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.)

Kolmogorov-Smirnov Method (ks)
Wilcoxon Rank-Sum Method (wilcox)
Conditional Mutual Information Method (revealer)
K-Nearest Neighbor Mutual Information Estimator (knnmi)
Correlation Method (correlation)
Custom - An User Defined Scoring Method (custom)

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

Important Notes:

The legacy or deprecated function topn_eval() is equivalent to the new and recommended candidate_search() function

Load packages

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

Load required datasets

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)
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 each of the 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 Kolmogorov-Smirnov 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 from REVEALER

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. K-Nearest Neighbor Mutual Information Estimator from knnmi package

See ?knnmi_rowscore for more details

knnmi_topn_l <- CaDrA::candidate_search(
  FS = sim_FS,
  input_score = sim_Scores,
  method = "knnmi",            # Use knnmi 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
knnmi_topn_best_meta <- topn_best(topn_list = knnmi_topn_l)

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

5. Correlation Scoring Method

See ?corr_rowscore for more details

corr_topn_l <- CaDrA::candidate_search(
  FS = SummarizedExperiment::assay(sim_FS),
  input_score = sim_Scores,
  method = "correlation",      # Use correlation scoring function
  cmethod = "spearman",        # Use spearman correlation scoring function
  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
corr_topn_best_meta <- topn_best(topn_list = corr_topn_l)

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

6. 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, weights=NULL, meta_feature=NULL, alternative="less", metric="pval"){
  
  metric <- match.arg(metric)
  alternative <- match.arg(alternative)
  
  # 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(locs) > 1) {
      meta_vector <- as.numeric(ifelse(colSums(FS[locs,]) == 0, 0, 1))
    }else{
      meta_vector <- as.numeric(FS[locs, , drop=FALSE])
    }
    
    # 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))){
      verbose("Features with all 1s generated from taking the matrix union ",
              "will be removed before progressing...\n")
      FS <- FS[rowSums(FS) != ncol(FS), , drop=FALSE]
      # If no features remained after filtering, exist the function
      if(nrow(FS) == 0) return(NULL)
    }
  }
    
  # 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]  
  
  # Check if weights is provided
  if(length(weights) > 0){
    # Check if weights has any labels or names
    if(is.null(names(weights)))
      stop("The weights object must have names or labels that ",
           "match the labels of input_score\n")

    # Make sure its labels or names match the
    # the labels of input_score 
    weights <- as.numeric(weights[names(input_score)])
  }

  # Get the alternative hypothesis testing method
  alt_int <- switch(alternative, two.sided=0L, less=1L, greater=-1L, 1L)

  # Compute the ks statistic and p-value per row in the matrix
  ks <- .Call(ks_genescore_mat_, FS, weights, alt_int)

  # Obtain score statistics from KS method
  # Change values of 0 to the machine lowest value to avoid taking -log(0)
  stat <- ks[1,]

  # Obtain p-values from KS method
  # 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 scores according to the provided metric
  scores <- ifelse(rep(metric, nrow(FS)) %in% "pval", -log(pval), 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
  weights = NULL,                           # If weights is provided, perform a weighted 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 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
CaDrA::meta_plot(topn_best_list = custom_topn_best_meta)


# Evaluate results across top N features you started from
CaDrA::topn_plot(custom_topn_l)

For validation purposes, compare the custom and built-in function.

topn_res <- CaDrA::candidate_search(
  FS = sim_FS,
  input_score = sim_Scores,
  method = "ks_pval",          # Use Kolmogorov-Smirnov 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 7 starting points for each search
  max_size = 10,               # Maximum size a meta-feature matrix can extend to
  do_plot = FALSE,             # Plot after finding the best features
  best_score_only = FALSE      # Return all results from the search
)

## Fetch the meta-feature set corresponding to its best scores over top N features searches
topn_best_meta <- topn_best(topn_res)

# Visualize the best results with the meta-feature plot
meta_plot(topn_best_list = topn_best_meta)


# Evaluate results across top N features you started from
topn_plot(topn_res)

SessionInfo

sessionInfo()
R Under development (unstable) (2024-10-21 r87258)
Platform: x86_64-pc-linux-gnu
Running under: Ubuntu 24.04.1 LTS

Matrix products: default
BLAS:   /home/biocbuild/bbs-3.21-bioc/R/lib/libRblas.so 
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.12.0

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_GB              LC_COLLATE=C              
 [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_1.5.0                 pheatmap_1.0.12            
 [3] SummarizedExperiment_1.37.0 Biobase_2.67.0             
 [5] GenomicRanges_1.59.0        GenomeInfoDb_1.43.0        
 [7] IRanges_2.41.0              S4Vectors_0.45.0           
 [9] BiocGenerics_0.53.0         MatrixGenerics_1.19.0      
[11] matrixStats_1.4.1           testthat_3.2.1.1           
[13] devtools_2.4.5              usethis_3.0.0              

loaded via a namespace (and not attached):
 [1] bitops_1.0-9            tcltk_4.5.0             remotes_2.5.0          
 [4] rlang_1.1.4             magrittr_2.0.3          compiler_4.5.0         
 [7] vctrs_0.6.5             reshape2_1.4.4          stringr_1.5.1          
[10] profvis_0.4.0           pkgconfig_2.0.3         crayon_1.5.3           
[13] fastmap_1.2.0           XVector_0.47.0          ellipsis_0.3.2         
[16] labeling_0.4.3          caTools_1.18.3          utf8_1.2.4             
[19] promises_1.3.0          rmarkdown_2.28          sessioninfo_1.2.2      
[22] UCSC.utils_1.3.0        purrr_1.0.2             xfun_0.48              
[25] zlibbioc_1.53.0         cachem_1.1.0            jsonlite_1.8.9         
[28] highr_0.11              later_1.3.2             DelayedArray_0.33.0    
[31] parallel_4.5.0          R6_2.5.1                RColorBrewer_1.1-3     
[34] bslib_0.8.0             stringi_1.8.4           pkgload_1.4.0          
[37] brio_1.1.5              jquerylib_0.1.4         Rcpp_1.0.13            
[40] iterators_1.0.14        knitr_1.48              R.utils_2.12.3         
[43] httpuv_1.6.15           Matrix_1.7-1            R.cache_0.16.0         
[46] tidyselect_1.2.1        rstudioapi_0.17.1       abind_1.4-8            
[49] yaml_2.3.10             doParallel_1.0.17       gplots_3.2.0           
[52] codetools_0.2-20        miniUI_0.1.1.1          misc3d_0.9-1           
[55] pkgbuild_1.4.5          lattice_0.22-6          tibble_3.2.1           
[58] plyr_1.8.9              shiny_1.9.1             withr_3.0.2            
[61] evaluate_1.0.1          desc_1.4.3              urlchecker_1.0.1       
[64] pillar_1.9.0            KernSmooth_2.23-24      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_1.8.0              ppcor_1.1              
[76] tools_4.5.0             fs_1.6.4                grid_4.5.0             
[79] knnmi_1.0               colorspace_2.1-1        GenomeInfoDbData_1.2.13
[82] cli_3.6.3               fansi_1.0.6             S4Arrays_1.7.0         
[85] dplyr_1.1.4             gtable_0.3.6            R.methodsS3_1.8.2      
[88] sass_0.4.9              digest_0.6.37           SparseArray_1.7.0      
[91] farver_2.1.2            htmlwidgets_1.6.4       memoise_2.0.1          
[94] htmltools_0.5.8.1       R.oo_1.26.0             lifecycle_1.0.4        
[97] httr_1.4.7              mime_0.12               MASS_7.3-61

Scoring Functions