Extract PCs and Loadings from a superpcOut- or aespcOut-class Object.

Given an object of class aespcOut or superpcOut, as returned by the functions AESPCA_pVals or SuperPCA_pVals, respectively, and the name or unique ID of a pathway, return a data frame of the principal components and a data frame of the loading vectors corresponding to that pathway.

getPathPCLs(pcOut, pathway_char, ...)

# S3 method for superpcOut
getPathPCLs(pcOut, pathway_char, ...)

# S3 method for aespcOut
getPathPCLs(pcOut, pathway_char, ...)

Arguments

pcOut

An object of classes superpcOut or aespcOut as returned by the SuperPCA_pVals or AESPCA_pVals functions, respectively.
pathway_char

A character string of the name or unique identifier of a pathway
...

Dots for additional arguments (currently unused).

Value

A list of four elements:

  • PCs : A data frame of the principal components

  • Loadings : A matrix of the loading vectors with features in the row names

  • pathway : The unique pathway identifier for the pcOut object

  • term : The name of the pathway

NULL

NULL

Details

Match the supplied pathway character string to either the pathways or terms columns of the pVals_df data frame within the pcOut object. Then, subset the loadings_ls and PCs_ls lists for their entries which match the supplied pathway. Finally, return a list of the PCs, loadings, and the pathway ID and name.

Examples


  ###  Load Data  ###
  data("colonSurv_df")
  data("colon_pathwayCollection")

  ###  Create -Omics Container  ###
  colon_Omics <- CreateOmics(
    assayData_df = colonSurv_df[, -(2:3)],
    pathwayCollection_ls = colon_pathwayCollection,
    response = colonSurv_df[, 1:3],
    respType = "survival"
  )
#> 
#>   ======  Creating object of class OmicsSurv  =======
#> The input pathway database included 676 unique features.
#> The input assay dataset included 656 features.
#> Only pathways with at least 3 or more features included in the assay dataset are
#>   tested (specified by minPathSize parameter). There are 15 pathways which meet
#>   this criterion.
#> Because pathwayPCA is a self-contained test (PMID: 17303618), only features in
#>   both assay data and pathway database are considered for analysis. There are 615
#>   such features shared by the input assay and pathway database.

  ###  Calculate Supervised PCA Pathway p-Values  ###
  colon_superpc <- SuperPCA_pVals(
    colon_Omics,
    numPCs = 2,
    parallel = TRUE,
    numCores = 2,
    adjustment = "BH"
  )
#> Initializing Computing Cluster: 
#> DONE
#> Calculating Pathway Test Statistics in Parallel: 
#> DONE
#> Calculating Pathway Critical Values in Parallel: 
#> DONE
#> Calculating Pathway p-Values: 
#> DONE
#> Adjusting p-Values and Sorting Pathway p-Value Data Frame: 
#> DONE

  ###  Extract PCs and Loadings  ###
  getPathPCLs(
    colon_superpc,
    "KEGG_PENTOSE_PHOSPHATE_PATHWAY"
  )
#> $PCs
#> # A tibble: 250 x 3
#>    sampleID       V1      V2
#>    <chr>       <dbl>   <dbl>
#>  1 subj1     0.0404   0.0457
#>  2 subj2     0.129   -0.0526
#>  3 subj3    -0.0267   0.0764
#>  4 subj4     0.0385  -0.0676
#>  5 subj5     0.0577  -0.0284
#>  6 subj6     0.00634  0.0518
#>  7 subj7    -0.00680  0.0869
#>  8 subj8     0.0367  -0.0246
#>  9 subj9    -0.00828 -0.0105
#> 10 subj10   -0.109   -0.0215
#> # … with 240 more rows
#> 
#> $Loadings
#> # A tibble: 11 x 3
#>    featureID     PC1     PC2
#>    <chr>       <dbl>   <dbl>
#>  1 RPE        -0.330 11.3   
#>  2 RPIA       -8.68   0.0615
#>  3 PGLS      -11.3   -5.62  
#>  4 PFKL      -11.0   -3.01  
#>  5 TKT       -12.0    6.25  
#>  6 TKTL2      -3.04   1.34  
#>  7 PGD        -7.83   4.39  
#>  8 H6PD       -3.51  -7.57  
#>  9 PRPS1L1     0.286  1.95  
#> 10 PRPS1      -0.283 12.9   
#> 11 PFKP       -0.576  3.69  
#> 
#> $pathway
#> [1] "pathway3"
#> 
#> $term
#> [1] "KEGG_PENTOSE_PHOSPHATE_PATHWAY"
#> 
#> $description
#> [1] NA
#>