<!-- README.md is generated from README.Rmd. Please edit that file --> # CaDrA     **Ca**ndidate **Dr**ivers **A**nalysis: Multi-Omic Search for Candidate Drivers of Functional Signatures **CaDrA** is an R package that supports a heuristic search framework aimed at identifying candidate drivers of a molecular phenotype of interest. The main function takes two inputs: 1) A binary multi-omics dataset, which can be represented as a matrix of binary features or a **SummarizedExperiment** class object where the rows are 1/0 vectors indicating the presence/absence of ‘omics’ features (e.g. somatic mutations, copy number alterations, epigenetic marks, etc.), and the columns are the samples. 2) A molecular phenotype of interest which can be represented as a vector of continuous scores (e.g. protein expression, pathway activity, etc.) Based on these two inputs, **CaDrA** implements a forward and/or backward search algorithm to find a set of features that together is maximally associated with the observed input scores, based on one of several scoring functions (*Kolmogorov-Smirnov*, *Conditional Mutual Information*, *Wilcoxon*, or *custom-defined scoring function*), making it useful to find complementary omics features likely driving the input molecular phenotype. Please see our [documentation](https://montilab.github.io/CaDrA/) for additional examples. # Web Interface We developed an R Shiny Dashboard that would allow users to interact with **CaDrA** directly without the need to install or maintain the package. See our web portal at <https://cadra.bu.edu/> # Installation - Using `devtools` package ``` r library(devtools) devtools::install_github("montilab/CaDrA") ``` - Using `BiocManager` package ``` r # Install BiocManager if (!require("BiocManager", quietly = TRUE)) install.packages("BiocManager") # Install CaDrA BiocManager::install("CaDrA") # Install SummarizedExperiment BiocManager::install("SummarizedExperiment") ``` # Usage Here, we are using a dataset of somatic mutations and CNAs extracted from the TCGA Breast Cancer Dataset. We will query this Feature Set based on an Input Score that measures the per-sample activity of YAP/TAZ (two important regulators of the hippo pathway). This score represents the projection on the TCGA BrCa dataset of a gene expression signature of YAP/TAZ knockdown derived in breast cancer cell lines. Our question of interest: what is the combination of genetic features (mutations and copy number alterations) that best “explain” the YAP/TAZ activity? ## (i) Load R packages ``` r library(CaDrA) library(SummarizedExperiment) ``` ## (ii) Format and filter data inputs ``` r ## Read in BRCA GISTIC+Mutation object utils::data(BRCA_GISTIC_MUT_SIG) eset_mut_scna <- BRCA_GISTIC_MUT_SIG ## Read in input score utils::data(TAZYAP_BRCA_ACTIVITY) input_score <- TAZYAP_BRCA_ACTIVITY ## Samples to keep based on the overlap between the two inputs overlap <- base::intersect(base::names(input_score), base::colnames(eset_mut_scna)) eset_mut_scna <- eset_mut_scna[, overlap] input_score <- input_score[overlap] ## Binarize FS to only have 0's and 1's SummarizedExperiment::assay(eset_mut_scna)[SummarizedExperiment::assay(eset_mut_scna) > 1] <- 1.0 ## Pre-filter FS based on occurrence frequency eset_mut_scna_flt <- CaDrA::prefilter_data( FS = eset_mut_scna, max_cutoff = 0.6, # max event frequency (60%) min_cutoff = 0.03 # min event frequency (3%) ) ``` ## (iii) Run CaDrA Here, we repeat the candidate search starting from each of the top ‘N’ features and report the combined results as a heatmap (to summarize the number of times each feature is selected across repeated runs). **IMPORTANT NOTE**: The legacy function `topn_eval()` is equivalent to the new recommended `candidate_search()` function. ``` r topn_res <- CaDrA::candidate_search( FS = eset_mut_scna_flt, input_score = input_score, 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 = 7, # Evaluate top 7 starting points for each search max_size = 7, # 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 ) ``` ## (iv) Visualize the results ### Meta-feature plot This plot produces 3 graphics stacked on top of each other: 1. A density diagram of observed input scores sorted from highest to lowest 2. A tile plot for the top meta-features that associated with a molecular phenotype of interest (e.g. input_score) 3. A KS enrichment plot of the meta-feature set (this correspond to the logical OR of the features) ``` r ## Fetch the meta-feature set corresponding to its best scores over top N features searches topn_best_meta <- CaDrA::topn_best(topn_res) # Visualize the best results with the meta-feature plot CaDrA::meta_plot(topn_best_list = topn_best_meta, input_score_label = "YAP/TAZ Activity") ``` <!-- --> ### Top-N plot This plot is a heatmap of overlapping meta-features by repeating `candidate_search` over top N feature searches. ``` r # Evaluate results across top N features you started from CaDrA::topn_plot(topn_res) ``` <!-- --> # Additional Guides - [How to run CaDrA within a Docker environment](https://montilab.github.io/CaDrA/articles/docker.html) # Acknowledgements This project is funded in part by the [NIH/NIDCR](https://www.nidcr.nih.gov/) (3R01DE030350-01A1S1, R01DE031831), [Find the Cause Breast Cancer Foundation](https://findthecausebcf.org), and [NIH/NIA](https://www.nia.nih.gov/) (UH3 AG064704).