Description Usage Arguments Value See Also Examples

View source: R/generateTimeSeries.R

Generates time series by simulating successive state transitions from random start states. In addition, the resulting matrices can be perturbed by Gaussian noise.

1 2 3 4 5 6 7 | ```
generateTimeSeries(network,
numSeries,
numMeasurements,
type = c("synchronous","asynchronous","probabilistic"),
geneProbabilities,
perturbations = 0,
noiseLevel = 0)
``` |

`network` |
An object of class |

`numSeries` |
The number of random start states used to generate successive series of states, that is, the number of time series matrices to generate |

`numMeasurements` |
The number of states in each of the time series matrices. The first state of each time series is the randomly generated start state. The remaining |

`type` |
The type of state transitions to be performed (see |

`geneProbabilities` |
An optional vector of probabilities for the genes if |

`perturbations` |
If this argument has a value greater than 0, artificial perturbation experiments are generated. That is, |

`noiseLevel` |
If this is non-zero, it specifies the standard deviation of the Gaussian noise which is added to all entries of the time series matrices. By default, no noise is added to the time series. |

A list of matrices, each corresponding to one time series. Each row of these matrices contains measurements for one gene on a time line, i. e. column `i+1`

contains the successor states of column `i+1`

. If `noiseLevel`

is non-zero, the matrices contain real values, otherwise they contain only 0 and 1.

If `perturbations>0`

, the result list contains an additional matrix `perturbations`

specifying the artificial perturbations applied to the different time series. This matrix has `numSeries`

columns and one row for each gene in the network. A matrix entry is 0 for a knock-out of the corresponding gene in the corresponding time series, 1 for overexpression, and NA for no perturbation.

The result format is compatible with the input parameters of `binarizeTimeSeries`

and `reconstructNetwork`

.

`stateTransition`

, `binarizeTimeSeries`

, `reconstructNetwork`

1 2 3 4 5 6 7 8 9 | ```
# generate noisy time series from the cell cycle network
data(cellcycle)
ts <- generateTimeSeries(cellcycle, numSeries=50, numMeasurements=10, noiseLevel=0.1)
# binarize the noisy time series
bin <- binarizeTimeSeries(ts, method="kmeans")$binarizedMeasurements
# reconstruct the network
print(reconstructNetwork(bin, method="bestfit"))
``` |

```
Probabilistic Boolean network with 10 genes
Involved genes:
CycD Rb E2F CycE CycA p27 Cdc20 Cdh1 UbcH10 CycB
Transition functions:
Alternative transition functions for gene CycD:
CycD = <f(CycD){01}> (probability: 1, error: 0)
Alternative transition functions for gene Rb:
Rb = <f(CycD,CycE,CycA,p27,CycB){10100010001000100000000000000000}> (probability: 0.125, error: 0)
Rb = <f(CycD,CycE,CycA,p27,CycB){10100010001000100010000000000000}> (probability: 0.125, error: 0)
Rb = <f(CycD,CycE,CycA,p27,CycB){10100010001100100000000000000000}> (probability: 0.125, error: 0)
Rb = <f(CycD,CycE,CycA,p27,CycB){10100010001100100010000000000000}> (probability: 0.125, error: 0)
Rb = <f(CycD,CycE,CycA,p27,CycB){10100110001000100000000000000000}> (probability: 0.125, error: 0)
Rb = <f(CycD,CycE,CycA,p27,CycB){10100110001000100010000000000000}> (probability: 0.125, error: 0)
Rb = <f(CycD,CycE,CycA,p27,CycB){10100110001100100000000000000000}> (probability: 0.125, error: 0)
Rb = <f(CycD,CycE,CycA,p27,CycB){10100110001100100010000000000000}> (probability: 0.125, error: 0)
Alternative transition functions for gene E2F:
E2F = <f(Rb,CycA,p27,CycB){1010001000000000}> (probability: 1, error: 0)
Alternative transition functions for gene CycE:
CycE = <f(Rb,E2F){0100}> (probability: 1, error: 0)
Alternative transition functions for gene CycA:
CycA = <f(Rb,E2F,Cdc20,Cdh1,UbcH10){11000000111000000000000000000000}> (probability: 0.125, error: 2)
CycA = <f(Rb,E2F,Cdc20,Cdh1,UbcH10){11000000111000000000000001000000}> (probability: 0.125, error: 2)
CycA = <f(Rb,E2F,Cdc20,Cdh1,UbcH10){11000000111000000000100000000000}> (probability: 0.125, error: 2)
CycA = <f(Rb,E2F,Cdc20,Cdh1,UbcH10){11000000111000000000100001000000}> (probability: 0.125, error: 2)
CycA = <f(Rb,E2F,Cdc20,Cdh1,UbcH10){11000000111000100000000000000000}> (probability: 0.125, error: 2)
CycA = <f(Rb,E2F,Cdc20,Cdh1,UbcH10){11000000111000100000000001000000}> (probability: 0.125, error: 2)
CycA = <f(Rb,E2F,Cdc20,Cdh1,UbcH10){11000000111000100000100000000000}> (probability: 0.125, error: 2)
CycA = <f(Rb,E2F,Cdc20,Cdh1,UbcH10){11000000111000100000100001000000}> (probability: 0.125, error: 2)
Alternative transition functions for gene p27:
p27 = <f(CycD,CycE,CycA,p27,CycB){10100010001000000000000000000000}> (probability: 0.125, error: 0)
p27 = <f(CycD,CycE,CycA,p27,CycB){10100010001000000010000000000000}> (probability: 0.125, error: 0)
p27 = <f(CycD,CycE,CycA,p27,CycB){10100010001100000000000000000000}> (probability: 0.125, error: 0)
p27 = <f(CycD,CycE,CycA,p27,CycB){10100010001100000010000000000000}> (probability: 0.125, error: 0)
p27 = <f(CycD,CycE,CycA,p27,CycB){10100110001000000000000000000000}> (probability: 0.125, error: 0)
p27 = <f(CycD,CycE,CycA,p27,CycB){10100110001000000010000000000000}> (probability: 0.125, error: 0)
p27 = <f(CycD,CycE,CycA,p27,CycB){10100110001100000000000000000000}> (probability: 0.125, error: 0)
p27 = <f(CycD,CycE,CycA,p27,CycB){10100110001100000010000000000000}> (probability: 0.125, error: 0)
Alternative transition functions for gene Cdc20:
Cdc20 = <f(CycB){01}> (probability: 1, error: 0)
Alternative transition functions for gene Cdh1:
Cdh1 = <f(CycA,p27,Cdc20,CycB){1011101100111011}> (probability: 1, error: 0)
Alternative transition functions for gene UbcH10:
UbcH10 = <f(Cdc20,Cdh1,UbcH10,CycB){1111000111110011}> (probability: 1, error: 0)
Alternative transition functions for gene CycB:
CycB = <f(Cdc20,Cdh1){1000}> (probability: 1, error: 0)
```

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