inmoose.deseq2.nbinomLRT

inmoose.deseq2.nbinomLRT(obj, full=None, reduced=None, betaTol=1e-08, maxit=100, useOptim=True, quiet=False, useQR=True, minmu=None, type_='DESeq2')

Likelihood ratio test (chi-squared test) for GLMs

This function tests for significance of change in deviance between a full and reduced model which are provided as formula. Fitting uses previously calculated DESeqDataSet.sizeFactors (or DESeqDataSet.normalizationFactors) and dispersion estimates.

The difference in deviance is compared to chi-squared distribution with df = (reduced residual degrees of freedom - full residual degrees of freedom). This function is an alternative to nbinomWaldTest().

See also

DESeq, nbinomWaldTest

Parameters:

  • obj (DESeqDataSet) – a DESeqDataSet

  • full – the full model formula, this should be the formula in obj.design. Alternatively can be a matrix.

  • reduced – a reduced formula to compare against, e.g. the full model with a term or terms of interest removed.

  • betaTol (float) – control parameter defining convergence

  • maxit (int) – the maximum number of iterations to allow for convergence of the coefficient vector

  • useOptim (bool) – whether to use the native optim function on columns which do not converge within maxit iterations

  • quiet (bool) – whether to print messages at each step

  • useQR (bool) – whether to use the QR decomposition of the design matrix while fitting the GLM

  • minmu (float) – lower bound on the estimated count while fitting the GLM

  • type ("DESeq2" or "glmGamPoi") –

    If "DESeq2", a classical Likelihood ratio test based on the Chi-squared distribution is conducted.

    If "glmGamPoi" and previously the dispersion has been estimated with "glmGamPoi" as well, a quasi-likelihood ratio test based on the F-distribution is conducted. It is supposed to be more accurate, because it takes the uncertainty of dispersion estimate into account in the same way that a t-test improves upon a Z-test.

Returns:

the input obj with new results columns accessible through DESeqDataSet.results(). The coefficients and standard errors are reported on a log2 scale.

Return type:

DESeqDataSet