edgepy

This module is a partial port in Python of the R Bioconductor edgeR package. Only the functionalities necessary to inmoose.pycombat.pycombat_seq() have been ported so far.

DGEList(counts[, lib_size, norm_factors, ...])	A class for storing read counts and associated information from digital gene expression or sequencing technologies.
addPriorCount(y[, lib_size, offset, prior_count])	Add a library size-adjusted prior count to each observation.
adjustedProfileLik(dispersion, y, design, offset)	Compute adjusted profile log-likelihoods for the dispersion parameters of genewise negative binomial GLMs.
aveLogCPM(y[, lib_size, offset, ...])	Compute average log2 counts per million for each row of counts.
designAsFactor(design)	Convert a general design matrix into a oneway layout if that is possible.
dispCoxReid(y[, design, offset, weights, ...])	Estimate a common dispersion parameter across multiple negative binomial GLMs, by maximizing the Cox-Reid adjusted profile likelihood.
dispCoxReidInterpolateTagwise(y, design, ...)	Estimate genewise dispersion parameters across multiple negative binomial GLMs using weighted Cox-Reid adjusted profile likelihood and cubic spline interpolation over a genewise grid.
estimateGLMCommonDisp(y[, design, offset, ...])	Estimate a common negative binomial dispersion parameter for a DGE dataset with a general experimental design.
estimateGLMTagwiseDisp(y, dispersion[, ...])	Compute an empirical Bayes estimate of the negative binomial dispersion parameter for each tag, with expression levels specified by a log-linear model.
glmFit(y[, design, dispersion, offset, ...])	Fit a negative binomial generalized log-linear model to the read counts for each gene.
mglmLevenberg(y, design[, dispersion, ...])	Fit genewise negative binomial GLMs with log-link using Levenberg damping to ensure convergence.
mglmOneGroup(y[, dispersion, offset, ...])	Fit single-group negative-binomial GLMs genewise.
mglmOneWay(y[, design, group, dispersion, ...])	Fit multiple negative binomial GLMs with log-link by Fisher scoring with a single explanatory factor in the model.
movingAverageByCol(x[, width, full_length])	Moving average smoother for matrix columns.
nbinomDeviance(y, mean[, dispersion, weights])	Residual deviances for row-wise negative binomial GLMs.
predFC(y, design[, prior_count, offset, ...])	Compute estimated coefficients for a negative binomial GLM in such a way that the log-fold-changes are shrunk towards zero.
splitIntoGroups(y[, group])	Split the counts according to group
systematicSubset(n, order_by)	Take a systematic subset of indices stratified by a ranking variable
validDGEList(y)	Check for standard components of DGEList object