Package 'AMARETTO'

Title: Regulatory Network Inference and Driver Gene Evaluation using Integrative Multi-Omics Analysis and Penalized Regression
Description: Integrating an increasing number of available multi-omics cancer data remains one of the main challenges to improve our understanding of cancer. One of the main challenges is using multi-omics data for identifying novel cancer driver genes. We have developed an algorithm, called AMARETTO, that integrates copy number, DNA methylation and gene expression data to identify a set of driver genes by analyzing cancer samples and connects them to clusters of co-expressed genes, which we define as modules. We applied AMARETTO in a pancancer setting to identify cancer driver genes and their modules on multiple cancer sites. AMARETTO captures modules enriched in angiogenesis, cell cycle and EMT, and modules that accurately predict survival and molecular subtypes. This allows AMARETTO to identify novel cancer driver genes directing canonical cancer pathways.
Authors: Jayendra Shinde, Celine Everaert, Shaimaa Bakr, Mohsen Nabian, Jishu Xu, Vincent Carey, Nathalie Pochet and Olivier Gevaert
Maintainer: Olivier Gevaert <[email protected]>
License: Apache License (== 2.0) + file LICENSE
Version: 1.23.0
Built: 2024-11-30 05:20:54 UTC
Source: https://github.com/bioc/AMARETTO

Help Index

AMARETTO_CreateModuleData

Description

AMARETTO_CreateModuleData

Usage

AMARETTO_CreateModuleData(AMARETTOinit, AMARETTOresults)

Arguments

AMARETTOinit

List output from AMARETTO_Initialize().
AMARETTOresults

List output from AMARETTO_Run()

Value

result

Examples

data('ProcessedDataLIHC')
AMARETTOinit <- AMARETTO_Initialize(ProcessedData = ProcessedDataLIHC,
                                    NrModules = 2, VarPercentage = 50)
AMARETTOresults <- AMARETTO_Run(AMARETTOinit)
AMARETTO_MD <- AMARETTO_CreateModuleData(AMARETTOinit, AMARETTOresults)

AMARETTO_CreateRegulatorPrograms

Description

AMARETTO_CreateRegulatorPrograms

Usage

AMARETTO_CreateRegulatorPrograms(AMARETTOinit, AMARETTOresults)

Arguments

AMARETTOinit

List output from AMARETTO_Initialize().
AMARETTOresults

List output from AMARETTO_Run()

Value

result

Examples

data('ProcessedDataLIHC')
AMARETTOinit <- AMARETTO_Initialize(ProcessedData = ProcessedDataLIHC,
                                    NrModules = 2, VarPercentage = 50)
AMARETTOresults <- AMARETTO_Run(AMARETTOinit)
AMARETTO_RP <- AMARETTO_CreateRegulatorPrograms(AMARETTOinit,AMARETTOresults)

AMARETTO_Download

Description

Downloading TCGA dataset for AMARETTO analysis

Usage

AMARETTO_Download(CancerSite = "CHOL",
  TargetDirectory = TargetDirectory)

Arguments

CancerSite

TCGA cancer code for data download
TargetDirectory

Directory path to download data

Value

result

Examples

TargetDirectory <- file.path(getwd(),"Downloads/");dir.create(TargetDirectory)
CancerSite <- 'CHOL'
DataSetDirectories <- AMARETTO_Download(CancerSite,TargetDirectory = TargetDirectory)

AMARETTO_EvaluateTestSet

Description

Code to evaluate AMARETTO on a new gene expression test set. Uses output from AMARETTO_Run() and CreateRegulatorData().

Usage

AMARETTO_EvaluateTestSet(AMARETTOresults = AMARETTOresults,
  MA_Data_TestSet = MA_Data_TestSet,
  RegulatorData_TestSet = RegulatorData_TestSet)

Arguments

AMARETTOresults

AMARETTO output from AMARETTO_Run().
MA_Data_TestSet

Gene expression matrix from a test set (that was not used in AMARETTO_Run()).
RegulatorData_TestSet

Test regulator data from CreateRegulatorData().

Value

result

Examples

data('ProcessedDataLIHC')
AMARETTOinit <- AMARETTO_Initialize(ProcessedData = ProcessedDataLIHC,
                                    NrModules = 2, VarPercentage = 50)

AMARETTOresults <- AMARETTO_Run(AMARETTOinit)
AMARETTOtestReport <- AMARETTO_EvaluateTestSet(AMARETTOresults = AMARETTOresults,
                                               MA_Data_TestSet = AMARETTOinit$MA_matrix_Var,
                                               RegulatorData_TestSet = AMARETTOinit$RegulatorData)

AMARETTO_ExportResults

Description

Retrieve a download of all the data linked with the run (including heatmaps)

Usage

AMARETTO_ExportResults(AMARETTOinit, AMARETTOresults, data_address,
  Heatmaps = TRUE, CNV_matrix = NULL, MET_matrix = NULL)

Arguments

AMARETTOinit

AMARETTO initialize output
AMARETTOresults

AMARETTO results output
data_address

Directory to save data folder
Heatmaps

Output heatmaps as pdf
CNV_matrix

CNV_matrix
MET_matrix

MET_matrix

Value

result

Examples

data('ProcessedDataLIHC')
TargetDirectory <- file.path(getwd(),"Downloads/");dir.create(TargetDirectory)
AMARETTOinit <- AMARETTO_Initialize(ProcessedData = ProcessedDataLIHC,
                                    NrModules = 2, VarPercentage = 50)

AMARETTOresults <- AMARETTO_Run(AMARETTOinit)
AMARETTO_ExportResults(AMARETTOinit,AMARETTOresults,TargetDirectory,Heatmaps = FALSE)

AMARETTO_HTMLreport

Description

Retrieve an interactive html report, including gene set enrichment analysis if asked for.

Usage

AMARETTO_HTMLreport(AMARETTOinit, AMARETTOresults, ProcessedData,
  show_row_names = FALSE, SAMPLE_annotation = NULL, ID = NULL,
  hyper_geo_test_bool = FALSE, hyper_geo_reference = NULL,
  output_address = "./", MSIGDB = TRUE, driverGSEA = TRUE,
  phenotype_association_table = NULL)

Arguments

AMARETTOinit

AMARETTO initialize output
AMARETTOresults

AMARETTO results output
ProcessedData

List of processed input data
show_row_names

if True, sample names will appear in the heatmap
SAMPLE_annotation

SAMPLE annotation will be added to heatmap
ID

ID column of the SAMPLE annotation data frame
hyper_geo_test_bool

Boolean if a hyper geometric test needs to be performed. If TRUE provide a GMT file in the hyper_geo_reference parameter.
hyper_geo_reference

GMT file with gene sets to compare with.
output_address

Output directory for the html files.
MSIGDB

TRUE if gene sets were retrieved from MSIGDB. Links will be created in the report.
driverGSEA

if TRUE, module drivers will also be included in the hypergeometric test.
phenotype_association_table

a Data Frame, containing all modules phenotype association data. Optional.

Value

result

Examples

## Not run: 
data('ProcessedDataLIHC')
AMARETTOinit <- AMARETTO_Initialize(ProcessedData = ProcessedDataLIHC,
                                    NrModules = 2, VarPercentage = 50)

AMARETTOresults <- AMARETTO_Run(AMARETTOinit)

AMARETTO_HTMLreport(AMARETTOinit= AMARETTOinit,AMARETTOresults= AMARETTOresults,
                    ProcessedData = ProcessedDataLIHC,
                    hyper_geo_test_bool=FALSE,
                    output_address='./')

## End(Not run)

AMARETTO_Initialize (version: reorder and filter MA_Matrix)

Description

Code used to initialize the seed clusters for an AMARETTO run. Requires processed gene expressiosn (rna-seq or microarray), CNV (usually from a GISTIC run), and methylation (from MethylMix, provided in this package) data. Uses the function CreateRegulatorData() and results are fed into the function AMARETTO_Run().

Usage

AMARETTO_Initialize(ProcessedData = ProcessedData, Driver_list = NULL,
  NrModules, VarPercentage, PvalueThreshold = 0.001,
  RsquareThreshold = 0.1, pmax = 10, NrCores = 1, OneRunStop = 0,
  method = "union", random_seeds = NULL, convergence_cutoff = 0.01)

Arguments

ProcessedData

List of Expression, CNV and MethylMix data matrices, with genes in rows and samples in columns.
Driver_list

Custom list of driver genes to be considered in analysis
NrModules

How many gene co-expression modules should AMARETTO search for? Usually around 100 is acceptable, given the large number of possible driver-passenger gene combinations.
VarPercentage

Minimum percentage by variance for filtering of genes; for example, 75% would indicate that the CreateRegulatorData() function only analyses genes that have a variance above the 75th percentile across all samples.
PvalueThreshold

Threshold used to find relevant driver genes with CNV alterations: maximal p-value.
RsquareThreshold

Threshold used to find relevant driver genes with CNV alterations: minimal R-square value between CNV and gene expression data.
pmax

'pmax' variable for glmnet function from glmnet package; the maximum number of variables aver to be nonzero. Should not be changed by user unless she/he fully understands the AMARETTO algorithm and how its parameters choices affect model output.
NrCores

A numeric variable indicating the number of computer/server cores to use for paralellelization. Default is 1, i.e. no parallelization. Please check your computer or server's computing capacities before increasing this number. Parallelization is done via the RParallel package. Mac vs. Windows environments may behave differently when using parallelization.
OneRunStop

OneRunStop
method

Perform union or intersection of the driver genes evaluated from the input data matrices and custom driver gene list provided.
random_seeds

A numeric vector of length 2, containing two seed numbers for randomization : 1st for kmeans and 2nd for glmnet
convergence_cutoff

A numeric value (E.g. 0.01) representing the fraction of the total number of genes, in which, The algorithm is considered reaching convergence and will stop, if Nr of Gene-replacements in an iteration falls below this threshold * total number of genes.

Value

result

Examples

data('ProcessedDataLIHC')
data('Driver_Genes')
AMARETTOinit <- AMARETTO_Initialize(ProcessedData = ProcessedDataLIHC,
                                    NrModules = 2, VarPercentage = 50)
## Not run: 
AMARETTOinit <- AMARETTO_Initialize(ProcessedData = ProcessedDataLIHC,
                                    Driver_list = Driver_Genes[['MSigDB']],
                                    NrModules = 2, VarPercentage = 50)

## End(Not run)

AMARETTO_Preprocess

Description

Wrapper code that analyzes process TCGA GISTIC (CNV) and gene expression (rna-seq or microarray) data via one call

Usage

AMARETTO_Preprocess(DataSetDirectories = DataSetDirectories,
  BatchData = BatchData)

Arguments

DataSetDirectories

DataSetDirectories
BatchData

BatchData

Value

result

Examples

## Not run: 
TargetDirectory <-  "Downloads"  # path to data download directory
CancerSite <- 'CHOL'
DataSetDirectories <- AMARETTO_Download(CancerSite,TargetDirectory)
ProcessedData <- AMARETTO_Preprocess(DataSetDirectories,BatchData)

## End(Not run)

AMARETTO_Run Function to run AMARETTO, a statistical algorithm to identify cancer drivers by integrating a variety of omics data from cancer and normal tissue.

Description

AMARETTO_Run Function to run AMARETTO, a statistical algorithm to identify cancer drivers by integrating a variety of omics data from cancer and normal tissue.

Usage

AMARETTO_Run(AMARETTOinit)

Arguments

AMARETTOinit

List output from AMARETTO_Initialize().

Value

result

Examples

data('ProcessedDataLIHC')
AMARETTOinit <- AMARETTO_Initialize(ProcessedData = ProcessedDataLIHC,
                                    NrModules = 2, VarPercentage = 50)
AMARETTOresults <- AMARETTO_Run(AMARETTOinit)

AMARETTO_VisualizeModule

Description

Function to visualize the gene modules

Usage

AMARETTO_VisualizeModule(AMARETTOinit, AMARETTOresults, ProcessedData,
  ModuleNr, show_row_names = FALSE, SAMPLE_annotation = NULL,
  ID = NULL, order_samples = NULL)

Arguments

AMARETTOinit

List output from AMARETTO_Initialize().
AMARETTOresults

List output from AMARETTO_Run().
ProcessedData

List of processed input data
ModuleNr

Module number to visualize
show_row_names

If TRUE, row names will be shown on the plot.
SAMPLE_annotation

Matrix or Dataframe with sample annotation
ID

Column used as sample name
order_samples

Order samples in heatmap by mean or by clustering

Value

result

Examples

data('ProcessedDataLIHC')
AMARETTOinit <- AMARETTO_Initialize(ProcessedData = ProcessedDataLIHC,
                                    NrModules = 2, VarPercentage = 50)

AMARETTOresults <- AMARETTO_Run(AMARETTOinit)

AMARETTO_VisualizeModule(AMARETTOinit = AMARETTOinit,AMARETTOresults = AMARETTOresults,
                         ProcessedData = ProcessedDataLIHC, ModuleNr = 1)

BatchData

Description

A dataset for conducting batch corerction in TCGA samples

Usage

BatchData

Format

A data frame with 23263 observations and 3 variables:

Source

AMARETTO

Driver_Genes

Description

A list of cancer driver genes described in literature.

Usage

Driver_Genes

Format

List

Source

AMARETTO

MsigdbMapping

Description

A dataset containing all MSIGDB pathways and their descriptions. .

Usage

MsigdbMapping

Format

List

Source

AMARETTO

Title plot_run_history

Description

Title plot_run_history

Usage

plot_run_history(AMARETTOinit, AMARETTOresults)

Arguments

AMARETTOinit

AMARETTO initialize output
AMARETTOresults

AMARETTO results output

Value

plot

Examples

data('ProcessedDataLIHC')
AMARETTOinit <- AMARETTO_Initialize(ProcessedData = ProcessedDataLIHC,
                                    NrModules = 2, VarPercentage = 50)

AMARETTOresults <- AMARETTO_Run(AMARETTOinit)

plot_run_history(AMARETTOinit,AMARETTOresults)

ProcessedDataLIHC

Description

A list of dataframes of processed toy example dataset from TCGA-LIHC.

Usage

ProcessedDataLIHC

Format

List

Source

AMARETTO

read_gct

Description

Function to turn a .gct data files into a matrix format

Usage

read_gct(file_address)

Arguments

file_address

Address of the input gct file.

Value

result

Examples

data_matrix<-read_gct(file_address="")