Extracts the gene expression matrix from GEO DataSets as a AffyBatch object. Additionally, can make the normalization process using two different methods (vsn and rma). The summarization (pass from multi-probe to one gene) uses two different criteria (Maximum value and Median of the samples expression data) and the process of gene differentially expressed analysis using two methods (sam and acde). The construction of the co-expression network can be conduced using two different methods, Pearson Correlation Coefficient (PCC) or Mutual Information (MI) and choosing a threshold value using a graph theory approach.
To install coexnet from GitHub you need devtools package:
# Install devtools
install.packages("devtools")
# Install coexnet
devtools::install_github("gibbslab/coexnet")
library(coexnet)
| Name | Description |
|---|---|
| CCP | Obtain the Common Connection patterns for two or more compared networks |
| cof.var | Calculate the coefficient of variation to expression matrix. |
| create.net | Create a co-expression network from expression matrix. |
| dif.exprs | Differential expression analysis using two different methods. |
| expr.mat | Calculate the expression matrix from the raw expression data. |
| find.threshold | Find the threshold value to create a co-expression network. |
| gene.symbol | Create a table relating probesets with genes. |
| get.affy | Charge and create an AffyBatch object |
| get.info | Download raw expression data from GEO DataSet |
| ppi.net | Create a protein-protein interaction network |
| shared.components | Obtain the shared components for two or more compared networks |
Juan Henao, Liliana Lopez-Kleine Andres Pinzon-Velasco (2016). coexnet: An R package to build CO-EXpression NETworks from Microarray Data (Version 0.1) [software] Available at https://github.com/gibbslab/coexnet