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I am using DEP package to analyze proteomics data. I did PCA for my samples (see the following plot) and wish to extract proteins in PC1 for further analysis. However, the objects x and y generated by the following code do not contain the information of the principal component (only the coordinates). May I ask for a solution?
x <- plot_pca(dep_MDA231, x = 1, y = 2, n = 500, point_size = 4,plot = T)
y <- plot_pca(dep_MDA231, x = 1, y = 2, n = 500, point_size = 4,plot = F)
sessionInfo( )
R version 4.2.2 (2022-10-31)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Ventura 13.2
Matrix products: default
LAPACK: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRlapack.dylib
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] stats4 stats graphics grDevices utils datasets methods base
other attached packages:
[1] magick_2.7.3 DEP_1.20.0 forcats_1.0.0
[4] stringr_1.5.0 dplyr_1.1.0 purrr_1.0.1
[7] readr_2.1.4 tidyr_1.3.0 tibble_3.1.8
[10] ggplot2_3.4.1 tidyverse_1.3.2 SummarizedExperiment_1.28.0
[13] Biobase_2.58.0 GenomicRanges_1.50.2 GenomeInfoDb_1.34.9
[16] IRanges_2.32.0 S4Vectors_0.36.1 BiocGenerics_0.44.0
[19] MatrixGenerics_1.10.0 matrixStats_0.63.0
loaded via a namespace (and not attached):
[1] googledrive_2.0.0 colorspace_2.1-0 rjson_0.2.21
[4] ellipsis_0.3.2 circlize_0.4.15 XVector_0.38.0
[7] GlobalOptions_0.1.2 fs_1.6.1 clue_0.3-64
[10] rstudioapi_0.14 farver_2.1.1 mzR_2.32.0
[13] affyio_1.68.0 DT_0.27 fansi_1.0.4
[16] mvtnorm_1.1-3 lubridate_1.9.2 xml2_1.3.3
[19] codetools_0.2-18 ncdf4_1.21 doParallel_1.0.17
[22] impute_1.72.3 jsonlite_1.8.4 broom_1.0.3
[25] cluster_2.1.4 vsn_3.66.0 dbplyr_2.3.0
[28] png_0.1-8 shinydashboard_0.7.2 shiny_1.7.4
[31] BiocManager_1.30.19 compiler_4.2.2 httr_1.4.4
[34] backports_1.4.1 fastmap_1.1.0 assertthat_0.2.1
[37] Matrix_1.5-1 gmm_1.7 gargle_1.3.0
[40] limma_3.54.1 cli_3.6.0 later_1.3.0
[43] htmltools_0.5.4 tools_4.2.2 gtable_0.3.1
[46] glue_1.6.2 GenomeInfoDbData_1.2.9 affy_1.76.0
[49] Rcpp_1.0.10 MALDIquant_1.22 cellranger_1.1.0
[52] vctrs_0.5.2 preprocessCore_1.60.2 iterators_1.0.14
[55] tmvtnorm_1.5 rvest_1.0.3 mime_0.12
[58] timechange_0.2.0 lifecycle_1.0.3 XML_3.99-0.13
[61] googlesheets4_1.0.1 zoo_1.8-11 zlibbioc_1.44.0
[64] MASS_7.3-58.1 scales_1.2.1 MSnbase_2.24.2
[67] promises_1.2.0.1 pcaMethods_1.90.0 hms_1.1.2
[70] ProtGenerics_1.30.0 sandwich_3.0-2 parallel_4.2.2
[73] RColorBrewer_1.1-3 ComplexHeatmap_2.14.0 gridExtra_2.3
[76] stringi_1.7.12 foreach_1.5.2 BiocParallel_1.32.5
[79] shape_1.4.6 rlang_1.0.6 pkgconfig_2.0.3
[82] bitops_1.0-7 imputeLCMD_2.1 mzID_1.36.0
[85] lattice_0.20-45 labeling_0.4.2 htmlwidgets_1.6.1
[88] tidyselect_1.2.0 norm_1.0-10.0 plyr_1.8.8
[91] magrittr_2.0.3 R6_2.5.1 generics_0.1.3
[94] DelayedArray_0.24.0 DBI_1.1.3 pillar_1.8.1
[97] haven_2.5.1 withr_2.5.0 MsCoreUtils_1.10.0
[100] RCurl_1.98-1.10 modelr_0.1.10 crayon_1.5.2
[103] fdrtool_1.2.17 utf8_1.2.3 tzdb_0.3.0
[106] GetoptLong_1.0.5 grid_4.2.2 readxl_1.4.2
[109] reprex_2.0.2 digest_0.6.31 xtable_1.8-4
[112] httpuv_1.6.8 munsell_0.5.0
Which information do you want precisely ? plot_pca is just a wrapper for PCA visualization, but it applies prcomp to perform PCA : https://rdrr.io/bioc/DEP/src/R/plot_functions_explore.R