Description Usage Arguments Details Author(s) References See Also Examples

Function to simulate SAGE libraries with sequencing errors.

1 2 3 4 | ```
sagelibrary.simulate(taglength = 4, lambda = 1000, mean.error = 0.01,
error.sd = 1, withintagerror.sd = 0.2,
ngenes = min(4^taglength, 1e+05), base.lib = NULL,
libseed = -1, ...)
``` |

`taglength` |
Tag length for library. |

`lambda` |
Aproximate size of library. |

`mean.error` |
Mean amount of sequencing errors. |

`error.sd` |
Standard deviation for sequencing errors. |

`withintagerror.sd` |
Standard deviation for sequencing errors within tags. |

`ngenes` |
Number of genes to generate tags from. |

`base.lib` |
Simulate library based on tags in other lib and create variations. |

`libseed` |
Seed for random number generator. |

`...` |
Arguments passed to em.estimate. |

We set the number of possible transcripts and assign a random SAGE tag to each of them out of all 4\^taglength possible SAGE tags. For each SAGE tag a random proportion p within the library is generated from a log-normal distribution, and the proportions are then adjusted to have a sum of 1. The true counts of a tag are simulated by sampling from Poisson distributions with parameters p lambda, where p is the proportion of the tag in the library and lambda is a parameter for setting the size of the library. The simulation of the sequencing errors is done on each individual occurrence of a tag sequence. For each tag sequence a mean sequencing quality value is generated from a log-normal distribution. The individual quality values for each base are then generated from log-normal distributions with means equal to the simulated sequencing quality values for the tag sequences. We have noticed that with experimentally generated data the within tag sequence variation of sequencing quality values is usually about 1/5 of the between tag sequence variation. From each true tag sequence one observed tag sequence is generated using the simulated quality values of the true sequence as the multinomial probabilities, i.e. replacing each base with either one of the 3 other bases with the probability specified by the sequencing quality value of that base. The counts of these generated tags are then summed to represent the observed tags. When generating several simulated libraries for comparisons, we use the same proportions of the genes for all libraries, replacing up to 1/3 of the proportions by proportions with a known differential factor.

Tim Beissbarth

http://tagcalling.mbgproject.org

`sage.library`

,
`error.correction`

1 2 3 4 5 6 7 | ```
library(sagenhaft)
testlib1 <- sagelibrary.simulate(taglength=10, lambda=10000,
mean.error=0.01)
testlib2 <- sagelibrary.simulate(taglength=10, lambda=20000,
mean.error=0.02, base.lib=testlib1)
testlib3 <- sagelibrary.simulate(taglength=10, lambda=10000,
mean.error=0.01, libseed=testlib1$seed)
``` |

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.