Result for 180C90FC7D299BF12EA45426E69042861A3363F2

Query result

Key Value
FileName./usr/lib64/R/library/graphsim/R/graphsim.rdb
FileSize63037
MD5BAFCF4FA52A3563C85F6FEDA6F2F5969
SHA-1180C90FC7D299BF12EA45426E69042861A3363F2
SHA-256CA43C7A6801A13FCA5DB40F096D679147117743068232E44D4C5EDC2FC4888F8
SSDEEP1536:z8Ky94K3XaAWoFIitX9HxdWvgr/YDnbGthcVQp/VQpE:ntvoeItRdxYWth8QDQa
TLSHT14F5302FD77BCB16605437C6DA803E31FB96410A82B915BE424A2A24DCE6CF282F557D4
hashlookup:parent-total1
hashlookup:trust55

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Parents (Total: 1)

The searched file hash is included in 1 parent files which include package known and seen by metalookup. A sample is included below:

Key Value
MD5528B8D301D6C7AF1A876C709477EA9F9
PackageArchx86_64
PackageDescriptionFunctions to develop simulated continuous data (e.g., gene expression) from a sigma covariance matrix derived from a graph structure in 'igraph' objects. Intended to extend 'mvtnorm' to take 'igraph' structures rather than sigma matrices as input. This allows the use of simulated data that correctly accounts for pathway relationships and correlations. This allows the use of simulated data that correctly accounts for pathway relationships and correlations. Here we present a versatile statistical framework to simulate correlated gene expression data from biological pathways, by sampling from a multivariate normal distribution derived from a graph structure. This package allows the simulation of biological pathways from a graph structure based on a statistical model of gene expression. For example methods to infer biological pathways and gene regulatory networks from gene expression data can be tested on simulated datasets using this framework. This also allows for pathway structures to be considered as a confounding variable when simulating gene expression data to test the performance of genomic analyses.
PackageNameR-graphsim
PackageRelease1.17
PackageVersion1.0.2
SHA-19B95E441C9AB215A57F7BE2E84077CF00303BD8F
SHA-2568F79DD9369C6571176CBAD81C216263CA8BF9CE6F075DE8D12314D49B81D896D