inmoose.edgepy.estimateGLMCommonDisp

inmoose.edgepy.estimateGLMCommonDisp(y, design=None, offset=None, method='CoxReid', subset=10000, AveLogCPM=None, verbose=False, weights=None)

Estimate a common negative binomial dispersion parameter for a DGE dataset with a general experimental design.

This function calls dispCoxReid(), dispPearson() or dispDeviance() depending on the method specified. See dispCoxReid() for details of the three methods and a discussion of their relative performance.

See also

dispCoxReid

estimateGLMTrendedDisp

for trended dipsersions

estimateGLMTagwiseDisp

for genewise dispersions in the context of a GLM

estimateCommonDisp

for the common dispersion

estimateTagwiseDisp

for genewise dispersion in the context of a multiple group experiment (one-way layout)

Parameters:

  • y (matrix) – matrix of counts, as in glmFit()

  • design (matrix, optional) – design matrix, as in glmFit()

  • offset (array_like, optional) – vector or matrix of offsets for the log-linear models, as in glmFit()

  • method (str) – method for estimating the dispersion. Possible values are “CoxReid”, “Pearson” or “deviance”. Defaults to “CoxReid”.

  • subset (int) – maximum number of rows of y to use in the calculation. Rows used are chosen evenly spaced by AveLogCPM using systematicSubset().

  • AveLogCPM (array_like) – vector of log2 average counts per million for each gene

  • weights (matrix, optional) – observation weights

Returns:

estimated common dispersion

Return type:

float