text: user15487: have a good R and statistical analysis background (also with machine learning). in addition, I'm a fresh biotechnology grad. I would like to try to replicate some Rna-seq analysis with R papers (with their provided data). Any SHORT (beginner-friendly) papers to recommend?
  
  user240: I like Gierlinksi, et al., Bioinformatics, 2015 for this type of problem.
  I like this paper because the methods are descriptive and clear, they have lots of figures that you can reproduce along the way, they spend a lot of time discussing "normalization" and quality control, and the data is easy to download.
  Even better, the authors spent a lot of time making sure all the samples were split across lanes and flowcells in a consistent way so that both biological and technical differences could be robustly identified.
  They also compared a few different methods published at the time for performing differential expression analysis, so you can see how you can process the same data with different statistical models.
  Unfortunately, they don't provide the explicit code they run for this paper, so you can't look at their code as a reference to guide you.
  But the methods are descriptive enough that I don't think there should be too much difficulty.
  
original_text: 15487: <p>have a good R and statistical analysis background (also with machine learning). in addition, I'm a fresh biotechnology grad. I would like to try to replicate some Rna-seq analysis with R papers (with their provided data). Any SHORT (beginner-friendly) papers to recommend?</p>
   240: <p>I like <a href="https://doi.org/10.1093/bioinformatics/btv425" rel="nofollow noreferrer">Gierlinksi, <em>et al.</em>, Bioinformatics, 2015</a> for this type of problem.</p>
  <p>I like this paper because the methods are descriptive and clear, they have lots of figures that you can reproduce along the way, they spend a lot of time discussing &quot;normalization&quot; and quality control, and the data is easy to download.</p>
  <p>Even better, the authors spent a lot of time making sure all the samples were split across lanes and flowcells in a consistent way so that both biological and technical differences could be robustly identified.
  They also compared a few different methods published at the time for performing differential expression analysis, so you can see how you can process the same data with different statistical models.</p>
  <p>Unfortunately, they don't provide the explicit code they run for this paper, so you can't look at their code as a reference to guide you.
  But the methods are descriptive enough that I don't think there should be too much difficulty.</p>
  