Entering edit mode
Hi Everyone, Im trying to deconvolute cell types for EPICV2 Array data, and am having issues with both minifi's estimateCellCounts function and the Flow.Sorted.Blood.Epic estimateCellCounts2 functions.
propEPIC<-estimateCellCounts2(rgSetFlt, compositeCellType = "Blood",
processMethod = "preprocessNoob",
probeSelect = "IDOL",
cellTypes = c("CD8T", "CD4T", "NK", "Bcell",
"Mono", "Neu"))
Running this and estimateCellCounts in minifi both give me the same .convertArray_450k_epic error:
snapshotDate(): 2022-10-31
snapshotDate(): 2022-10-31
see ?FlowSorted.Blood.EPIC and browseVignettes('FlowSorted.Blood.EPIC') for documentation
loading from cache
[convertArray] Casting as IlluminaHumanMethylationEPIC
Error in .convertArray_450k_epic(rgSet = object, outType = outType, verbose = verbose) :
.is450k(rgSet) || .isEPIC(rgSet) is not TRUE
Is this because of the IlluminaHumanMethylationEPICv2 Annotation I am using? Thanks in advance for any help!
rgSetFlt
class: RGChannelSet
dim: 1105209 219
metadata(0):
assays(2): Green Red
rownames(1105209): 1600157 1600179 ... 99810982 99810990
rowData names(0):
colnames(219): 207925050049_R07C01 207882990112_R05C01 ...
207865030003_R05C01 207865030077_R04C01
colData names(15): Sample_Name Sample_Plate ... Basename filenames
Annotation
array: IlluminaHumanMethylationEPICv2
annotation: 20a1.hg38
Session Info:
sessionInfo( )
R version 4.2.2 (2022-10-31)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: CentOS Linux 7 (Core)
Matrix products: default
BLAS/LAPACK: /u/local/compilers/intel/oneapi/2022.1.1/mkl/2022.0.1/lib/intel64/libmkl_gf_lp64.so.2
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] grid parallel stats4 stats graphics grDevices utils
[8] datasets methods base
other attached packages:
[1] IlluminaHumanMethylationEPICv2manifest_0.99.1
[2] IlluminaHumanMethylationEPICmanifest_0.3.0
[3] FlowSorted.Blood.EPIC_2.2.0
[4] methylCC_1.12.0
[5] FlowSorted.Blood.450k_1.36.0
[6] remotes_2.4.2.1
[7] RColorBrewer_1.1-3
[8] DMRcatedata_2.16.0
[9] ExperimentHub_2.6.0
[10] AnnotationHub_3.6.0
[11] BiocFileCache_2.11.1
[12] dbplyr_2.4.0
[13] mCSEA_1.18.0
[14] Homo.sapiens_1.3.1
[15] TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2
[16] org.Hs.eg.db_3.16.0
[17] GO.db_3.16.0
[18] OrganismDbi_1.40.0
[19] GenomicFeatures_1.50.4
[20] AnnotationDbi_1.60.2
[21] mCSEAdata_1.18.0
[22] DMRcate_2.12.0
[23] Gviz_1.42.1
[24] minfiData_0.44.0
[25] IlluminaHumanMethylation450kmanifest_0.4.0
[26] missMethyl_1.32.1
[27] IlluminaHumanMethylationEPICanno.ilm10b4.hg19_0.6.0
[28] IlluminaHumanMethylation450kanno.ilmn12.hg19_0.6.1
[29] minfi_1.44.0
[30] bumphunter_1.40.0
[31] locfit_1.5-9.8
[32] iterators_1.0.14
[33] foreach_1.5.2
[34] Biostrings_2.66.0
[35] XVector_0.38.0
[36] SummarizedExperiment_1.28.0
[37] Biobase_2.58.0
[38] MatrixGenerics_1.10.0
[39] matrixStats_1.1.0
[40] GenomicRanges_1.50.2
[41] GenomeInfoDb_1.34.9
[42] IRanges_2.32.0
[43] S4Vectors_0.36.2
[44] BiocGenerics_0.44.0
[45] limma_3.54.2
[46] lubridate_1.9.3
[47] forcats_1.0.0
[48] stringr_1.5.1
[49] dplyr_1.1.4
[50] purrr_1.0.2
[51] readr_2.1.5
[52] tidyr_1.3.1
[53] tibble_3.2.1
[54] ggplot2_3.5.0
[55] tidyverse_2.0.0
[56] BiocManager_1.30.22
loaded via a namespace (and not attached):
[1] rappdirs_0.3.3 rtracklayer_1.58.0
[3] R.methodsS3_1.8.2 bit64_4.0.5
[5] knitr_1.45 DelayedArray_0.24.0
[7] R.utils_2.12.3 data.table_1.15.0
[9] rpart_4.1.19 KEGGREST_1.38.0
[11] RCurl_1.98-1.14 GEOquery_2.66.0
[13] AnnotationFilter_1.22.0 generics_0.1.3
[15] preprocessCore_1.60.2 RSQLite_2.3.5
[17] bit_4.0.5 tzdb_0.4.0
[19] xml2_1.3.6 httpuv_1.6.14
[21] xfun_0.42 hms_1.1.3
[23] evaluate_0.23 promises_1.2.1
[25] fansi_1.0.6 restfulr_0.0.15
[27] scrime_1.3.5 progress_1.2.3
[29] readxl_1.4.3 DBI_1.2.2
[31] htmlwidgets_1.6.4 reshape_0.8.9
[33] ellipsis_0.3.2 backports_1.4.1
[35] permute_0.9-7 annotate_1.76.0
[37] biomaRt_2.54.1 deldir_2.0-4
[39] sparseMatrixStats_1.10.0 vctrs_0.6.5
[41] ensembldb_2.22.0 cachem_1.0.8
[43] withr_3.0.0 BSgenome_1.66.3
[45] checkmate_2.3.1 GenomicAlignments_1.34.1
[47] prettyunits_1.2.0 mclust_6.1
[49] cluster_2.1.4 lazyeval_0.2.2
[51] crayon_1.5.2 genefilter_1.80.3
[53] edgeR_3.40.2 pkgconfig_2.0.3
[55] nlme_3.1-160 ProtGenerics_1.30.0
[57] nnet_7.3-18 rlang_1.1.3
[59] lifecycle_1.0.4 filelock_1.0.3
[61] dichromat_2.0-0.1 cellranger_1.1.0
[63] graph_1.76.0 rngtools_1.5.2
[65] base64_2.0.1 Matrix_1.5-1
[67] Rhdf5lib_1.20.0 base64enc_0.1-3
[69] png_0.1-8 rjson_0.2.21
[71] bitops_1.0-7 R.oo_1.26.0
[73] rhdf5filters_1.10.1 blob_1.2.4
[75] DelayedMatrixStats_1.20.0 doRNG_1.8.6
[77] nor1mix_1.3-2 jpeg_0.1-10
[79] scales_1.3.0 memoise_2.0.1
[81] magrittr_2.0.3 plyr_1.8.9
[83] zlibbioc_1.44.0 compiler_4.2.2
[85] BiocIO_1.8.0 illuminaio_0.40.0
[87] Rsamtools_2.14.0 cli_3.6.2
[89] DSS_2.46.0 htmlTable_2.4.2
[91] Formula_1.2-5 MASS_7.3-58.1
[93] tidyselect_1.2.0 stringi_1.8.3
[95] yaml_2.3.8 askpass_1.2.0
[97] latticeExtra_0.6-30 VariantAnnotation_1.44.1
[99] tools_4.2.2 timechange_0.3.0
[101] rstudioapi_0.15.0 foreign_0.8-83
[103] bsseq_1.34.0 gridExtra_2.3
[105] plyranges_1.18.0 digest_0.6.34
[107] shiny_1.8.0 quadprog_1.5-8
[109] Rcpp_1.0.12 siggenes_1.72.0
[111] BiocVersion_3.16.0 later_1.3.2
[113] httr_1.4.7 biovizBase_1.46.0
[115] colorspace_2.1-0 XML_3.99-0.16.1
[117] splines_4.2.2 RBGL_1.74.0
[119] statmod_1.5.0 multtest_2.54.0
[121] xtable_1.8-4 R6_2.5.1
[123] Hmisc_5.1-1 pillar_1.9.0
[125] htmltools_0.5.7 mime_0.12
[127] glue_1.7.0 fastmap_1.1.1
[129] BiocParallel_1.32.6 interactiveDisplayBase_1.36.0
[131] beanplot_1.3.1 codetools_0.2-18
[133] utf8_1.2.4 lattice_0.20-45
[135] curl_5.2.0 gtools_3.9.5
[137] openssl_2.1.1 interp_1.1-6
[139] survival_3.4-0 rmarkdown_2.25
[141] munsell_0.5.0 rhdf5_2.42.1
[143] GenomeInfoDbData_1.2.9 HDF5Array_1.26.0
[145] gtable_0.3.4
Thank you, James. That is correct. I am a bit nervous about including non-Bioconductor packages in the pipeline. Please see below for the current solution.
Yes, we should probably see if Zuguang Gu will submit those.
Thank you so much James and Lucas! The solution posted works and now I have the cell proportions. Really appreciate the help!