PRODIGY_cohort: PRODIGY_cohort

In Shamir-Lab/PRODIGY: Personalized prioritization of driver genes

PRODIGY_cohort

Description

This is a wrapper that runs PRODIGY for a cohort of patients. It could take a long time to finish a group of patints, hence it is advised to output PRODIGY's results into files using the write_results parameter. It is also advised to use as much cores as possible using the num_of_cores parameter

Usage

PRODIGY_cohort(
  snv_matrix = NULL,
  expression_matrix,
  network = NULL,
  samples = NULL,
  DEGs = NULL,
  alpha = 0.05,
  pathway_list = NULL,
  num_of_cores = 1,
  sample_origins = NULL,
  write_results = F,
  results_folder = "./",
  beta = 2,
  gamma = 0.05,
  delta = 0.05,
  mutation_list = NULL
)

Arguments

snv_matrix	A binary matrix with genes in rows and patients on columns. 1=mutation. All genes must be contained in the global PPI network.
expression_matrix	A read count matrix with genes in rows and patients on columns. All genes must be contained in the global PPI network.
network	The global PPI network. Columns describe the source protein, destination protein and interaction score respectively. The network is considered as undirected.
DEGs	Named list of differentially expressed genes for every sample. All genes must be contained in the global PPI network.
alpha	The penalty exponent.
pathway_list	A list where each object is a 3 column data.table (src,dest,weight). Names correspond to pathway names
num_of_cores	The number of CPU cores to be used by the influence scores calculation step.
sample_origins	A vector that contains two optional values ("tumor","normal") corresponds to the tissues from which each column in expression_matrix was derived. This vector is utilized for differential expression analysis. If no vector is specified, the sample names of expression_matrix are assumed to be in TCGA format where last two digits correspond to sample type: "01"= solid tumor and "11"= normal.
write_results	Should the results be written to text files?
results_folder	Location for resulting influence matrices storage (if write_results = T)
beta	Minimal fold-change threshold for declering gene as differentially expressed by DESeq (default = 0.2)
gamma	FDR threshold for declering gene as differentially expressed by DESeq (default = 0.05)
delta	FDR threshold for declering a pathway as statistically enriched for differentially expressed genes (default = 0.05)
mutation_list	An alternative to snv_matrix, one can simply provide a list of named vectors with genes to be analyzed by PRODIGY
sample	The sample labels as appears in the SNV and expression matrices.

Value

A list of influence scores matrices.

References

Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 1-21 (2014). Gabriele Sales, Enrica Calura and Chiara Romualdi, graphite: GRAPH Interaction from pathway Topological Environment (2017).

Examples

data(COAD_SNV)
data(COAD_Expression)
# Load STRING network data 
data(STRING_network)
network = STRING_network
# Take samples for which SNV and expression is available 
samples = intersect(colnames(expression_matrix),colnames(snv_matrix))[1:5]
# Get differentially expressed genes (DEGs) for all samples
expression_matrix = expression_matrix[which(rownames(expression_matrix) %in% unique(c(network[,1],network[,2]))),]
DEGs = get_DEGs(expression_matrix,samples,sample_origins=NULL)
# Identify sample origins (tumor or normal)
sample_origins = rep("tumor",ncol(expression_matrix))
sample_origins[substr(colnames(expression_matrix),nchar(colnames(expression_matrix)[1])-1,nchar(colnames(expression_matrix)[1]))=="11"] = "normal"	
# Run PRODIGY
all_patients_scores = PRODIGY_cohort(snv_matrix,expression_matrix,network=network,samples=samples,DEGs=DEGs,alpha=0.05,pathwayDB="reactome",num_of_cores=1,sample_origins=sample_origins,
write_results = F, results_folder = "./",beta=2,gama=0.05,delta=0.05)

Shamir-Lab/PRODIGY documentation built on March 27, 2022, 5:29 p.m.