How to extract proteins from PCs in plot_pca in DEP package?

Question

[Wang Cong]

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

Answer 1

[ATpoint]

You can run PCA yourself with only few lines of code. See here under PCA Basic normalization, batch correction and visualization of RNA-seq data

You can then use the prcomp output to filter for all relevant elements.