Hello,
I tried to perform PCA using R, but I couldn’t proceed due to an error.
Previously, the issue only occurred when I attempted to create a 3D plot(same with github link).
However, thinking it might have been fixed, I uninstalled and reinstalled MetaboAnalystR(1/16/2025).
Now, none of the PCA plots can be generated at all.
My code:
mSet<-PCA.Anal(mSet)
mSet<-PlotPCAPairSummary(mSet, "pca_pair_0_", format = "pdf", dpi = 72, width=NA, 5)
Console:
Error in mSetObj$analSet : $ operator is invalid for atomic vectors
And when I checked mSet,
> mSet
[1] 809
After performing PCA.Anal(mSet), the information stored in mSet changes.
Here is my session Information:
R version 4.4.1 (2024-06-14)
Platform: x86_64-conda-linux-gnu
Running under: CentOS Linux 7 (Core)
Matrix products: default
BLAS/LAPACK: /<local path>/anaconda3/envs/metaboanalyst/lib/libopenblasp-r0.3.28.so; LAPACK version 3.12.0
locale:
[1] LC_CTYPE=ko_KR.UTF-8 LC_NUMERIC=C
[3] LC_TIME=ko_KR.UTF-8 LC_COLLATE=ko_KR.UTF-8
[5] LC_MONETARY=ko_KR.UTF-8 LC_MESSAGES=ko_KR.UTF-8
[7] LC_PAPER=ko_KR.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=ko_KR.UTF-8 LC_IDENTIFICATION=C
tzcode source: system (glibc)
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] memoise_2.0.1 ggplot2_3.5.1 scales_1.3.0
[4] Rserve_1.8-15 MetaboAnalystR_4.0.0
loaded via a namespace (and not attached):
[1] DBI_1.2.3 bitops_1.0-9 pROC_1.18.5
[4] rlang_1.1.4 magrittr_2.0.3 scrime_1.3.5
[7] compiler_4.4.1 RSQLite_2.3.9 vctrs_0.6.5
[10] reshape2_1.4.4 stringr_1.5.1 pkgconfig_2.0.3
[13] fastmap_1.2.0 labeling_0.4.3 caTools_1.18.3
[16] prodlim_2024.06.25 purrr_1.0.2 bit_4.5.0.1
[19] cachem_1.1.0 jsonlite_1.8.9 recipes_1.1.0
[22] blob_1.2.4 BiocParallel_1.40.0 parallel_4.4.1
[25] R6_2.5.1 stringi_1.8.4 RColorBrewer_1.1-3
[28] qs_0.27.2 limma_3.62.1 parallelly_1.41.0
[31] rpart_4.1.24 lubridate_1.9.4 Rcpp_1.0.14
[34] iterators_1.0.14 future.apply_1.11.3 Matrix_1.6-5
[37] splines_4.4.1 nnet_7.3-20 igraph_2.1.3
[40] timechange_0.3.0 tidyselect_1.2.1 stringfish_0.16.0
[43] siggenes_1.80.0 timeDate_4041.110 gplots_3.2.0
[46] codetools_0.2-20 listenv_0.9.1 lattice_0.22-6
[49] tibble_3.2.1 plyr_1.8.9 Biobase_2.66.0
[52] withr_3.0.2 future_1.34.0 survival_3.8-3
[55] RcppParallel_5.1.9 pillar_1.10.1 KernSmooth_2.23-26
[58] foreach_1.5.2 stats4_4.4.1 plotly_4.10.4
[61] generics_0.1.3 munsell_0.5.1 RApiSerialize_0.1.4
[64] gtools_3.9.5 globals_0.16.3 class_7.3-23
[67] glue_1.8.0 lazyeval_0.2.2 tools_4.4.1
[70] data.table_1.16.4 ModelMetrics_1.2.2.2 fgsea_1.32.0
[73] gower_1.0.2 locfit_1.5-9.10 fastmatch_1.1-4
[76] cowplot_1.1.3 grid_4.4.1 Cairo_1.6-2
[79] impute_1.80.0 tidyr_1.3.1 ipred_0.9-15
[82] edgeR_4.4.1 colorspace_2.1-1 nlme_3.1-165
[85] crmn_0.0.21 cli_3.6.3 viridisLite_0.4.2
[88] lava_1.8.1 dplyr_1.1.4 glasso_1.11
[91] pcaMethods_1.98.0 gtable_0.3.6 digest_0.6.37
[94] BiocGenerics_0.52.0 caret_7.0-1 ggrepel_0.9.6
[97] farver_2.1.2 htmlwidgets_1.6.4 htmltools_0.5.8.1
[100] multtest_2.62.0 lifecycle_1.0.4 httr_1.4.7
[103] hardhat_1.4.0 statmod_1.5.0 bit64_4.5.2
[106] MASS_7.3-64
Thank you.