Parents (Total: 4)
The searched file hash is included in 4 parent files which include package known and seen by metalookup. A sample is included below:
| Key |
Value |
| MD5 | 32C7DAF4C7EAA60583D37A75770CAE20 |
| PackageArch | x86_64 |
| PackageDescription | Functions 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. |
| PackageName | R-graphsim |
| PackageRelease | lp152.1.3 |
| PackageVersion | 1.0.2 |
| SHA-1 | DDEACC9479C4F423DA61A8D098CEDB0256EF180A |
| SHA-256 | DAF154FC3FD7CF18733CBDB1BFAB5A3B47E39615C26A27BA291DDB2BE587C148 |
| Key |
Value |
| MD5 | 0C2439F973AB6D7D9984044D9286F1B4 |
| PackageArch | x86_64 |
| PackageDescription | Functions 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. |
| PackageName | R-graphsim |
| PackageRelease | lp153.1.2 |
| PackageVersion | 1.0.2 |
| SHA-1 | 849B5CAD637A371B66D037F41D88B3123B9307E3 |
| SHA-256 | 2FECEEAFE5441BB50D67ADDD47B1495F613340D694E04E8C6B5C8EFFBC9B75E9 |
| Key |
Value |
| MD5 | 528B8D301D6C7AF1A876C709477EA9F9 |
| PackageArch | x86_64 |
| PackageDescription | Functions 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. |
| PackageName | R-graphsim |
| PackageRelease | 1.17 |
| PackageVersion | 1.0.2 |
| SHA-1 | 9B95E441C9AB215A57F7BE2E84077CF00303BD8F |
| SHA-256 | 8F79DD9369C6571176CBAD81C216263CA8BF9CE6F075DE8D12314D49B81D896D |
| Key |
Value |
| MD5 | 2DC1A03C990D83BF25F4AEC278DCB9E9 |
| PackageArch | x86_64 |
| PackageDescription | Functions 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. |
| PackageName | R-graphsim |
| PackageRelease | lp154.1.1 |
| PackageVersion | 1.0.2 |
| SHA-1 | 070A1D138D8B2EC9EAFFA7E4686506A4F6F96ADD |
| SHA-256 | 57E9A2DA5C777D3A70FC9A0C253E2FBA35ECF3EE8A8609EDC2C4ED115754EBAF |