panda: Passing Messages between Biological Networks to Refine...
In pandaR: PANDA Algorithm

Description Usage Arguments Value References Examples

This function runs the PANDA algorithm

panda(motif, expr = NULL, ppi = NULL, alpha = 0.1, 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")

`motif`	A motif dataset, a data.frame, matrix or exprSet containing 3 columns. Each row describes an motif associated with a transcription factor (column 1) a gene (column 2) and a score (column 3) for the motif.
`expr`	An expression dataset, as a genes (rows) by samples (columns) data.frame
`ppi`	A Protein-Protein interaction dataset, a data.frame containing 3 columns. Each row describes a protein-protein interaction between transcription factor 1(column 1), transcription factor 2 (column 2) and a score (column 3) for the interaction.
`alpha`	value to be used for update variable, alpha (default=0.1)
`hamming`	value at which to terminate the process based on hamming distance (default 10^-3)
`iter`	sets the maximum number of iterations PANDA 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 TFs 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'.

An object of class "panda" containing matrices describing networks achieved by convergence with PANDA algorithm.
"regNet" is the regulatory network
"coregNet" is the coregulatory network
"coopNet" is the cooperative network

Glass K, Huttenhower C, Quackenbush J, Yuan GC. Passing Messages Between Biological Networks to Refine Predicted Interactions. PLoS One. 2013 May 318(5):e64832.

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data(pandaToyData)
pandaRes <- panda(pandaToyData$motif,
           pandaToyData$expression,pandaToyData$ppi,hamming=.1,progress=TRUE)

Loading required package: Biobase
Loading required package: BiocGenerics
Loading required package: parallel

Attaching package: 'BiocGenerics'

The following objects are masked from 'package:parallel':

    clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
    clusterExport, clusterMap, parApply, parCapply, parLapply,
    parLapplyLB, parRapply, parSapply, parSapplyLB

The following objects are masked from 'package:stats':

    IQR, mad, sd, var, xtabs

The following objects are masked from 'package:base':

    Filter, Find, Map, Position, Reduce, anyDuplicated, append,
    as.data.frame, basename, cbind, colMeans, colSums, colnames,
    dirname, do.call, duplicated, eval, evalq, get, grep, grepl,
    intersect, is.unsorted, lapply, lengths, mapply, match, mget,
    order, paste, pmax, pmax.int, pmin, pmin.int, rank, rbind,
    rowMeans, rowSums, rownames, sapply, setdiff, sort, table, tapply,
    union, unique, unsplit, which, which.max, which.min

Welcome to Bioconductor

    Vignettes contain introductory material; view with
    'browseVignettes()'. To cite Bioconductor, see
    'citation("Biobase")', and for packages 'citation("pkgname")'.

[1] "Initializing and validating"
[1] "Verified sufficient samples"
[1] "Normalizing networks..."
[1] "Learning Network..."
[1] "Using tanimoto similarity"
Iteration0: hamming distance =0.71897
Iteration1: hamming distance =0.38993
Iteration2: hamming distance =0.40237
Iteration3: hamming distance =0.40052
Iteration4: hamming distance =0.38904
Iteration5: hamming distance =0.37051
Iteration6: hamming distance =0.34681
Iteration7: hamming distance =0.31972
Iteration8: hamming distance =0.29081
Iteration9: hamming distance =0.26141
Iteration10: hamming distance =0.23257
Iteration11: hamming distance =0.20505
Iteration12: hamming distance =0.17937
Iteration13: hamming distance =0.15583
Iteration14: hamming distance =0.13456
Iteration15: hamming distance =0.11559
Iteration16: hamming distance =0.09882
Successfully ran PANDA on 1000 Genes and 87 TFs.
Time elapsed:4.12seconds.