Simple wrapper around stepAIC() (package MASS) to repeatedly perform stepwise model selection by AIC on several dependent variables (or responses, taken as rows of a matrix).

1 |

`y` |
Numeric matrix (with responses as rows and samples as columns) or ExpressionSet. Typically the expression data with transcripts (i.e. for a microarray, probes) as rows and samples as columns. If an ExpressionSet is provided the expression data is extracted with the function exprs. |

`subset` |
Integer vector. Represents a subset of samples (specified as column indices in y) to use for model fitting. By default all samples are used. |

`verbose` |
logical. If TRUE (default) the response number being fitted is printed. |

`upper` |
see ?stepAIC |

`lower` |
see ?stepAIC |

`direction` |
see ?stepAIC |

`trace` |
see ?stepAIC |

`keep` |
see ?stepAIC |

The initial model for the stepwise approach only contains an intercept term.

`swft` |
List of stepwise-selected models (see ?stepAIC) |

Alexandre Kuhn alexandre.m.kuhn@gmail.com

Kuhn A, Kumar A, Beilina A, Dillman A, Cookson MR, Singleton AB. Cell population-specific expression analysis of human cerebellum. BMC Genomics 2012, 13:610.

`marker`

,`lmfitst`

.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 | ```
## Load example expression data (variable "expression")
## and phenotype data (variable "groups")
data("example")
## Four cell population-specific reference signals
neuron_probesets <- list(c("221805_at", "221801_x_at", "221916_at"),
"201313_at", "210040_at", "205737_at", "210432_s_at")
neuron_reference <- marker(expression, neuron_probesets)
astro_probesets <- list("203540_at",c("210068_s_at","210906_x_at"),
"201667_at")
astro_reference <- marker(expression, astro_probesets)
oligo_probesets <- list(c("211836_s_at","214650_x_at"),"216617_s_at",
"207659_s_at",c("207323_s_at","209072_at"))
oligo_reference <- marker(expression, oligo_probesets)
micro_probesets <- list("204192_at", "203416_at")
micro_reference <- marker(expression, micro_probesets)
## Stepwise model selection for 2 transcripts (202429_s_at and 200850_s_at)
## and focusing on control samples (i.e. groups == 0)
swlm(expression[c("202429_s_at", "200850_s_at"),],
subset = which(groups == 0),
upper = formula(~neuron_reference + astro_reference +
oligo_reference + micro_reference))
``` |

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