The CaDrA package currently supports four scoring functions to search for subsets of genomic features that are likely associated with a specific outcome of interest (e.g., protein expression, pathway activity, etc.)

  1. Kolmogorov-Smirnov Method (ks)
  2. Conditional Mutual Information Method (revealer)
  3. Wilcoxon Rank-Sum Method (wilcox)
  4. Custom - An User Defined Scoring Method (custom)

Below, we run candidate_search() over the top 3 starting features using each of the four scoring functions described above.

Important Note:

Load packages

library(CaDrA)
library(pheatmap)
library(SummarizedExperiment)

Load required datasets

  1. A binary features matrix also known as Feature Set (such as somatic mutations, copy number alterations, chromosomal translocations, etc.) The 1/0 row vectors indicate the presence/absence of ‘omics’ features in the samples. The Feature Set can be a matrix or an object of class SummarizedExperiment from SummarizedExperiment package)
  2. A vector of continuous scores (or Input Scores) representing a functional response of interest (such as protein expression, pathway activity, etc.)
# Load pre-computed feature set
data(sim_FS)
 
# Load pre-computed input scores
data(sim_Scores)

Heatmap of simulated feature set

The simulated dataset, sim_FS, comprises of 1000 genomic features and 100 sample profiles. There are 10 left-skewed (i.e. True Positive or TP) and 990 uniformly-distributed (i.e. True Null or TN) features simulated in the dataset. Below is a heatmap of the first 100 features.

mat <- SummarizedExperiment::assay(sim_FS)
pheatmap::pheatmap(mat[1:100, ], color = c("white", "red"), cluster_rows = FALSE, cluster_cols = FALSE)

Search for a subset of genomic features that are likely associated with a functional response of interest using four scoring methods

1. Kolmogorov-Smirnov Scoring Method

See ?ks_rowscore for more details

ks_topn_l <- CaDrA::candidate_search(
  FS = sim_FS,
  input_score = sim_Scores,
  method = "ks_pval",          # Use Kolmogorow-Smirnow scoring function 
  method_alternative = "less", # Use one-sided hypothesis testing
  weights = NULL,              # If weights is provided, perform a weighted-KS test
  search_method = "both",      # Apply both forward and backward search
  top_N = 3,                   # Evaluate top 3 starting points for the search
  max_size = 10,               # Allow at most 10 features in meta-feature matrix
  do_plot = FALSE,             # We will plot it AFTER finding the best hits
  best_score_only = FALSE      # Return all results from the search
)

# Now we can fetch the feature set of top N features that corresponded to the best scores over the top N search
ks_topn_best_meta <- topn_best(ks_topn_l)

# Visualize best meta-feature result
meta_plot(topn_best_list = ks_topn_best_meta)

2. Wilcoxon Rank-Sum Scoring Method

See ?wilcox_rowscore for more details

wilcox_topn_l <- CaDrA::candidate_search(
  FS = sim_FS,
  input_score = sim_Scores,
  method = "wilcox_pval",      # Use Wilcoxon Rank-Sum scoring function
  method_alternative = "less", # Use one-sided hypothesis testing
  search_method = "both",      # Apply both forward and backward search
  top_N = 3,                   # Evaluate top 3 starting points for the search
  max_size = 10,               # Allow at most 10 features in meta-feature matrix
  do_plot = FALSE,             # We will plot it AFTER finding the best hits
  best_score_only = FALSE      # Return all results from the search
)

# Now we can fetch the feature set of top N feature that corresponded to the best scores over the top N search
wilcox_topn_best_meta <- topn_best(topn_list = wilcox_topn_l)

# Visualize best meta-feature result
meta_plot(topn_best_list = wilcox_topn_best_meta)

3. Conditional Mutual Information Scoring Method

See ?revealer_rowscore for more details

revealer_topn_l <- CaDrA::candidate_search(
  FS = sim_FS,
  input_score = sim_Scores,
  method = "revealer",         # Use REVEALER's CMI scoring function
  search_method = "both",      # Apply both forward and backward search
  top_N = 3,                   # Evaluate top 3 starting points for the search
  max_size = 10,               # Allow at most 10 features in meta-feature matrix
  do_plot = FALSE,             # We will plot it AFTER finding the best hits
  best_score_only = FALSE      # Return all results from the search
)

# Now we can fetch the ESet of top feature that corresponded to the best scores over the top N search
revealer_topn_best_meta <- topn_best(topn_list = revealer_topn_l)

# Visualize best meta-feature result
meta_plot(topn_best_list = revealer_topn_best_meta)

4. Custom - An User Defined Scoring Method

See ?custom_rowscore for more details

# A customized function using ks-test
customized_ks_rowscore <- function(FS, input_score, meta_feature=NULL, alternative="less", metric="pval"){
  
  # Check if meta_feature is provided
  if(!is.null(meta_feature)){
    # Getting the position of the known meta features
    locs <- match(meta_feature, row.names(FS))
    
    # Taking the union across the known meta features
    if(length(meta_feature) > 1) {
      meta_vector <- as.numeric(ifelse(colSums(FS[meta_feature,]) == 0, 0, 1))
    }else{
      meta_vector <- as.numeric(FS[meta_feature,])
    }
    
    # Remove the meta features from the binary feature matrix
    # and taking logical OR btw the remaining features with the meta vector
    FS <- base::sweep(FS[-locs, , drop=FALSE], 2, meta_vector, `|`)*1
    
    # Check if there are any features that are all 1s generated from
    # taking the union between the matrix
    # We cannot compute statistics for such features and thus they need
    # to be filtered out
    if(any(rowSums(FS) == ncol(FS))){
      warning("Features with all 1s generated from taking the matrix union ",
              "will be removed before progressing...\n")
      FS <- FS[rowSums(FS) != ncol(FS), , drop=FALSE]
    }
  }
  
  # KS is a ranked-based method
  # So we need to sort input_score from highest to lowest values
  input_score <- sort(input_score, decreasing=TRUE)
  
  # Re-order the matrix based on the order of input_score
  FS <- FS[, names(input_score), drop=FALSE]  
  
  # Compute the scores using the KS method
  ks <- apply(FS, 1, function(r){ 
    x = input_score[which(r==1)]; 
    y = input_score[which(r==0)];
    res <- ks.test(x, y, alternative=alternative)
    return(c(res$statistic, res$p.value))
  })
  
  # Obtain score statistics
  stat <- ks[1,]
  
  # Obtain p-values and change values of 0 to the machine lowest value 
  # to avoid taking -log(0)
  pval <- ks[2,]
  pval[which(pval == 0)] <- .Machine$double.xmin
  
  # Compute the -log(pval)
  # Make sure scores has names that match the row names of FS object
  pval <- -log(pval)
  
  # Determine which metric to returned the scores
  if(metric == "pval"){
    scores <- pval
  }else{
    scores <- stat
  }
  
  names(scores) <- rownames(FS)
  
  return(scores)
  
}

# Search for best features using a custom-defined function
custom_topn_l <- CaDrA::candidate_search(
  FS = SummarizedExperiment::assay(sim_FS),
  input_score = sim_Scores,
  method = "custom",                        # Use custom scoring function
  custom_function = customized_ks_rowscore, # Use a customized scoring function
  custom_parameters = NULL,                 # Additional parameters to pass to custom_function
  search_method = "both",                   # Apply both forward and backward search
  top_N = 3,                                # Evaluate top 3 starting points for the search
  max_size = 10,                            # Allow at most 10 features in meta-feature matrix
  do_plot = FALSE,                          # We will plot it AFTER finding the best hits
  best_score_only = FALSE                   # Return all results from the search
)

# Now we can fetch the feature set of top N feature that corresponded to the best scores over the top N search
custom_topn_best_meta <- topn_best(topn_list = custom_topn_l)

# Visualize best meta-feature result
meta_plot(topn_best_list = custom_topn_best_meta)

SessionInfo

sessionInfo()
R version 4.4.0 beta (2024-04-15 r86425)
Platform: x86_64-pc-linux-gnu
Running under: Ubuntu 22.04.4 LTS

Matrix products: default
BLAS:   /home/biocbuild/bbs-3.19-bioc/R/lib/libRblas.so 
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

time zone: America/New_York
tzcode source: system (glibc)

attached base packages:
[1] stats4    stats     graphics  grDevices utils     datasets  methods  
[8] base     

other attached packages:
 [1] CaDrA_1.2.0                 pheatmap_1.0.12            
 [3] SummarizedExperiment_1.34.0 Biobase_2.64.0             
 [5] GenomicRanges_1.56.0        GenomeInfoDb_1.40.0        
 [7] IRanges_2.38.0              S4Vectors_0.42.0           
 [9] BiocGenerics_0.50.0         MatrixGenerics_1.16.0      
[11] matrixStats_1.3.0           testthat_3.2.1.1           
[13] devtools_2.4.5              usethis_2.2.3              

loaded via a namespace (and not attached):
 [1] bitops_1.0-7            tcltk_4.4.0             remotes_2.5.0          
 [4] rlang_1.1.3             magrittr_2.0.3          compiler_4.4.0         
 [7] vctrs_0.6.5             reshape2_1.4.4          stringr_1.5.1          
[10] profvis_0.3.8           pkgconfig_2.0.3         crayon_1.5.2           
[13] fastmap_1.1.1           XVector_0.44.0          ellipsis_0.3.2         
[16] labeling_0.4.3          caTools_1.18.2          utf8_1.2.4             
[19] promises_1.3.0          rmarkdown_2.26          sessioninfo_1.2.2      
[22] UCSC.utils_1.0.0        purrr_1.0.2             xfun_0.43              
[25] zlibbioc_1.50.0         cachem_1.0.8            jsonlite_1.8.8         
[28] highr_0.10              later_1.3.2             DelayedArray_0.30.0    
[31] parallel_4.4.0          R6_2.5.1                RColorBrewer_1.1-3     
[34] bslib_0.7.0             stringi_1.8.3           pkgload_1.3.4          
[37] brio_1.1.5              jquerylib_0.1.4         Rcpp_1.0.12            
[40] iterators_1.0.14        knitr_1.46              R.utils_2.12.3         
[43] httpuv_1.6.15           Matrix_1.7-0            R.cache_0.16.0         
[46] tidyselect_1.2.1        rstudioapi_0.16.0       abind_1.4-5            
[49] yaml_2.3.8              doParallel_1.0.17       gplots_3.1.3.1         
[52] codetools_0.2-20        miniUI_0.1.1.1          misc3d_0.9-1           
[55] pkgbuild_1.4.4          lattice_0.22-6          tibble_3.2.1           
[58] plyr_1.8.9              shiny_1.8.1.1           withr_3.0.0            
[61] evaluate_0.23           desc_1.4.3              urlchecker_1.0.1       
[64] pillar_1.9.0            KernSmooth_2.23-22      foreach_1.5.2          
[67] generics_0.1.3          rprojroot_2.0.4         ggplot2_3.5.1          
[70] munsell_0.5.1           scales_1.3.0            gtools_3.9.5           
[73] xtable_1.8-4            glue_1.7.0              ppcor_1.1              
[76] tools_4.4.0             fs_1.6.4                grid_4.4.0             
[79] colorspace_2.1-0        GenomeInfoDbData_1.2.12 cli_3.6.2              
[82] fansi_1.0.6             S4Arrays_1.4.0          dplyr_1.1.4            
[85] gtable_0.3.5            R.methodsS3_1.8.2       sass_0.4.9             
[88] digest_0.6.35           SparseArray_1.4.0       farver_2.1.1           
[91] htmlwidgets_1.6.4       memoise_2.0.1           htmltools_0.5.8.1      
[94] R.oo_1.26.0             lifecycle_1.0.4         httr_1.4.7             
[97] mime_0.12               MASS_7.3-60.2