1 Analysis

A guided example showing how processed results from the RNAseq pipeline SPEAQeasy can be used in differential expression analyses and visualization. We also show how existing genotype information for a set of samples can be combined with SPEAQeasy results to resolve any identity issues, as can emerge during sequencing.

2 Cite SPEAQeasy

We hope that SPEAQeasy will be useful for your research. Please use the following information to cite the package and the overall approach. Thank you!

@article {Eagles2021,
    author = {Eagles, Nicholas J. and Burke, Emily E. and Leonard, Jabob and Barry, Brianna K. and Stolz, Joshua M. and Huuki, Louise and Phan, BaDoi N. and Larrios Serrato, Violeta and Guti{\'e}rrez-Mill{\'a}n, Everardo and Aguilar-Ordo{\~n}ez, Israel and Jaffe, Andrew E. and Collado-Torres, Leonardo},
    title = {SPEAQeasy: a scalable pipeline for expression analysis and quantification for R/bioconductor-powered RNA-seq analyses},
    year = {2021},
    doi = {10.1186/s12859-021-04142-3},
    publisher = {Cold Spring Harbor Laboratory},
    URL = {https://doi.org/10.1186/s12859-021-04142-3},
    journal = {BMC Bioinformatics}
}

This is a project by the R/Bioconductor-powered Team Data Science at the Lieber Institute for Brain Development.

3 References

The analysis was possible thanks to R and many of its packages:

Bibliography file

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##  XVector                0.30.0   2020-10-27 [1] Bioconductor                             
##  yaml                   2.2.1    2020-02-01 [2] RSPM (R 4.0.3)                           
##  zlibbioc               1.36.0   2020-10-27 [1] Bioconductor                             
## 
## [1] /__w/_temp/Library
## [2] /usr/local/lib/R/site-library
## [3] /usr/local/lib/R/library