#### Docs update 1.19

Ahmed Mohamed authored on 01/03/2019 06:31:42
Showing1 changed files
 ... ... @@ -4,8 +4,8 @@ 4 4  \alias{samplePaths} 5 5  \title{Creates a set of sample path p-values for each length given a weighted network} 6 6  \usage{ 7 -samplePaths(graph, max.path.length, num.samples = 1000, num.warmup = 10, 8 - verbose = TRUE) 7 +samplePaths(graph, max.path.length, num.samples = 1000, 8 + num.warmup = 10, verbose = TRUE) 9 9  } 10 10  \arguments{ 11 11  \item{graph}{A weighted igraph object. Weights must be in \code{edge.weights} or \code{weight} ... ... @@ -23,7 +23,7 @@ edge attributes.} 23 23  A matrix where each row is a path length and each column is the number of paths sampled. 24 24  } 25 25  \description{ 26 -Randomly traverses paths of increasing lengths within a set network to create an  26 +Randomly traverses paths of increasing lengths within a set network to create an 27 27  empirical pathway distribution for more accurate determination of path significance. 28 28  } 29 29  \details{ ... ... @@ -39,26 +39,26 @@ Can take a bit of time. 39 39  # Calculate Pearson's correlation. 40 40  data(ex_microarray) # Part of ALL dataset. 41 41  rgraph <- assignEdgeWeights(microarray = ex_microarray, graph = rgraph, 42 - weight.method = "cor", use.attr="miriam.uniprot",  42 + weight.method = "cor", use.attr="miriam.uniprot", 43 43  y=factor(colnames(ex_microarray)), bootstrap = FALSE) 44 -  44 + 45 45  ## Get significantly correlated paths using "p-valvue" method. 46 - ## First, establish path score distribution by calling "samplePaths"  46 + ## First, establish path score distribution by calling "samplePaths" 47 47  pathsample <- samplePaths(rgraph, max.path.length=10, 48 48  num.samples=100, num.warmup=10) 49 -  50 - ## Get all significant paths with p<0.1  51 - significant.p <- pathRanker(rgraph, method = "pvalue",  49 + 50 + ## Get all significant paths with p<0.1 51 + significant.p <- pathRanker(rgraph, method = "pvalue", 52 52  sampledpaths = pathsample ,alpha=0.1) 53 53   54 -} 55 -\author{ 56 -Timothy Hancock 57 - 58 -Ahmed Mohamed 59 54  } 60 55  \seealso{ 61 56  Other Path ranking methods: \code{\link{extractPathNetwork}}, 62 57  \code{\link{getPathsAsEIDs}}, \code{\link{pathRanker}} 63 58  } 59 +\author{ 60 +Timothy Hancock 64 61   62 +Ahmed Mohamed 63 +} 64 +\concept{Path ranking methods}

#### update docs using roxygen2

ahmohamed authored on 19/09/2016 02:16:33
Showing1 changed files
 ... ... @@ -1,4 +1,5 @@ 1 -% Generated by roxygen2 (4.0.1): do not edit by hand 1 +% Generated by roxygen2: do not edit by hand 2 +% Please edit documentation in R/pathRank.R 2 3  \name{samplePaths} 3 4  \alias{samplePaths} 4 5  \title{Creates a set of sample path p-values for each length given a weighted network} ... ... @@ -22,14 +23,14 @@ edge attributes.} 22 23  A matrix where each row is a path length and each column is the number of paths sampled. 23 24  } 24 25  \description{ 25 -Randomly traverses paths of increasing lengths within a set network to create an 26 +Randomly traverses paths of increasing lengths within a set network to create an  26 27  empirical pathway distribution for more accurate determination of path significance. 27 28  } 28 29  \details{ 29 30  Can take a bit of time. 30 31  } 31 32  \examples{ 32 -## Prepare a weighted reaction network. 33 + ## Prepare a weighted reaction network. 33 34  ## Conver a metabolic network to a reaction network. 34 35  data(ex_sbml) # bipartite metabolic network of Carbohydrate metabolism. 35 36  rgraph <- makeReactionNetwork(ex_sbml, simplify=TRUE) ... ... @@ -38,17 +39,18 @@ Can take a bit of time. 38 39  # Calculate Pearson's correlation. 39 40  data(ex_microarray) # Part of ALL dataset. 40 41  rgraph <- assignEdgeWeights(microarray = ex_microarray, graph = rgraph, 41 - weight.method = "cor", use.attr="miriam.uniprot", 42 + weight.method = "cor", use.attr="miriam.uniprot",  42 43  y=factor(colnames(ex_microarray)), bootstrap = FALSE) 43 - 44 +  44 45  ## Get significantly correlated paths using "p-valvue" method. 45 - ## First, establish path score distribution by calling "samplePaths" 46 + ## First, establish path score distribution by calling "samplePaths"  46 47  pathsample <- samplePaths(rgraph, max.path.length=10, 47 48  num.samples=100, num.warmup=10) 48 - 49 - ## Get all significant paths with p<0.1 50 - significant.p <- pathRanker(rgraph, method = "pvalue", 49 +  50 + ## Get all significant paths with p<0.1  51 + significant.p <- pathRanker(rgraph, method = "pvalue",  51 52  sampledpaths = pathsample ,alpha=0.1) 53 + 52 54  } 53 55  \author{ 54 56  Timothy Hancock ... ... @@ -56,7 +58,7 @@ Timothy Hancock 56 58  Ahmed Mohamed 57 59  } 58 60  \seealso{ 59 -Other Path ranking methods: \code{\link{extractPathNetwork}}; 60 - \code{\link{getPathsAsEIDs}}; \code{\link{pathRanker}} 61 +Other Path ranking methods: \code{\link{extractPathNetwork}}, 62 + \code{\link{getPathsAsEIDs}}, \code{\link{pathRanker}} 61 63  } 62 64 

#### empty commit

Ahmed Mohamed authored on 04/07/2015 00:36:26
Showing1 changed files
 1 1 old mode 100644 2 2 new mode 100755

#### Updates

added plotCytoscapeGML , Geneset export formats, bug fixes

amfy10 authored on 28/06/2014 15:36:04
Showing1 changed files
 ... ... @@ -1,3 +1,4 @@ 1 +% Generated by roxygen2 (4.0.1): do not edit by hand 1 2  \name{samplePaths} 2 3  \alias{samplePaths} 3 4  \title{Creates a set of sample path p-values for each length given a weighted network} ... ... @@ -6,26 +7,23 @@ samplePaths(graph, max.path.length, num.samples = 1000, num.warmup = 10, 6 7  verbose = TRUE) 7 8  } 8 9  \arguments{ 9 - \item{graph}{A weighted igraph object. Weights must be in 10 - \code{edge.weights} or \code{weight} edge attributes.} 10 +\item{graph}{A weighted igraph object. Weights must be in \code{edge.weights} or \code{weight} 11 +edge attributes.} 11 12   12 - \item{max.path.length}{The maxmimum path length.} 13 +\item{max.path.length}{The maxmimum path length.} 13 14   14 - \item{num.samples}{The numner of paths to sample} 15 +\item{num.samples}{The numner of paths to sample} 15 16   16 - \item{num.warmup}{The number of warm up paths to sample.} 17 +\item{num.warmup}{The number of warm up paths to sample.} 17 18   18 - \item{verbose}{Whether to display the progress of the 19 - function.} 19 +\item{verbose}{Whether to display the progress of the function.} 20 20  } 21 21  \value{ 22 -A matrix where each row is a path length and each column is 23 -the number of paths sampled. 22 +A matrix where each row is a path length and each column is the number of paths sampled. 24 23  } 25 24  \description{ 26 -Randomly traverses paths of increasing lengths within a set 27 -network to create an empirical pathway distribution for 28 -more accurate determination of path significance. 25 +Randomly traverses paths of increasing lengths within a set network to create an 26 +empirical pathway distribution for more accurate determination of path significance. 29 27  } 30 28  \details{ 31 29  Can take a bit of time. ... ... @@ -58,8 +56,7 @@ Timothy Hancock 58 56  Ahmed Mohamed 59 57  } 60 58  \seealso{ 61 -Other Path ranking methods: 62 -\code{\link{extractPathNetwork}}; 63 -\code{\link{getPathsAsEIDs}}; \code{\link{pathRanker}} 59 +Other Path ranking methods: \code{\link{extractPathNetwork}}; 60 + \code{\link{getPathsAsEIDs}}; \code{\link{pathRanker}} 64 61  } 65 62 

#### Release 0.99.0 Biocunductor submission

amfy10 authored on 10/03/2014 12:16:44
Showing1 changed files
 1 1 new file mode 100644 ... ... @@ -0,0 +1,65 @@ 1 +\name{samplePaths} 2 +\alias{samplePaths} 3 +\title{Creates a set of sample path p-values for each length given a weighted network} 4 +\usage{ 5 +samplePaths(graph, max.path.length, num.samples = 1000, num.warmup = 10, 6 + verbose = TRUE) 7 +} 8 +\arguments{ 9 + \item{graph}{A weighted igraph object. Weights must be in 10 + \code{edge.weights} or \code{weight} edge attributes.} 11 + 12 + \item{max.path.length}{The maxmimum path length.} 13 + 14 + \item{num.samples}{The numner of paths to sample} 15 + 16 + \item{num.warmup}{The number of warm up paths to sample.} 17 + 18 + \item{verbose}{Whether to display the progress of the 19 + function.} 20 +} 21 +\value{ 22 +A matrix where each row is a path length and each column is 23 +the number of paths sampled. 24 +} 25 +\description{ 26 +Randomly traverses paths of increasing lengths within a set 27 +network to create an empirical pathway distribution for 28 +more accurate determination of path significance. 29 +} 30 +\details{ 31 +Can take a bit of time. 32 +} 33 +\examples{ 34 +## Prepare a weighted reaction network. 35 + ## Conver a metabolic network to a reaction network. 36 + data(ex_sbml) # bipartite metabolic network of Carbohydrate metabolism. 37 + rgraph <- makeReactionNetwork(ex_sbml, simplify=TRUE) 38 + 39 + ## Assign edge weights based on Affymetrix attributes and microarray dataset. 40 + # Calculate Pearson's correlation. 41 + data(ex_microarray) # Part of ALL dataset. 42 + rgraph <- assignEdgeWeights(microarray = ex_microarray, graph = rgraph, 43 + weight.method = "cor", use.attr="miriam.uniprot", 44 + y=factor(colnames(ex_microarray)), bootstrap = FALSE) 45 + 46 + ## Get significantly correlated paths using "p-valvue" method. 47 + ## First, establish path score distribution by calling "samplePaths" 48 + pathsample <- samplePaths(rgraph, max.path.length=10, 49 + num.samples=100, num.warmup=10) 50 + 51 + ## Get all significant paths with p<0.1 52 + significant.p <- pathRanker(rgraph, method = "pvalue", 53 + sampledpaths = pathsample ,alpha=0.1) 54 +} 55 +\author{ 56 +Timothy Hancock 57 + 58 +Ahmed Mohamed 59 +} 60 +\seealso{ 61 +Other Path ranking methods: 62 +\code{\link{extractPathNetwork}}; 63 +\code{\link{getPathsAsEIDs}}; \code{\link{pathRanker}} 64 +} 65 +