Error in PerformPSEA() with peak list data

Hi! I am trying to run functional analysis with a list of “mz”, “rt”, and “p.value” with MetaboAnalystR. The input list is here:
48Hr_neg_MetaboAnalystR.txt (12.2 KB)

The pipeline I am using is as follows:

mSet_negative<-InitDataObjects(“mass_all”, “mummichog”, FALSE)
mSet_negative<-SetPeakFormat(mSet_negative,“mpr”)
mSet_negative<-UpdateInstrumentParameters(mSet_negative, 5.0, “negative”, “no”, 0.05);
mSet_negative<-Read.PeakListData(mSet_negative, “./48Hr_neg_MetaboAnalystR.txt”)
mSet_negative<-SetRTincluded(mSet_negative, “seconds”)
mSet_negative<-SanityCheckMummichogData(mSet_negative)
mSet_negative<-SetPeakEnrichMethod(mSet_negative, “mum”, “v2”)
mSet_negative<-SetMummichogPval(mSet_negative, 0.05)
mSet_negative<-PerformPSEA(mSet_negative, “hsa_kegg”, “current”, 3, 100)

Error message from PerformPSEA is as follows:
[1] “compoundLib”
[1] “Got 282 mass features.”
[1] “47 initial ECs created!”
Error in data.frame(value = unlist(try2), L1 = rep(try$L1, sapply(try2, :
arguments imply differing number of rows: 65, 0

I am wondering what exactly this means. Is the input list not long enough? The same input file works when SetPeakEnrichMethod is set to “v1” instead of “v2”.

Session info:

sessionInfo()
R version 4.4.1 (2024-06-14)
Platform: aarch64-apple-darwin20
Running under: macOS Sonoma 14.3

Matrix products: default
BLAS: /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRlapack.dylib; LAPACK version 3.12.0

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

time zone: America/Los_Angeles
tzcode source: internal

attached base packages:
[1] stats4 splines grid stats graphics grDevices utils datasets methods base

other attached packages:
[1] FELLA_1.24.0 pathview_1.44.0 MetaboAnalystR_4.0.0 MSnbase_2.30.1 ProtGenerics_1.36.0
[6] S4Vectors_0.42.1 mzR_2.38.0 fgsea_1.30.0 plotly_4.10.4 siggenes_1.78.0
[11] multtest_2.60.0 RColorBrewer_1.1-3 reshape_0.8.9 KEGGgraph_1.64.0 gplots_3.1.3.1
[16] caret_6.0-94 lattice_0.22-6 pls_2.8-4 xtable_1.8-4 magrittr_2.0.3
[21] Hmisc_5.1-3 lars_1.3 fitdistrplus_1.2-1 MASS_7.3-60.2 car_3.1-2
[26] carData_3.0-5 limma_3.60.4 data.table_1.15.4 pROC_1.18.5 Rcpp_1.0.13
[31] sva_3.52.0 mgcv_1.9-1 nlme_3.1-164 pheatmap_1.0.12 genefilter_1.86.0
[36] preprocessCore_1.66.0 Rgraphviz_2.48.0 graph_1.82.0 GlobalAncova_4.22.0 corpcor_1.6.10
[41] globaltest_5.58.0 survival_3.6-4 ROCR_1.0-11 RJSONIO_1.3-1.9 pcaMethods_1.96.0
[46] Biobase_2.64.0 BiocGenerics_0.50.0 impute_1.78.0 som_0.3-5.1 e1071_1.7-14
[51] caTools_1.18.2 randomForest_4.7-1.1 Cairo_1.6-2 scatterplot3d_0.3-44 ellipse_0.5.0
[56] Rserve_1.8-13 pacman_0.5.1 httr_1.4.7 igraph_2.0.3 lubridate_1.9.3
[61] forcats_1.0.0 stringr_1.5.1 purrr_1.0.2 readr_2.1.5 tibble_3.2.1
[66] tidyverse_2.0.0 plyr_1.8.9 readxl_1.4.3 DT_0.33 ggplot2_3.5.1
[71] writexl_1.5.0 tidyr_1.3.1 dplyr_1.1.4 xcms_4.2.2 BiocParallel_1.38.0
[76] shiny_1.9.1

loaded via a namespace (and not attached):
[1] IRanges_2.38.1 GSEABase_1.66.0 progress_1.2.3 vsn_3.72.0
[5] urlchecker_1.0.1 nnet_7.3-19 Biostrings_2.72.1 vctrs_0.6.5
[9] RApiSerialize_0.1.3 digest_0.6.36 png_0.1-8 proxy_0.4-27
[13] parallelly_1.38.0 reshape2_1.4.4 httpuv_1.6.15 foreach_1.5.2
[17] withr_3.0.1 xfun_0.46 ellipsis_0.3.2 MetaboCoreUtils_1.12.0
[21] memoise_2.0.1 profvis_0.3.8 gtools_3.9.5 Formula_1.2-5
[25] prettyunits_1.2.0 KEGGREST_1.44.1 promises_1.3.0 globals_0.16.3
[29] ps_1.7.7 stringfish_0.16.0 rstudioapi_0.16.0 UCSC.utils_1.0.0
[33] miniUI_0.1.1.1 generics_0.1.3 base64enc_0.1-3 MassSpecWavelet_1.70.0
[37] processx_3.8.4 curl_5.2.1 ncdf4_1.22 zlibbioc_1.50.0
[41] GenomeInfoDbData_1.2.12 SparseArray_1.4.8 desc_1.4.3 doParallel_1.0.17
[45] evaluate_0.24.0 S4Arrays_1.4.1 hms_1.1.3 GenomicRanges_1.56.1
[49] qs_0.26.3 colorspace_2.1-1 later_1.3.2 viridis_0.6.5
[53] MsCoreUtils_1.16.1 future.apply_1.11.2 XML_3.99-0.17 cowplot_1.1.3
[57] matrixStats_1.3.0 class_7.3-22 pillar_1.9.0 iterators_1.0.14
[61] compiler_4.4.1 stringi_1.8.4 gower_1.0.1 SummarizedExperiment_1.34.0
[65] dendextend_1.17.1 devtools_2.4.5 crayon_1.5.3 abind_1.4-5
[69] locfit_1.5-9.10 org.Hs.eg.db_3.19.1 bit_4.0.5 fastmatch_1.1-4
[73] codetools_0.2-20 recipes_1.1.0 crosstalk_1.2.1 bslib_0.8.0
[77] mime_0.12 MultiAssayExperiment_1.30.3 cellranger_1.1.0 knitr_1.48
[81] blob_1.2.4 utf8_1.2.4 clue_0.3-65 AnnotationFilter_1.28.0
[85] fs_1.6.4 QFeatures_1.14.2 listenv_0.9.1 mzID_1.42.0
[89] checkmate_2.3.2 pkgbuild_1.4.4 Matrix_1.7-0 callr_3.7.6
[93] statmod_1.5.0 tzdb_0.4.0 pkgconfig_2.0.3 tools_4.4.1
[97] cachem_1.1.0 RSQLite_2.3.7 viridisLite_0.4.2 DBI_1.2.3
[101] fastmap_1.2.0 rmarkdown_2.27 scales_1.3.0 crmn_0.0.21
[105] usethis_3.0.0 sass_0.4.9 BiocManager_1.30.23 scrime_1.3.5
[109] rpart_4.1.23 yaml_2.3.10 VGAM_1.1-11 MatrixGenerics_1.16.0
[113] foreign_0.8-86 cli_3.6.3 lifecycle_1.0.4 lava_1.8.0
[117] sessioninfo_1.2.2 backports_1.5.0 annotate_1.82.0 timechange_0.3.0
[121] gtable_0.3.5 parallel_4.4.1 jsonlite_1.8.8 edgeR_4.2.1
[125] bitops_1.0-8 bit64_4.0.5 glasso_1.11 MsExperiment_1.6.0
[129] RcppParallel_5.1.8 jquerylib_0.1.4 timeDate_4032.109 lazyeval_0.2.2
[133] htmltools_0.5.8.1 affy_1.82.0 glue_1.7.0 XVector_0.44.0
[137] RCurl_1.98-1.16 MALDIquant_1.22.2 gridExtra_2.3 R6_2.5.1
[141] cluster_2.1.6 pkgload_1.4.0 Spectra_1.14.1 GenomeInfoDb_1.40.1
[145] ipred_0.9-15 DelayedArray_0.30.1 tidyselect_1.2.1 htmlTable_2.4.3
[149] AnnotationDbi_1.66.0 future_1.34.0 MsFeatures_1.12.0 ModelMetrics_1.2.2.2
[153] munsell_0.5.1 KernSmooth_2.23-24 affyio_1.74.0 htmlwidgets_1.6.4
[157] rlang_1.1.4 remotes_2.5.0 fansi_1.0.6 hardhat_1.4.0
[161] prodlim_2024.06.25 PSMatch_1.8.0

Thank you very much

No. It should not work. Even it works, the results should not be trusted.

The PSEA or mummichog function is designed for high resolution MS peaks with good coverage of the metabolic pathway space. A minimum of 3000 peaks is highly recommended.

This topic was automatically closed after 2 days. New replies are no longer allowed.