Package 'DRaWR'

Title: Discriminative Random Walk with Restart
Description: We present DRaWR, a network-based method for ranking genes or properties related to a given gene set. Such related genes or properties are identified from among the nodes of a large, heterogeneous network of biological information. Our method involves a random walk with restarts, performed on an initial network with multiple node and edge types, preserving more of the original, specific property information than current methods that operate on homogeneous networks. In this first stage of our algorithm, we find the properties that are the most relevant to the given gene set and extract a subnetwork of the original network, comprising only the relevant properties. We then rerank genes by their similarity to the given gene set, based on a second random walk with restarts, performed on the above subnetwork.
Authors: Charles Blatti [aut, cre]
Maintainer: Charles Blatti <[email protected]>
License: GPL-2
Version: 1.0.3
Built: 2025-03-13 03:45:59 UTC
Source: https://github.com/cran/DRaWR

Help Index

DRaWR

Description

This function runs the DRaWR two stage random walk with restart method.

Usage

DRaWR(possetfile = "extdata/sample.setlist", unifile = "extdata/sample.uni",
  networkfile = "extdata/sample.edge", outdir = "output_",
  restarts = c(0.7), nfolds = 1, st2keep = 1, undirected = TRUE,
  unweighted = FALSE, normalize = "type", maxiters = 50, thresh = 1e-04,
  property_types = c("allen_brain_atlas", "chip_binding", "gene_ontology",
  "motif_u5", "pfam_domain", "T1", "T2"), writepreds = 0)

Arguments

possetfile

(string): location of file containing location of gene sets to test.
unifile

(string): location of file listing gene universe.
networkfile

(string): location of file containing network contents.
outdir

(string): prefix of location of file to write performance results (optionally prediction results).
restarts

(vector): vector of restart values to test. Default is c(0.7).
nfolds

(int): number of folds for cross validation, 1 is no cross-validation. Default is 4.
st2keep

(int): number of property nodes to keep in second stage for each property type. Default is 50.
undirected

(bool): boolean to make network undirected.
unweighted

(bool): boolean to make network unweighted.
normalize

(string): "type" or "none". Default is 'type'.
maxiters

(int): maximum number of allowable iterations. Default is 50.
thresh

(float): threshold for L1 norm convergence. Default is 0.001.
property_types

(vector): list of possible property types. Default is c("go_curated_evidence", "go_inferred_evidence", "pfam_domain").
writepreds

(boolean): write predictions out to a file. Default is FALSE

Examples

DRaWR(possetfile = system.file("extdata", "sample.setlist", package="DRaWR"),
	unifile = system.file("extdata", "sample.uni", package="DRaWR"),
	networkfile = system.file("extdata", "sample.edge", package="DRaWR"),
	outdir = "exampleRun_", restarts = c(.7), nfolds = 1, st2keep = 1,
	undirected = TRUE, unweighted = FALSE, normalize = "type", maxiters = 50,
	thresh = 0.0001, property_types = c("T1", "T2"), writepreds = 0)

RWR

Description

This function runs a random walk with restart using two supported matrix representations.

Usage

RWR(boolSparceMat, transmat, restart, query, startvec, maxiters, thresh)

Arguments

boolSparceMat

(bool): Boolean to indicate sparce Matrix or list matrix.
transmat

(sparce Matrix / list matrix): transition probabilities.
restart

(float): probability of restart.
query

(vector): probability of restarting at all nodes.
startvec

(vector): initial probability of being at any node.
maxiters

(int): maximum number of allowable iterations.
thresh

(float): threshold for L1 norm convergence.

Value

list of 'iter':number of iterations, 'diff': L1 norm of difference, 'vec': converged probability distribution vector.

Examples

RWR(boolSparceMat=TRUE, transmat=transmat, restart=.3, query=c(rep(0.1,10),rep(0,5)),
	startvec=rep(1/15,15), maxiters=10, thresh=0.001)

threeCol2listMat

Description

This function takes a three vectors of equal length (source nodes, target nodes, and edge weights) and return the adjacency matrix as a list of vectors.

Usage

threeCol2listMat(a = c("a", "b", "c", "c"), b = c("a", "b", "b", "b"),
  v = c(1, 2, 3, 4))

Arguments

a

(vector): vector of source node names.
b

(vector): vector of target node names.
v

(vector): vector of edge weights names.

Value

list of vectors matrix representation.

Examples

threeCol2listMat(a = c("a","b","c","c"), b = c("a","b","b","b"), v = c(1,2,3,4))

threeCol2MaxMat

Description

This function takes a three vectors of equal length (source nodes, target nodes, and edge weights) and return the adjacency matrix as a sparse Matrix.

Usage

threeCol2MaxMat(a = c("a", "b", "c", "c"), b = c("a", "b", "b", "b"),
  v = c(1, 2, 3, 4))

Arguments

a

(vector): vector of source node names.
b

(vector): vector of target node names.
v

(vector): vector of edge weights names.

Value

sparce Matrix.

Examples

threeCol2MaxMat(a = c("a","b","c","c"), b = c("a","b","b","b"), v = c(1,2,3,4))

Sample transition matrix.

Description

A Matrix containing the normalized transition matrix from the test network

Usage

transmat

Format

a Matrix containing the normalized transition matrix from the test network.