I am currently trying to use sva with DESeq2 on a bulk rna-seq data.
Following this 8.1 chapter from this vignette: https://bioconductor.org/packages/release/workflows/vignettes/rnaseqGene/inst/doc/rnaseqGene.html#using-sva-with-deseq2
Code ran just fine in that chapter (8.1 Using sva with DESeq2) until :
design(ddssva) <- ~ SV1 + SV2 + dex
Error in (function (classes, fdef, mtable) : unable to find an inherited method for function ‘design<-’ for signature ‘"DESeqTransform", "formula"’
When i looked up the help page for design() <- value
it only tells that i need an object + optional further arguments which there is none afaik. Function belongs to BiocGenerics, it's loaded. Input should be good since i gave it an input like in the vignette. I am a bit confused. Thanks for any help.
Best regards,
sessionInfo( ) R version 4.2.2 (2022-10-31) Platform: x86_64-redhat-linux-gnu (64-bit) Running under: Fedora Linux 37 (Workstation Edition)
Matrix products: default BLAS/LAPACK: /usr/lib64/libflexiblas.so.3.3
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 stats4 stats graphics grDevices utils datasets methods base
other attached packages:
1 sva_3.46.0 GEOquery_2.66.0 ashr_2.2-54
[4] pheatmap_1.0.12 RColorBrewer_1.1-3 forcats_1.0.0
[7] stringr_1.5.0 dplyr_1.1.0 purrr_1.0.1
[10] readr_2.1.3 tidyr_1.3.0 tibble_3.1.8
[13] tidyverse_1.3.2 ggplot2_3.4.0 BiocParallel_1.32.5
[16] genefilter_1.80.3 mgcv_1.8-41 nlme_3.1-162
[19] DESeq2_1.38.3 SummarizedExperiment_1.28.0 Biobase_2.58.0
[22] MatrixGenerics_1.10.0 matrixStats_0.63.0 GenomicRanges_1.50.2
[25] GenomeInfoDb_1.34.9 IRanges_2.32.0 S4Vectors_0.36.1
[28] BiocGenerics_0.44.0
loaded via a namespace (and not attached):
1 googledrive_2.0.0 colorspace_2.1-0 ellipsis_0.3.2 XVector_0.38.0
[5] fs_1.6.1 rstudioapi_0.14 farver_2.1.1 affyio_1.68.0
[9] bit64_4.0.5 AnnotationDbi_1.60.0 fansi_1.0.4 lubridate_1.9.1
[13] xml2_1.3.3 codetools_0.2-18 splines_4.2.2 oligoClasses_1.60.0
[17] cachem_1.0.6 knitr_1.42 geneplotter_1.76.0 jsonlite_1.8.4
[21] broom_1.0.3 annotate_1.76.0 vsn_3.66.0 dbplyr_2.3.0
[25] png_0.1-8 BiocManager_1.30.19 compiler_4.2.2 httr_1.4.4
[29] backports_1.4.1 assertthat_0.2.1 Matrix_1.5-3 fastmap_1.1.0
[33] gargle_1.3.0 limma_3.54.1 cli_3.6.0 htmltools_0.5.4
[37] tools_4.2.2 gtable_0.3.1 glue_1.6.2 GenomeInfoDbData_1.2.9
[41] affy_1.76.0 affxparser_1.70.0 Rcpp_1.0.10 cellranger_1.1.0
[45] vctrs_0.5.2 Biostrings_2.66.0 preprocessCore_1.60.2 iterators_1.0.14
[49] xfun_0.37 rvest_1.0.3 irlba_2.3.5.1 timechange_0.2.0
[53] lifecycle_1.0.3 XML_3.99-0.13 googlesheets4_1.0.1 edgeR_3.40.2
[57] zlibbioc_1.44.0 scales_1.2.1 hms_1.1.2 parallel_4.2.2
[61] oligo_1.62.2 yaml_2.3.7 memoise_2.0.1 stringi_1.7.12
[65] RSQLite_2.2.20 SQUAREM_2021.1 foreach_1.5.2 truncnorm_1.0-8
[69] rlang_1.0.6 pkgconfig_2.0.3 bitops_1.0-7 evaluate_0.20
[73] lattice_0.20-45 invgamma_1.1 labeling_0.4.2 bit_4.0.5
[77] tidyselect_1.2.0 magrittr_2.0.3 R6_2.5.1 generics_0.1.3
[81] DelayedArray_0.24.0 DBI_1.1.3 pillar_1.8.1 haven_2.5.1
[85] withr_2.5.0 survival_3.5-0 KEGGREST_1.38.0 RCurl_1.98-1.10
[89] mixsqp_0.3-48 modelr_0.1.10 crayon_1.5.2 utf8_1.2.3
[93] rmarkdown_2.20 tzdb_0.3.0 locfit_1.5-9.7 grid_4.2.2
[97] readxl_1.4.2 data.table_1.14.6 blob_1.2.3 digest_0.6.31
[101] reprex_2.0.2 xtable_1.8-4 ff_4.0.9 munsell_0.5.0
Thanks for the prompt response.