,
Katia Bulekova [aut] ,
Vinay Kartha [aut],
Stefano Monti [aut] | Title: | Candidate Driver Analysis |
|---|---|
| Description: | Performs both stepwise and backward heuristic search for candidate (epi)genetic drivers based on a binary multi-omics dataset. CaDrA's main objective is to identify features which, together, are significantly skewed or enriched pertaining to a given vector of continuous scores (e.g. sample-specific scores representing a phenotypic readout of interest, such as protein expression, pathway activity, etc.), based on the union occurence (i.e. logical OR) of the events. |
| Authors: | Reina Chau [aut, cre] ,
Katia Bulekova [aut] ,
Vinay Kartha [aut],
Stefano Monti [aut] |
| Maintainer: | Reina Chau <[email protected]> |
| License: | GPL-3 + file LICENSE |
| Version: | 1.5.0 |
| Built: | 2024-11-29 04:26:11 UTC |
| Source: | https://github.com/bioc/CaDrA |
A SummarizedExperiment object consists of 16,873 genomic features across 951 samples.
data(BRCA_GISTIC_MUT_SIG)data(BRCA_GISTIC_MUT_SIG)
An object of class SummarizedExperiment from
SummarizedExperiment package
containing an assay of 16,873 rows (features) and 951 columns (samples)
see SummarizedExperiment for more details.
a SummarizedExperiment object
Kartha VK, Kern JG, Sebastiani P, Zhang L, Varelas X, Monti S (2019) CaDrA: A computational framework for performing candidate driver analyses using binary genomic features. (Frontiers in Genetics)
Perform permutation-based testings on a sample of permuted input scores
using candidate_search as the main iterative function for each run.
CaDrA( FS, input_score, method = c("ks_pval", "ks_score", "wilcox_pval", "wilcox_score", "revealer", "knnmi", "correlation", "custom"), method_alternative = c("less", "greater", "two.sided"), cmethod = c("spearman", "pearson"), custom_function = NULL, custom_parameters = NULL, weights = NULL, search_start = NULL, top_N = 1, search_method = c("both", "forward"), max_size = 7, n_perm = 1000, perm_alternative = c("one.sided", "two.sided"), obs_best_score = NULL, smooth = TRUE, plot = FALSE, ncores = 1, cache = FALSE, cache_path = NULL, verbose = FALSE )CaDrA( FS, input_score, method = c("ks_pval", "ks_score", "wilcox_pval", "wilcox_score", "revealer", "knnmi", "correlation", "custom"), method_alternative = c("less", "greater", "two.sided"), cmethod = c("spearman", "pearson"), custom_function = NULL, custom_parameters = NULL, weights = NULL, search_start = NULL, top_N = 1, search_method = c("both", "forward"), max_size = 7, n_perm = 1000, perm_alternative = c("one.sided", "two.sided"), obs_best_score = NULL, smooth = TRUE, plot = FALSE, ncores = 1, cache = FALSE, cache_path = NULL, verbose = FALSE )
FS |
a matrix of binary features or a SummarizedExperiment class object from SummarizedExperiment package where rows represent features of interest (e.g. genes, transcripts, exons, etc...) and columns represent the samples. The assay of FS contains binary (1/0) values indicating the presence/absence of omics features. |
input_score |
a vector of continuous scores representing a phenotypic readout of interest such as protein expression, pathway activity, etc. NOTE: |
method |
a character string specifies a scoring method that is
used in the search. There are 6 options: ( |
method_alternative |
a character string specifies an alternative
hypothesis testing ( NOTE: This argument only applies to |
cmethod |
correlation method to use - spearman or pearson. Default is "spearman". NOTE: This argument only applies to |
custom_function |
If method is NOTE: |
custom_parameters |
If method is |
weights |
If method is NOTE: |
search_start |
a vector of character strings (separated by commas)
specifies feature names in the FS object to start the search with.
If |
top_N |
an integer specifies the number of features to start the search over. By default, it starts with the feature that has the highest best score (top_N = 1). NOTE: If |
search_method |
a character string specifies an algorithm to filter out
the best candidates ( |
max_size |
an integer specifies a maximum size that a meta-feature can
extend to do for a given search. Default is |
n_perm |
an integer specifies the number of permutations to perform.
Default is |
perm_alternative |
an alternative hypothesis type for calculating
permutation-based p-value. Options: one.sided, two.sided. Default is
|
obs_best_score |
a numeric value corresponding to the best observed
score. This value is used to compare against the |
smooth |
a logical value indicates whether or not to add a smoothing
factor of 1 to the calculation of permutation-based p-value. This option is
used to avoid a returned p-value of 0. Default is |
plot |
a logical value indicates whether or not to plot the empirical
null distribution of the permuted best scores. Default is |
ncores |
an integer specifies the number of cores to perform
parallelization for permutation-based testing. Default is |
cache |
a logical value determines whether or not to cache the
permuted best scores. This helps to save time for future loading instead
of re-computing the permutation-based testing every time.
Default is |
cache_path |
a path to cache permuted best scores. Default is |
verbose |
a logical value indicates whether or not to print the
diagnostic messages. Default is |
a list of 4 objects: key, perm_best_scores,
obs_best_score, perm_pval
-key: a list of parameters that was used to cache the
results of the permutation-based testing. This is useful as the
permuted best scores can be recycled to save time for future loading.
-perm_best_scores: a vector of permuted best scores obtained
by performing candidate_search over n_perm iterations of
permuted input scores.
-obs_best_score: a user-provided best score or an observed best score
obtained by performing candidate_search on a given dataset and input
parameters. This value is later used to compare against the permuted best
scores (perm_best_scores).
perm_pval: a permutation-based p-value obtained by calculating
sum(perm_best_scores > obs_best_score)/n_perm
NOTE: If smooth = TRUE, a smoothing factor of 1 will be added to the
calculation of perm_pval.
e.g. (sum(perm_best_scores > obs_best_score) + 1) / (n_perm + c)
This is just to not return a p-value of 0
# Load pre-computed feature set data(sim_FS) # Load pre-computed input-score data(sim_Scores) # Set seed for permutation set.seed(21) # Define additional parameters and start the search function cadra_result <- CaDrA( FS = sim_FS, input_score = sim_Scores, method = "ks_pval", weights = NULL, method_alternative = "less", top_N = 1, search_start = NULL, search_method = "both", max_size = 7, n_perm = 10, perm_alternative = "one.sided", plot = FALSE, smooth = TRUE, obs_best_score = NULL, ncores = 1, cache = FALSE, cache_path = NULL )# Load pre-computed feature set data(sim_FS) # Load pre-computed input-score data(sim_Scores) # Set seed for permutation set.seed(21) # Define additional parameters and start the search function cadra_result <- CaDrA( FS = sim_FS, input_score = sim_Scores, method = "ks_pval", weights = NULL, method_alternative = "less", top_N = 1, search_start = NULL, search_method = "both", max_size = 7, n_perm = 10, perm_alternative = "one.sided", plot = FALSE, smooth = TRUE, obs_best_score = NULL, ncores = 1, cache = FALSE, cache_path = NULL )
Calculate row-wise scores of a given binary feature set based on a given scoring method
calc_rowscore( FS, input_score, meta_feature = NULL, method = c("ks_pval", "ks_score", "wilcox_pval", "wilcox_score", "revealer", "knnmi", "correlation", "custom"), method_alternative = c("less", "greater", "two.sided"), cmethod = c("spearman", "pearson"), custom_function = NULL, custom_parameters = NULL, weights = NULL, do_check = TRUE, verbose = FALSE, ... )calc_rowscore( FS, input_score, meta_feature = NULL, method = c("ks_pval", "ks_score", "wilcox_pval", "wilcox_score", "revealer", "knnmi", "correlation", "custom"), method_alternative = c("less", "greater", "two.sided"), cmethod = c("spearman", "pearson"), custom_function = NULL, custom_parameters = NULL, weights = NULL, do_check = TRUE, verbose = FALSE, ... )
FS |
a matrix of binary features or a SummarizedExperiment class object from SummarizedExperiment package where rows represent features of interest (e.g. genes, transcripts, exons, etc...) and columns represent the samples. The assay of FS contains binary (1/0) values indicating the presence/absence of omics features. |
input_score |
a vector of continuous scores representing a phenotypic readout of interest such as protein expression, pathway activity, etc. NOTE: |
meta_feature |
a vector of one or more features representing known
causes of activation or features associated with a response of interest
( |
method |
a character string specifies a scoring method that is
used in the search. There are 6 options: ( |
method_alternative |
a character string specifies an alternative
hypothesis testing ( NOTE: This argument only applies to |
cmethod |
correlation method to use - spearman or pearson. Default is "spearman"
#' NOTE: This argument only applies to |
custom_function |
if method is NOTE: |
custom_parameters |
if method is |
weights |
If method is NOTE: |
do_check |
a logical value indicates whether or not to validate if the
given parameters ( |
verbose |
a logical value indicates whether or not to print the
diagnostic messages. Default is |
... |
additional parameters to be passed to |
return a vector of row-wise positive scores where it is ordered from
most significant to least significant (e.g. from highest to lowest values)
and its labels or names must match the row names of FS object
# Create a feature matrix mat <- matrix(c(1,0,1,0,0,0,0,0,1,0, 0,0,1,0,1,0,1,0,0,0, 0,0,0,0,1,0,1,0,1,0), nrow=3) colnames(mat) <- 1:10 row.names(mat) <- c("TP_1", "TP_2", "TP_3") # Create a vector of observed input scores set.seed(42) input_score = rnorm(n = ncol(mat)) names(input_score) <- colnames(mat) # Run the ks method ks_rowscore_result <- calc_rowscore( FS = mat, input_score = input_score, meta_feature = NULL, method = "ks_pval", method_alternative = "less", weights = NULL ) # Run the wilcoxon method wilcox_rowscore_result <- calc_rowscore( FS = mat, input_score = input_score, meta_feature = NULL, method = "wilcox_pval", method_alternative = "less" ) # Run the revealer method revealer_rowscore_result <- calc_rowscore( FS = mat, input_score = input_score, meta_feature = NULL, method = "revealer" ) # Run the revealer method knnmi_rowscore_result <- calc_rowscore( FS = mat, input_score = input_score, meta_feature = NULL, method = "knnmi" ) # Run the correlation method corr_result <- calc_rowscore( FS = mat, input_score = input_score, meta_feature = NULL, method = "correlation", cmethod = "spearman" ) # A customized function using ks-test function customized_ks_rowscore <- function(FS, input_score, meta_feature=NULL, alternative="less"){ # 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,]) } # 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 scores <- -log(pval) names(scores) <- rownames(FS) return(scores) } # Search for best features using a custom-defined function custom_rowscore_result <- calc_rowscore( FS = mat, input_score = input_score, meta_feature = NULL, method = "custom", custom_function = customized_ks_rowscore, custom_parameters = NULL )# Create a feature matrix mat <- matrix(c(1,0,1,0,0,0,0,0,1,0, 0,0,1,0,1,0,1,0,0,0, 0,0,0,0,1,0,1,0,1,0), nrow=3) colnames(mat) <- 1:10 row.names(mat) <- c("TP_1", "TP_2", "TP_3") # Create a vector of observed input scores set.seed(42) input_score = rnorm(n = ncol(mat)) names(input_score) <- colnames(mat) # Run the ks method ks_rowscore_result <- calc_rowscore( FS = mat, input_score = input_score, meta_feature = NULL, method = "ks_pval", method_alternative = "less", weights = NULL ) # Run the wilcoxon method wilcox_rowscore_result <- calc_rowscore( FS = mat, input_score = input_score, meta_feature = NULL, method = "wilcox_pval", method_alternative = "less" ) # Run the revealer method revealer_rowscore_result <- calc_rowscore( FS = mat, input_score = input_score, meta_feature = NULL, method = "revealer" ) # Run the revealer method knnmi_rowscore_result <- calc_rowscore( FS = mat, input_score = input_score, meta_feature = NULL, method = "knnmi" ) # Run the correlation method corr_result <- calc_rowscore( FS = mat, input_score = input_score, meta_feature = NULL, method = "correlation", cmethod = "spearman" ) # A customized function using ks-test function customized_ks_rowscore <- function(FS, input_score, meta_feature=NULL, alternative="less"){ # 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,]) } # 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 scores <- -log(pval) names(scores) <- rownames(FS) return(scores) } # Search for best features using a custom-defined function custom_rowscore_result <- calc_rowscore( FS = mat, input_score = input_score, meta_feature = NULL, method = "custom", custom_function = customized_ks_rowscore, custom_parameters = NULL )
Performs heuristic search on a set of binary features to determine whether
there are features whose union is more skewed (enriched at the extremes)
than either features alone. This is the main functionality of
the CaDrA package.
candidate_search( FS, input_score, method = c("ks_pval", "ks_score", "wilcox_pval", "wilcox_score", "revealer", "knnmi", "correlation", "custom"), method_alternative = c("less", "greater", "two.sided"), cmethod = c("spearman", "pearson"), custom_function = NULL, custom_parameters = NULL, weights = NULL, search_start = NULL, top_N = 1, search_method = c("both", "forward"), max_size = 7, best_score_only = FALSE, do_plot = FALSE, verbose = FALSE )candidate_search( FS, input_score, method = c("ks_pval", "ks_score", "wilcox_pval", "wilcox_score", "revealer", "knnmi", "correlation", "custom"), method_alternative = c("less", "greater", "two.sided"), cmethod = c("spearman", "pearson"), custom_function = NULL, custom_parameters = NULL, weights = NULL, search_start = NULL, top_N = 1, search_method = c("both", "forward"), max_size = 7, best_score_only = FALSE, do_plot = FALSE, verbose = FALSE )
FS |
a matrix of binary features or a SummarizedExperiment class object from SummarizedExperiment package where rows represent features of interest (e.g. genes, transcripts, exons, etc...) and columns represent the samples. The assay of FS contains binary (1/0) values indicating the presence/absence of omics features. |
input_score |
a vector of continuous scores representing a phenotypic readout of interest such as protein expression, pathway activity, etc. NOTE: |
method |
a character string specifies a scoring method that is
used in the search. There are 7 options: ( |
method_alternative |
a character string specifies an alternative
hypothesis testing ( NOTE: This argument only applies to |
cmethod |
correlation method to use - spearman or pearson. Default is "spearman". NOTE: This argument only applies to |
custom_function |
if method is NOTE: |
custom_parameters |
if method is |
weights |
if method is NOTE: |
search_start |
a vector of character strings (separated by commas)
specifies feature names in the |
top_N |
an integer specifies the number of features to start the search over. By default, it starts with the feature that has the highest best score (top_N = 1). NOTE: If |
search_method |
a character string specifies an algorithm to filter
out the best features ( |
max_size |
an integer specifies a maximum size that a meta-feature
can extend to do for a given search. Default is |
best_score_only |
a logical value indicates whether or not to return
the best score corresponding to each top N searches only.
Default is |
do_plot |
a logical value indicates whether or not to plot the overlapping features of the resulting meta-feature matrix. NOTE: plot can only be produced if the resulting meta-feature matrix contains
more than 1 feature (e.g. length(search_start) > 1 or top_N > 1).
Default is |
verbose |
a logical value indicates whether or not to print the
diagnostic messages. Default is |
NOTE: The legacy function topn_eval is equivalent to the recommended
candidate_search function
If best_score_only = TRUE, the heuristic search will return
the best feature whose its union meta-feature matrix has the highest score
among the top_N feature searches.
If best_score_only = FALSE, a list of objects pertaining to
top_N feature searches will be returned. For each top_N feature search,
the candidate search will contain 7 objects: (1) its best meta-feature matrix
(feature_set), (2) its observed input scores (input_score),
(3) its corresponding best score pertaining to the union meta-feature
matrix (score), (4) names of the best meta-features (best_features),
(5) rank of the best meta-features in term of their best scores (best indices),
(6) marginal scores of the best meta-features (marginal_best_scores),
(7) cumulative scores of the best meta-features (cumulative_best_scores).
# Load pre-computed feature set data(sim_FS) # Load pre-computed input scores data(sim_Scores) # Define additional parameters and run the function candidate_search_result <- candidate_search( FS = sim_FS, input_score = sim_Scores, method = "ks_pval", method_alternative = "less", weights = NULL, search_start = NULL, top_N = 3, search_method = "both", max_size = 7, best_score_only = FALSE )# Load pre-computed feature set data(sim_FS) # Load pre-computed input scores data(sim_Scores) # Define additional parameters and run the function candidate_search_result <- candidate_search( FS = sim_FS, input_score = sim_Scores, method = "ks_pval", method_alternative = "less", weights = NULL, search_start = NULL, top_N = 3, search_method = "both", max_size = 7, best_score_only = FALSE )
A SummarizedExperiment object consists of 17,724 genomic features across 82 samples.
data(CCLE_MUT_SCNA)data(CCLE_MUT_SCNA)
An object of class SummarizedExperiment from
SummarizedExperiment package
containing a matrix of 17,724 rows (features) and 82 columns (samples).
See SummarizedExperiment for more details.
a SummarizedExperiment object
Kim, J., Botvinnik, O., Abudayyeh, O. et al. Characterizing genomic alterations in cancer by complementary functional associations. Nat Biotechnol 34, 539–546 (2016). https://doi.org/10.1038/nbt.3527
A vector of continuous scores represents the activation of B-catenin across multiple cancer cell lines
data(CTNBB1_reporter)data(CTNBB1_reporter)
Consists of a vector of continuous scores of B-catenin
activity across 82 cancer cell lines. The mutation and copy number
associated with this sample cohorts can be found in
CCLE_MUT_SCNA dataset.
a vector of continuous scores
Kim, J., Botvinnik, O., Abudayyeh, O. et al. Characterizing genomic alterations in cancer by complementary functional associations. Nat Biotechnol 34, 539–546 (2016). https://doi.org/10.1038/nbt.3527
Produces a random permutation score matrix given a vector of sample-specific scores representing a phenotypic readout of interest such as protein expression, pathway activity, etc.
generate_permutations(input_score, n_perm)generate_permutations(input_score, n_perm)
input_score |
a vector of continuous scores of a molecular phenotype of
interest such as protein expression, pathway activity, etc.
NOTE: The |
n_perm |
a number of permutations to generate. This determines the number of rows in the permutation matrix. |
a matrix of values where each row contains scores of a single
permuted input_score.
# Load pre-simulated scores data(sim_Scores) # Set seed for permutation set.seed(123) # Define number of permutations n_perm = 1000 # Generate permuted scores perm_matrix <- generate_permutations( input_score = sim_Scores, n_perm = n_perm )# Load pre-simulated scores data(sim_Scores) # Set seed for permutation set.seed(123) # Define number of permutations n_perm = 1000 # Generate permuted scores perm_matrix <- generate_permutations( input_score = sim_Scores, n_perm = n_perm )
By utilizing the top N results obtained from candidate_search,
we can find the best meta-feature among the top N searches using
topn_best. meta_plot is then used to produce graphics
including a tile plot for the top meta-features that associated with
a molecular phenotype of interest (e.g. input_score),
the KS enrichment plot of the meta-features,
and lastly, a density diagram of the distribution of the observed
input scores sorted from largest to smallest at the top.
meta_plot(topn_best_list, input_score_label = NULL, plot_title = NULL)meta_plot(topn_best_list, input_score_label = NULL, plot_title = NULL)
topn_best_list |
a list of objects returned from |
input_score_label |
a label that references to the |
plot_title |
a title to the plot. Default is |
3 plots stacked on top of each other: 1. a density diagram of observed input scores sorted from highest to lowest 2. a tile plot of the top features within the meta-feature set 3. a KS enrichment plot of the meta-feature set (this correspond to the logical OR of the features)
# Load pre-computed Top-N list generated for sim_FS dataset data(topn_list) # With the results obtained from top-N evaluation, # We can find the combination of features that gives the best score in # top N searches topn_best_meta <- topn_best(topn_list = topn_list) # Now we can plot this set of best meta-feature meta_plot(topn_best_list = topn_best_meta)# Load pre-computed Top-N list generated for sim_FS dataset data(topn_list) # With the results obtained from top-N evaluation, # We can find the combination of features that gives the best score in # top N searches topn_best_meta <- topn_best(topn_list = topn_list) # Now we can plot this set of best meta-feature meta_plot(topn_best_list = topn_best_meta)
sim_FS)The permutation result returned from CaDrA
using pre-simulated dataset
(FS = sim_FS), pre-simulated input scores
(input_score = sim_Scores),
top_N = 7, method = "ks_pval", alternative = "less",
search_method = "both", max_size = 10,
obs_best_score = NULL and n_perm = 1000
as inputs to the function.
data(perm_res)data(perm_res)
A list of objects returned from CaDrA function. The resulting
object contains a list of key parameters that was used to run the
permutation-based testing, a vector of permuted best scores for
a given n_perm, an observed best score, and a permuted p-value.
To visualize the Empirical Null Distribution of the permuted best scores over
n_perm iterations, just pass the resulting list to permutation_plot.
See permutation_plot for more details.
a list of objects returned from CaDrA function
Kartha VK, Kern JG, Sebastiani P, Zhang L, Varelas X, Monti S (2017) CaDrA: A computational framework for performing candidate driver analyses using binary genomic features. (Frontiers in Genetics)
# Load the pre-computed permutation results for sim_FS data(perm_res) # Plot the Empirical Null Distribution of the permuted best scores # against its observed best score permutation_plot(perm_res = perm_res)# Load the pre-computed permutation results for sim_FS data(perm_res) # Plot the Empirical Null Distribution of the permuted best scores # against its observed best score permutation_plot(perm_res = perm_res)
Plot the Empirical Null Distribution of Permutation Best Scores returned from
CaDrA function
permutation_plot(perm_res)permutation_plot(perm_res)
perm_res |
a list of objects returned from |
a density plot
# Load pre-computed permutation results data(perm_res) # Plot the permutation results permutation_plot(perm_res)# Load pre-computed permutation results data(perm_res) # Plot the permutation results permutation_plot(perm_res)
Pre-filter a dataset prior running candidate_search to avoid
testing features that are too prevalent or too sparse across samples in
the dataset
prefilter_data(FS, max_cutoff = 0.6, min_cutoff = 0.03, verbose = FALSE)prefilter_data(FS, max_cutoff = 0.6, min_cutoff = 0.03, verbose = FALSE)
FS |
a matrix of binary features or a SummarizedExperiment class object from SummarizedExperiment package where rows represent features of interest (e.g. genes, transcripts, exons, etc...) and columns represent the samples. The assay of FS contains binary (1/0) values indicating the presence/absence of ‘omics’ features. |
max_cutoff |
a numeric value between 0 and 1 describing the absolute prevalence of a feature across all samples in the FS object which the feature will be filtered out. Default is 0.6 (feature that occur in 60 percent or more of the samples will be removed) |
min_cutoff |
a numeric value between 0 and 1 describing the absolute prevalence of a feature across all samples in the FS object which the feature will be filtered out. Default is 0.03 (feature that occur in 3 percent or less of the samples will be removed) |
verbose |
a logical value indicates whether or not to print the
diagnostic messages. Default is |
A SummarizedExperiment object with only the filtered-in features given the filtered thresholds
# Load pre-computed feature set data(sim_FS) # Filter out features having < 3% and > 60% prevalence across all samples # by (default) sim_FS_filt1 <- prefilter_data(FS = sim_FS) # Change the min cut-off to 1% prevalence, instead of the default of 3% sim_FS_filt2 <- prefilter_data(FS = sim_FS, min_cutoff = 0.01) # Change the max cut-off to 65% prevalence, instead of the default of 60% sim_FS_filt3 <- prefilter_data(FS = sim_FS, max_cutoff = 0.65)# Load pre-computed feature set data(sim_FS) # Filter out features having < 3% and > 60% prevalence across all samples # by (default) sim_FS_filt1 <- prefilter_data(FS = sim_FS) # Change the min cut-off to 1% prevalence, instead of the default of 3% sim_FS_filt2 <- prefilter_data(FS = sim_FS, min_cutoff = 0.01) # Change the max cut-off to 65% prevalence, instead of the default of 60% sim_FS_filt3 <- prefilter_data(FS = sim_FS, max_cutoff = 0.65)
A simulated SummarizedExperiment object that comprises of 1000 genomic features (rows) and 100 sample profiles (columns). Each row is represented by a vector of binary values (1/0) indicating the presence/absence of the feature in the samples. This simulated data includes 10 left-skewed (i.e. True Positive or TP) and 990 uniformly-distributed (i.e. True Null or TN) features.
data(sim_FS)data(sim_FS)
An object of class SummarizedExperiment from
SummarizedExperiment package containing
an assay of 1000 rows (features)
and 100 columns (samples). Each row is represented by
a vector of binary values (1/0)
indicating the presence/absence of the feature in the samples.
See ?SummarizedExperiment for more details.
a SummarizedExperiment object
Kartha VK, Kern JG, Sebastiani P, Zhang L, Varelas X, Monti S (2019) CaDrA: A computational framework for performing candidate driver analyses using binary genomic features. (Frontiers in Genetics)
A simulated vector of continuous scores generated from
rnorm(n=ncol(sim_FS), mean=0, sd=1) with set.seed(123)
based on the number of samples in the
simulated dataset (sim_FS)
data(sim_Scores)data(sim_Scores)
A vector of continuous scores randomly generated
from rnorm(n=ncol(sim_FS), mean=0, sd=1) with set.seed(123)
based on the number of samples in the
simulated dataset (sim_FS)
a vector of continuous scores
Kartha VK, Kern JG, Sebastiani P, Zhang L, Varelas X, Monti S (2019) CaDrA: A computational framework for performing candidate driver analyses using binary genomic features. (Frontiers in Genetics)
A vector of continuous scores represents oncogenic YAP/TAZ activity in human breast carcinomas
data(TAZYAP_BRCA_ACTIVITY)data(TAZYAP_BRCA_ACTIVITY)
consists of a vector of continuous scores of YAP/TAZ activity
across 951 profiles. The mutation and copy number associated with this sample
cohorts can be found in BRCA_GISTIC_MUT_SIG dataset.
a vector of continuous scores
Kartha VK, Kern JG, Sebastiani P, Zhang L, Varelas X, Monti S (2019) CaDrA: A computational framework for performing candidate driver analyses using binary genomic features. (Frontiers in Genetics)
Take the resulting list of meta-features returned from
candidate_search over top N feature searches and
fetch the meta-feature with the best score.
topn_best(topn_list)topn_best(topn_list)
topn_list |
A nested list of objects that are returned
from |
A list of objects containing the best meta-feature matrix, its corresponding best score, its observed input scores, rank of best meta-features based on their scores, its marginal and cumulative best scores.
# Load pre-computed Top-N list generated for sim_FS dataset data(topn_list) # Get the best meta-features list topn_best_meta <- topn_best(topn_list = topn_list)# Load pre-computed Top-N list generated for sim_FS dataset data(topn_list) # Get the best meta-features list topn_best_meta <- topn_best(topn_list = topn_list)
sim_FS)A list of objects returned from candidate_search
using simulated dataset FS = sim_FS, input_score = sim_Scores,
top_N = 7, method = "ks_pval", alternative = "less",
search_method = "both", max_size = 10, and
best_score_only = FALSE.
data(topn_list)data(topn_list)
A list of objects returned from candidate_search including
a set of best meta-feature matrix, its corresponding best score, its
observed input scores, rank of the best features based on their scores,
marginal best scores, and cumulative best scores. pertaining to each
top N feature searches.
See candidate_search for more information.
NOTE: max_size is set to 10 as we would like to account
for the presence of 10 left-skewed (i.e. true positive or TP) features
in sim_FS dataset.
a list of objects returned from candidate_search function
Kartha VK, Kern JG, Sebastiani P, Zhang L, Varelas X, Monti S (2019) CaDrA: A computational framework for performing candidate driver analyses using binary genomic features. (Frontiers in Genetics)
# Load pre-computed Top-N list generated for sim_FS and sim_Scores dataset data(topn_list) # Fetch the first meta-feature topn_list[[1]]$feature_set # Fetch the second meta-feature topn_list[[2]]$feature_set # Retrieve the meta-feature with the best score among top_N = 7 runs topn_best_meta <- topn_best(topn_list = topn_list) # Visualize the best meta-feature using meta_plot function meta_plot(topn_best_list = topn_best_meta) # Visualize overlap of meta-features across top_N = 7 # using topn_plot function topn_plot(topn_list = topn_list)# Load pre-computed Top-N list generated for sim_FS and sim_Scores dataset data(topn_list) # Fetch the first meta-feature topn_list[[1]]$feature_set # Fetch the second meta-feature topn_list[[2]]$feature_set # Retrieve the meta-feature with the best score among top_N = 7 runs topn_best_meta <- topn_best(topn_list = topn_list) # Visualize the best meta-feature using meta_plot function meta_plot(topn_best_list = topn_best_meta) # Visualize overlap of meta-features across top_N = 7 # using topn_plot function topn_plot(topn_list = topn_list)
Generate a heatmap representation of overlapping meta-features across
top N feature searches using candidate_search function
topn_plot(topn_list)topn_plot(topn_list)
topn_list |
a list of objects obtained from |
a heatmap of overlapping meta-features across top N feature searches
# Load pre-computed Top-N list generated for sim_FS dataset data(topn_list) # Get the overlapping top N plot topn_plot(topn_list = topn_list)# Load pre-computed Top-N list generated for sim_FS dataset data(topn_list) # Get the overlapping top N plot topn_plot(topn_list = topn_list)