How to extract proteins from PCs in plot_pca in DEP package?
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Entering edit mode
11 months ago
Wang Cong ▴ 10

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?

enter image description here

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
DEP PCA Proteomics • 400 views
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0
Entering edit mode
11 months ago
ATpoint 80k

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.

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