edgepy
This module is a partial port in Python of the R Bioconductor edgeR package.
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 edgepy to perform a differential
expression analysis on the pasilla dataset.
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
Datasets
Models
