puma: PANDA using microRNA associations

In netZoo/netZooR: Unified methods for the inference and analysis of gene regulatory networks

PANDA using microRNA associations

Description

This function runs the PUMA algorithm to predict a miRNA-gene regulatory network

Usage

puma(
  motif,
  expr = NULL,
  ppi = NULL,
  alpha = 0.1,
  mir_file,
  hamming = 0.001,
  iter = NA,
  output = c("regulatory", "coexpression", "cooperative"),
  zScale = TRUE,
  progress = FALSE,
  randomize = c("None", "within.gene", "by.gene"),
  cor.method = "pearson",
  scale.by.present = FALSE,
  edgelist = FALSE,
  remove.missing.ppi = FALSE,
  remove.missing.motif = FALSE,
  remove.missing.genes = FALSE,
  mode = "union"
)

Arguments

motif	A miRNA target dataset, a data.frame, matrix or exprSet containing 3 columns. Each row describes the association between a miRNA (column 1) its target gene (column 2) and a score (column 3) for the association from TargetScan or miRanda
expr	An expression dataset, as a genes (rows) by samples (columns) data.frame
ppi	This can be set to 1) NULL which will be encoded as an identity matrix between miRNAs in PUMA for now. Or 2) it can include a set of TF interactions, or 3) a mix of TFs and miRNAs.
alpha	value to be used for update variable, alpha (default=0.1)
mir_file	list of miRNA to filter the PPI matrix and prevent update of miRNA edges.
hamming	value at which to terminate the process based on hamming distance (default 10^-3)
iter	sets the maximum number of iterations PUMA can run before exiting.
output	a vector containing which networks to return. Options include "regulatory", "coregulatory", "cooperative".
zScale	Boolean to indicate use of z-scores in output. False will use [0,1] scale.
progress	Boolean to indicate printing of output for algorithm progress.
randomize	method by which to randomize gene expression matrix. Default "None". Must be one of "None", "within.gene", "by.genes". "within.gene" randomization scrambles each row of the gene expression matrix, "by.gene" scrambles gene labels.
cor.method	Correlation method, default is "pearson".
scale.by.present	Boolean to indicate scaling of correlations by percentage of positive samples.
edgelist	Boolean to indicate if edge lists instead of matrices should be returned.
remove.missing.ppi	Boolean to indicate whether miRNAs in the PPI but not in the motif data should be removed. Only when mode=='legacy'.
remove.missing.motif	Boolean to indicate whether genes targeted in the motif data but not the expression data should be removed. Only when mode=='legacy'.
remove.missing.genes	Boolean to indicate whether genes in the expression data but lacking information from the motif prior should be removed. Only when mode=='legacy'.
mode	The data alignment mode. The mode 'union' takes the union of the genes in the expression matrix and the motif and the union of TFs in the ppi and motif and fills the matrics with zeros for nonintersecting TFs and gens, 'intersection' takes the intersection of genes and TFs and removes nonintersecting sets, 'legacy' is the old behavior with version 1.19.3. #' Parameters remove.missing.ppi, remove.missingmotif, remove.missing.genes work only with mode=='legacy'.

Value

An object of class "panda" containing matrices describing networks achieved by convergence with PUMA algorithm.
"regNet" is the regulatory network
"coregNet" is the coregulatory network
"coopNet" is the cooperative network which is not updated for miRNAs

References

Kuijjer, Marieke L., et al. "PUMA: PANDA using microRNA associations." Bioinformatics 36.18 (2020): 4765-4773.

Examples

data(pandaToyData)
mirs = c("AHR","AR","ARID3A","ARNT","BRCA1","CEBPA","CREB1","DDIT3")
pumaRes <- puma(pandaToyData$motif,
           pandaToyData$expression,NULL,mir_file=mirs,hamming=.1,progress=TRUE)

netZoo/netZooR documentation built on Oct. 16, 2024, 10:23 p.m.