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README.Rmd
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README.Rmd
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---
output: rmarkdown::github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, include=FALSE, echo=FALSE, message=FALSE, warning=FALSE}
knitr::opts_chunk$set(fig.path="./man/figures/", message=FALSE, collapse = TRUE, comment="")
# Load SummarizedExperiment
library(SummarizedExperiment)
# Load CaDrA
library(devtools)
load_all()
```
# 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:
i) 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.
ii) 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/](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 load.data}
## 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 cadra}
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 visualize.best}
## 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 summarize}
# 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).