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COSINE (version 2.1)
COndition SpecIfic sub-NEtwork
Description
To identify the globally most discriminative subnetwork from gene expression profiles using an optimization model and genetic algorithm
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Version
Version
2.1
2.0
1.0
0.0
Install
install.packages('COSINE')
Monthly Downloads
34
Version
2.1
License
GPL (>= 2)
Maintainer
Haisu Ma
Last Published
July 10th, 2014
Functions in COSINE (2.1)
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diff_gen
Calculate the F-statistics and ECF-statistics
choose_lambda
Choose the most appropriate weight parameter lambda
DataSimu
Simulation of the six datasets and the case dataset
COSINE-package
COndition SpecIfic subNEtwork identification
PPI
The protein protein interaction network data
get_components_PPI
Get all the components (connected clusters) of the sub-network
f.test
To get the F-statistics for each gene
GA_search
Use genetic algorithm to search for the globally optimal subnetwork
Score_adjust_PPI
To adjust the score of the selected PPI sub-network using random sampling
set1_unscaled_diff
The unstandardized F-statistics and ECF-statistics of simulated dataset 1
score_scaling
To get the normalzied F-statistics and ECF-statistics
diff_gen_for3
Generate the F-statistics and ECF-statistics for the comparison of three datasets
get_quantiles
Get the five quantiles of the weight parameter lambda
random_network_sampling_PPI
To sample random sub-network from the PPI data
GA_search_PPI
Run genetic algorithm to search for the PPI sub-network
diff_gen_PPI
Generate the scaled node score and scaled edge score for nodes and edges in the background network
scaled_node_score
The scaled ECF-statistics of all the edges
simulated_data
The simulated data sets used in the paper
scaled_edge_score
The scaled ECF statistics of all the edges
get_quantiles_PPI
Get the five quantile values of lambda for analysis of gene expression and PPI network data
cond.fyx
Compute the ECF-statistics measuring the differential correlation of gene pairs
set1_GA
Result of genetic algorithm search for simulated data set1
set1_scaled_diff
The standardized F-statistics and ECF-statistics for the comparison between simulated data1 and the control data