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M
- Simulated M data for examples.
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Net
- Simulated network data for examples.
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Net_inf
- Reverse-engineered network of the simulated data.
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Selection
- Selection of genes.
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analyze_network(<network>)
- Analysing the network
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as.micro_array()
- Coerce a matrix into a micro_array object.
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compare(<network>,<network>,<numeric>)
- Some basic criteria of comparison between actual and inferred network.
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cutoff(<network>)
- Choose the best cutoff
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dim(<micro_array>)
- Dimension of the data
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evolution(<network>)
- See the evolution of the network with change of cutoff
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geneNeighborhood(<network>)
- Find the neighborhood of a set of nodes.
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geneSelection(<micro_array>,<micro_array>,<numeric>) geneSelection(<list>,<list>,<numeric>) genePeakSelection(<micro_array>,<numeric>)
- Methods for selecting genes
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gene_expr_simulation(<network>)
- Simulates microarray data based on a given network.
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head(<micro_array>)
- Overview of a micro_array object
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inference(<micro_array>)
- Reverse-engineer the network
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micro_array-class
- Class
"micro_array"
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micropredict-class
- Class
"micropredict"
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network-class
- Class
"network"
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network
- A network object data.
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network_random()
- Generates a network.
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plot(<micro_array>,<ANY>) plot(<network>,<ANY>) plot(<micropredict>,<ANY>)
- Plot
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position(<network>)
- Returns the position of edges in the network
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predict(<micro_array>)
- Prediction of the gene expressions after a knock-out experience
predict
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print(<micro_array>) print(<network>)
- Methods for Function
print
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summary(<micro_array>)
- Methods for Function
summary
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unionMicro(<micro_array>,<micro_array>)
- Makes the union between two micro_array objects.
