inmoose.limma.MArrayLM

class inmoose.limma.MArrayLM(coefficients, stdev_unscaled, sigma, df_residual, cov_coef)

A class to store the results of fitting gene-wise linear models to a set of microarrays

Objects of this class are normally created by lmFit() and additional components are added by eBayes().

coefficients

matrix containing fitted coefficients or contrasts

Type:

pd.DataFrame

stdev_unscaled

matrix containing unscaled standard deviations of the coefficients or contrasts

Type:

pd.DataFrame

sigma

array containing residual standard deviations for each gene

Type:

ndarray

df_residual

array containing residual degrees of freedom for each gene

Type:

ndarray

Amean

array containing the average log-intensity for each probe over all the arrays in the original linear model fit. Note that this vector does not change when a contrast is applied to the fit using contrasts_fit().

Type:

ndarray, optional

genes

data frame containing probe annotation

Type:

pd.DataFrame, optional

design

design matrix

Type:

patsy.DesignMatrix, optional

cov_coefficients

matrix giving the unscaled covariance matrix of the estimable coefficients

Type:

pd.DataFrame, optional

contrasts

matrix defining contrasts of coefficients for which results are desired

Type:

pd.DataFrame, optional

s2_prior

single value or array giving empirical Bayes estimated prior value for residual variances

Type:

ndarray, optional

df_prior

value or vector giving empirical Bayes estimated degrees of freedom associated with s2_prior for each gene

Type:

ndarray, optional

df_total

array giving total degrees of freedom used for each gene, usually equal to df_prior + df_residual

Type:

ndarray, optional

s2_post

array giving posterior residual variances

Type:

ndarray, optional

var_prior

array giving empirical Bayes estimated prior variance for each true coefficient

Type:

ndarray, optional

F

array giving moderated F-statistics for testing all contrasts equal to zero

Type:

ndarray, optional

F_p_value

array giving p-value corresponding to F_stat

Type:

ndarray, optional

t

matrix containing empirical Bayes t-statistics

Type:

pd.DataFrame, optional

p_value

matrix of two-sided p-values corresponding to the t-statistics

Type:

pd.DataFrame, optional

lods

matrix giving the log-odds of differential expression (on the natural log scale)

Type:

pd.DataFrame, optional

__init__(coefficients, stdev_unscaled, sigma, df_residual, cov_coef)

Methods

__init__(coefficients, stdev_unscaled, ...)