inmoose.limma.lm_series

inmoose.limma.lm_series(M, design=None, ndups=1, spacing=1, weights=None)

Fit linear model to microarray data by ordinary least squares

Fit a linear model genewise to expression data from a series of arrays. This function uses ordinary least squares and is a utility function for lmFit(). Most users should not use this function directly but should use lmFit() instead.

The linear model is fit for each gene by calling the function lm_fit() or lm_wfit().

Parameters:

  • M (pd.DataFrame) – numeric matrix containing log-ratio or log-expression values for a series of microarrays, rows correspond to genes and columns to arrays

  • design – design matrix defining the linear model. The number of rows should agree with the number of columns of M. The number of columns will determine the number of coefficients estimated for each gene.

  • ndups (int) – number of duplicate spots. Each gene is printed ndups times in adjacent spots on each array

  • spacing (int) – the spacing between the rows of M corresponding to duplicate spots, spacing=1 for consecutive spots.

  • weights (array_like) – an optional 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.

Returns:

an object with attributes

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

  • stdev_unscaled: 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: vector giving the degrees of freedom corresponding to sigma.

  • qr: QR-decomposition of design

Return type:

MArrayLM