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Hello everyone!
I have bulk rna-seq data and would like to do some analysis to see what cell types I have, with the MCP-counter package. However, they require that the matrix be normalized with frma in r and here I am running into problems.
I have a data matrix and in order to normalize it in frma it has to be "AffyBatch," "ExonFeatureSet," "GeneFeatureSet" objects. I've been trying for days but I don't understand how to do it.
I installed affy but when I try to use the function:
affy_obj <- AffyBatch(data = as.matrix(matrixFiltered))
Error in AffyBatch(data = as.matrix(matrixFiltered)) :
can't find the function "AffyBatch"
> sessionInfo()
R version 4.3.2 (2023-10-31 ucrt)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 11 x64 (build 22631)
Matrix products: default
locale:
[1] LC_COLLATE=Italian_Italy.utf8 LC_CTYPE=Italian_Italy.utf8 LC_MONETARY=Italian_Italy.utf8
[4] LC_NUMERIC=C LC_TIME=Italian_Italy.utf8
time zone: Europe/Rome
tzcode source: internal
attached base packages:
[1] parallel grid stats4 stats graphics grDevices utils datasets methods base
other attached packages:
[1] frma_1.54.0 MCPcounter_1.2.0
[3] curl_5.2.0 immunedeconv_2.1.0
[5] EPIC_1.1.7 GSVA_1.50.0
[7] preprocessCore_1.64.0 e1071_1.7-14
[9] Homo.sapiens_1.3.1 TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2
[11] GO.db_3.18.0 OrganismDbi_1.44.0
[13] GenomicFeatures_1.54.3 goseq_1.54.0
[15] geneLenDataBase_1.38.0 BiasedUrn_2.0.11
[17] GOfuncR_1.22.2 vioplot_0.4.0
[19] zoo_1.8-12 sm_2.2-5.7.1
[21] RColorBrewer_1.1-3 org.Hs.eg.db_3.18.0
[23] org.Mm.eg.db_3.18.0 Glimma_2.12.0
[25] GSEABase_1.64.0 graph_1.80.0
[27] annotate_1.80.0 XML_3.99-0.16.1
[29] fgsea_1.28.0 EnhancedVolcano_1.20.0
[31] ggrepel_0.9.5 DGEobj.utils_1.0.6
[33] clusterProfiler_4.10.0 DESeq2_1.42.0
[35] msigdbr_7.5.1 affy_1.80.0
[37] readxl_1.4.3 xCell_1.1.0
[39] recount_1.28.0 enrichR_3.2
[41] ComplexHeatmap_2.18.0 Rtsne_0.17
[43] AnnotationDbi_1.64.1 SummarizedExperiment_1.32.0
[45] Biobase_2.62.0 GenomicRanges_1.54.1
[47] GenomeInfoDb_1.38.6 IRanges_2.36.0
[49] S4Vectors_0.40.2 BiocGenerics_0.48.1
[51] MatrixGenerics_1.14.0 matrixStats_1.2.0
[53] lubridate_1.9.3 forcats_1.0.0
[55] stringr_1.5.1 purrr_1.0.2
[57] readr_2.1.5 tidyr_1.3.1
[59] tibble_3.2.1 ggplot2_3.4.4
[61] tidyverse_2.0.0 knitr_1.45
[63] dplyr_1.1.4 edgeR_4.0.15
[65] limma_3.58.1
loaded via a namespace (and not attached):
[1] progress_1.2.3 nnet_7.3-19 Biostrings_2.70.1
[4] HDF5Array_1.30.0 vctrs_0.6.5 proxy_0.4-27
[7] digest_0.6.34 png_0.1-8 shape_1.4.6
[10] MASS_7.3-60 reshape2_1.4.4 foreach_1.5.2
[13] httpuv_1.6.14 bumphunter_1.44.0 qvalue_2.34.0
[16] withr_3.0.0 xfun_0.42 ggfun_0.1.4
[19] survival_3.5-7 ellipsis_0.3.2 doRNG_1.8.6
[22] memoise_2.0.1 gson_0.1.0 gtools_3.9.5
[25] tidytree_0.4.6 GlobalOptions_0.1.2 Formula_1.2-5
[28] prettyunits_1.2.0 derfinder_1.36.0 KEGGREST_1.42.0
[31] promises_1.2.1 httr_1.4.7 downloader_0.4
[34] restfulr_0.0.15 rhdf5filters_1.14.1 ComICS_1.0.4
[37] rhdf5_2.46.1 rstudioapi_0.15.0 generics_0.1.3
[40] DOSE_3.28.2 base64enc_0.1-3 babelgene_22.9
[43] zlibbioc_1.48.0 ScaledMatrix_1.10.0 ggraph_2.1.0
[46] polyclip_1.10-6 GenomeInfoDbData_1.2.11 SparseArray_1.2.2
[49] RBGL_1.78.0 interactiveDisplayBase_1.40.0 xtable_1.8-4
[52] pracma_2.4.4 doParallel_1.0.17 evaluate_0.23
[55] S4Arrays_1.2.0 BiocFileCache_2.10.1 hms_1.1.3
[58] irlba_2.3.5.1 colorspace_2.1-0 filelock_1.0.3
[61] magrittr_2.0.3 later_1.3.2 viridis_0.6.5
[64] ggtree_3.10.0 lattice_0.21-9 genefilter_1.84.0
[67] derfinderHelper_1.36.0 shadowtext_0.1.3 cowplot_1.1.3
[70] class_7.3-22 Hmisc_5.1-1 pillar_1.9.0
[73] nlme_3.1-163 iterators_1.0.14 mapplots_1.5.2
[76] compiler_4.3.2 beachmat_2.18.0 stringi_1.8.3
[79] GenomicAlignments_1.38.2 plyr_1.8.9 crayon_1.5.2
[82] abind_1.4-5 BiocIO_1.12.0 gridGraphics_0.5-1
[85] locfit_1.5-9.8 graphlayouts_1.1.0 bit_4.0.5
[88] fastmatch_1.1-4 codetools_0.2-19 BiocSingular_1.18.0
[91] mMCPcounter_1.1.0 GetoptLong_1.0.5 mime_0.12
[94] ff_4.0.12 splines_4.3.2 circlize_0.4.15
[97] Rcpp_1.0.11 dbplyr_2.4.0 sparseMatrixStats_1.14.0
[100] HDO.db_0.99.1 cellranger_1.1.0 GenomicFiles_1.38.0
[103] blob_1.2.4 utf8_1.2.4 clue_0.3-65
[106] BiocVersion_3.18.1 DGEobj_1.1.2 WriteXLS_6.5.0
[109] fs_1.6.3 oligo_1.66.0 checkmate_2.3.1
[112] DelayedMatrixStats_1.24.0 ggplotify_0.1.2 Matrix_1.6-4
[115] statmod_1.5.0 tzdb_0.4.0 tweenr_2.0.2
[118] pkgconfig_2.0.3 tools_4.3.2 cachem_1.0.8
[121] RSQLite_2.3.3 viridisLite_0.4.2 DBI_1.2.1
[124] fastmap_1.1.1 rmarkdown_2.25 scales_1.3.0
[127] Rsamtools_2.18.0 AnnotationHub_3.10.0 patchwork_1.2.0
[130] BiocManager_1.30.22 VariantAnnotation_1.48.1 rpart_4.1.21
[133] farver_2.1.1 mgcv_1.9-0 tidygraph_1.3.1
[136] scatterpie_0.2.1 yaml_2.3.8 foreign_0.8-85
[139] rtracklayer_1.62.0 cli_3.6.1 GEOquery_2.70.0
[142] lifecycle_1.0.4 backports_1.4.1 BiocParallel_1.36.0
[145] timechange_0.3.0 gtable_0.3.4 rjson_0.2.21
[148] ape_5.7-1 jsonlite_1.8.8 affxparser_1.74.0
[151] bitops_1.0-7 bit64_4.0.5 assertthat_0.2.1
[154] yulab.utils_0.1.4 GOSemSim_2.28.1 lazyeval_0.2.2
[157] shiny_1.8.0 htmltools_0.5.7 enrichplot_1.22.0
[160] data.tree_1.1.0 rappdirs_0.3.3 glue_1.6.2
[163] XVector_0.42.0 RCurl_1.98-1.13 treeio_1.26.0
[166] BSgenome_1.70.2 gridExtra_2.3 igraph_2.0.1.1
[169] R6_2.5.1 sva_3.50.0 SingleCellExperiment_1.24.0
[172] cluster_2.1.6 rngtools_1.5.2 Rhdf5lib_1.24.2
[175] aplot_0.2.2 DelayedArray_0.28.0 tidyselect_1.2.0
[178] htmlTable_2.4.2 ggforce_0.4.1 xml2_1.3.6
[181] oligoClasses_1.64.0 testit_0.13 rsvd_1.0.5
[184] munsell_0.5.0 affyio_1.72.0 data.table_1.15.0
[187] htmlwidgets_1.6.4 biomaRt_2.58.2 rlang_1.1.2
[190] rentrez_1.2.3 fansi_1.0.5
I read it but I still have the problem
Your problem is that you need to read CEL files into an
AffyBatchrather than trying to instantiate one using a matrix of data.