gls.series {limma} | R Documentation |

Fit a linear model genewise to expression data from a series of microarrays.
The fit is by generalized least squares allowing for correlation between duplicate spots or related arrays.
This is a utility function for `lmFit`

.

gls.series(M,design=NULL,ndups=2,spacing=1,block=NULL,correlation=NULL,weights=NULL,...)

`M` |
numeric matrix containing log-ratio or log-expression values for a series of microarrays, rows correspond to genes and columns to arrays. |

`design` |
numeric design matrix defining the linear model, with rows corresponding to arrays and columns to comparisons to be estimated. The number of rows must match the number of columns of `M` . Defaults to the unit vector meaning that the arrays are treated as replicates. |

`ndups` |
positive integer giving the number of times each gene is printed on an array. `nrow(M)` must be divisible by `ndups` . |

`spacing` |
the spacing between the rows of `M` corresponding to duplicate spots, `spacing=1` for consecutive spots |

`block` |
vector or factor specifying a blocking variable on the arrays.
Same length as `ncol(M)` . |

`correlation` |
numeric value specifying the inter-duplicate or inter-block correlation. |

`weights` |
an optional numeric matrix of the same dimension as `M` containing weights for each spot. If it is of different dimension to `M` , it will be filled out to the same size. |

`...` |
other optional arguments to be passed to `dupcor.series` . |

This is a utility function used by the higher level function `lmFit`

.
Most users should not use this function directly but should use `lmFit`

instead.

This function is for fitting gene-wise linear models when some of the expression values are correlated.
The correlated groups may arise from replicate spots on the same array (duplicate spots) or from a biological or technical replicate grouping of the arrays.
This function is normally called by `lmFit`

and is not normally called directly by users.

Note that the correlation is assumed to be constant across genes.
If `correlation=NULL`

then a call is made to `duplicateCorrelation`

to estimated the correlation.

A list with components

`coefficients` |
numeric matrix containing the estimated coefficients for each linear model. Same number of rows as `M` , same number of columns as `design` . |

`stdev.unscaled` |
numeric matrix conformal with `coef` containing the unscaled standard deviations for the coefficient estimators. The standard errors are given by `stdev.unscaled * sigma` . |

`sigma` |
numeric vector containing the residual standard deviation for each gene. |

`df.residual` |
numeric vector giving the degrees of freedom corresponding to `sigma` |

`correlation` |
inter-duplicate or inter-block correlation |

`qr` |
QR decomposition of the generalized linear squares problem, i.e., the decomposition of `design` standardized by the Choleski-root of the correlation matrix defined by `correlation` |

Gordon Smyth

An overview of linear model functions in limma is given by 06.LinearModels.

[Package *limma* version 2.4.7 Index]