======

edgepy

======

.. currentmodule:: inmoose.edgepy

This module is a partial port in Python of the R Bioconductor `edgeR package

<https://bioconductor.org/packages/release/bioc/html/edgeR.html>`_.

Only the functionalities necessary to :func:`inmoose.pycombat.pycombat_seq` and

differential expression analysis have been ported so far.

Differential Expression Analysis Example

========================================

We give below an example of how to use :code:`edgepy` to perform a differential

expression analysis on the pasilla dataset.

.. repl::

   from inmoose.data.pasilla import pasilla

   from inmoose.edgepy import DGEList, glmLRT, topTags

   from patsy import dmatrix

   # load the pasilla dataset as an AnnData

   pas = pasilla()

   # extract the count matrix and the annotation dataframe from the AnnData object

   counts = pas.X.T

   anno = pas.obs

   # build the design matrix

   design = dmatrix("~condition", data=anno)

   # build a DGEList object

   dge_list = DGEList(counts=counts, samples=anno, group_col="condition", genes=pas.var)

   # estimate the dispersions

   dge_list.estimateGLMCommonDisp(design=design)

   # fit the GLM

   fit = dge_list.glmFit(design=design)

   # run a differential expression analysis based on LRT

   lrt = glmLRT(fit)

   topTags(lrt)

References

==========

.. [Chen2016] Y. Chen, A.T.L Lun, G.K. Smyth. 2016. From reads to genes to

   pathways: differential expression analysis of RNA-Seq experiments using

   Rsubread and the edgeR quasi-likelihood pipeline. *F1000Research* 5, 1438.

   :doi:`10.12688/f1000research.8987.2`

.. [Gibbons1975] J.D. Gibbons, J.W. Pratt. 1975. P-values: interpretation and

   methodology. *The American Statistician* 29, 20-25.

   :doi:`10.1080/00031305.1975.10479106`

.. [Lun2016] A.T.L. Lun, Y. Chen, G.K. Smyth. 2016. It's DE-licious: a recipe

   for differential expression analyses of RNA-seq experiments using

   quasi-likelihood methods in edgeR.  *Methods in Molecular Biology* 1418,

   391-416. :doi:`10.1007/978-1-4939-3578-9_19`

.. [Lund2012] S.P. Lund, D. Nettleton, D.J. McCarthy, G.K. Smyth. 2012.

   Detecting differential expression in RNA-sequence data using quasi-likelihood

   with shrunken dispersion estimates. *Statistical Applications in Genetics and

   Molecular Biology* Volume 11, Issue 5, Article 8.

   :doi:`10.1515/1544-6115.1826`

.. [Lun2017] A.T.L. Lun, G.K. Smyth. 2017. No counts, no variance: allowing for

   loss of degrees of freedom when assessing biological variability from RNA-seq

   data. *Statistical Applications in Genetics and Molecular Biology* 16(2),

   83-93. :doi:`10.1515/sagmb-2017-0010`

.. [McCarthy2012] D. J. McCarthy, Y. Chen, G. K. Smyth. 2012. Differential

   expression analysis of multifactor RNA-Seq experiments with respect to

   biological variation. Nucleic Acids Research 40, 4288-4297.

   :doi:`10.1093/nar/gks042`

.. [Phipson2016] B. Phipson, S. Lee, I.J. Majewski, W. S. Alexander, G.K. Smyth.

   2016. Robust hyperparameter estimation protects against hypervariable genes

   and improves power to detect differential expression. *Annals of Applied

   Statistics* 10, 946-963. :doi:`10.1214/16-AOAS920`

.. [Robinson2008] M.D. Robinson, g.K. Smyth. 2008. Small-sample estimation of

   negative binomial dispersion, with applications to SAGE data.

   *Biostatistics* 9, 321-332.  :doi:`10.1093/biostatistics/kxm030`

Code documentation

==================

.. autosummary::

   :toctree: generated/

   DGEList

   addPriorCount

   adjustedProfileLik

   aveLogCPM

   binomTest

   designAsFactor

   dispCoxReid

   dispCoxReidInterpolateTagwise

   estimateGLMCommonDisp

   estimateGLMTagwiseDisp

   exactTest

   exactTestBetaApprox

   exactTestByDeviance

   exactTestBySmallP

   exactTestDoubleTail

   glmFit

   glmLRT

   glmQLFit

   glmQLFTest

   mglmLevenberg

   mglmOneGroup

   mglmOneWay

   movingAverageByCol

   nbinomDeviance

   plotQLDisp

   predFC

   splitIntoGroups

   systematicSubset

   topTags

   validDGEList