4
[93] geneplotter_1.64.0 lubridate_1.7.10 DBI_1.1.1 dbplyr_2.1.1
[97] MASS_7.3-53.1 rappdirs_0.3.3 car_3.0-10 Matrix_1.3-2
[101] cli_2.4.0 R.methodsS3_1.8.1 pkgconfig_2.0.3 GenomicAlignments_1.22.1
[105] numDeriv_2016.8-1.1 foreign_0.8-71 xml2_1.3.2 annotate_1.64.0
[109] XVector_0.26.0 AnnotationForge_1.28.0 rvest_1.0.0 VariantAnnotation_1.32.0
[113] digest_0.6.27 graph_1.64.0 Biostrings_2.54.0 rmarkdown_2.7
[117] cellranger_1.1.0 htmlTable_2.1.0 edgeR_3.28.1 GSEABase_1.48.0
[121] curl_4.3 Rsamtools_2.2.3 nlme_3.1-152 lifecycle_1.0.0
[125] jsonlite_1.7.2 PFAM.db_3.10.0 Rhdf5lib_1.8.0 carData_3.0-4
[129] askpass_1.1 limma_3.42.2 BSgenome_1.54.0 fansi_0.4.2
[133] pillar_1.6.0 lattice_0.20-41 GGally_2.1.1 fastmap_1.1.0
[137] httr_1.4.2 survival_3.2-10 GO.db_3.10.0 glue_1.4.2
[141] zip_2.1.1 png_0.1-7 bit_4.0.4 Rgraphviz_2.30.0
[145] stringi_1.5.3 blob_1.2.1 latticeExtra_0.6-29 memoise_2.0.0 sessionInfo( )
