Answers.Body: <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>
  
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Answers.Body: <p>Here's <a href="https://drive.google.com/file/d/1Lwh6ofxi_bb92mB0HoRrMJObE1MmtcMi/view?usp=sharing" rel="nofollow noreferrer">https://drive.google.com/file/d/1Lwh6ofxi_bb92mB0HoRrMJObE1MmtcMi/view?usp=sharing</a></p>
  <p>It is from the book of <code>Methods of mathematical oncology</code></p>
  <p><a href="https://github.com/ncbi/sra-tools" rel="nofollow noreferrer">https://github.com/ncbi/sra-tools</a></p>
  <pre><code>
  Suzuki, T., Poignard, C., Chaplain, M., &amp; Quaranta, V. (2021). Methods of mathematical oncology : Fusion of mathematics and biology, osaka, japan, october 26-28, 2020 (1st ed. 2021 ed.). Singapore: Springer Nature Singapore : Imprint: Springer.
  </code></pre>
  
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Answers.Text: Your answer could be improved with additional supporting information. Please [edit] to add further details, such as citations or documentation, so that others can confirm that your answer is correct. You can find more information on how to write good answers [in the help center](/help/how-to-answer).
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Answers.Body: <p>I am a beginner too.  I will try the galaxy (training):</p>
  <blockquote>
  <p>https://training.galaxyproject.org/training-material/topics/transcriptomics/tutorials/ref-based/tutorial.html</p>
  </blockquote>
  
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Body: <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>
  
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Comments.Text: Please edit the question to limit it to a specific problem with enough detail to identify an adequate answer.
Comments.UserDisplayName: Community
Tags: <r><rna-seq><bioconductor><rna>
Title: beginner RNA-seq Replicate papers
ThreadText: # beginner RNA-seq Replicate papers
  
  (Asked by: username_0 on 2022-06-08)
  
  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?
  
  **Tags:** r, rna-seq, bioconductor, rna
  
  > **Comments:**
  > **username_1**: Please edit the question to limit it to a specific problem with enough detail to identify an adequate answer.  
  
  ---
  
  ## 3 answers
  
  ### Answer by username_2 on 2022-06-20. Score: 2
  
  I like [Gierlinksi, *et al.*, Bioinformatics, 2015](https://doi.org/10.1093/bioinformatics/btv425) 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.
  
  
  ---
  
  ### Answer by username_3 on 2022-06-18. Score: 0
  
  Here's <https://drive.google.com/file/d/1Lwh6ofxi_bb92mB0HoRrMJObE1MmtcMi/view?usp=sharing>
  
  It is from the book of `Methods of mathematical oncology`
  
  <https://github.com/ncbi/sra-tools>
  
  ```
  
  Suzuki, T., Poignard, C., Chaplain, M., & Quaranta, V. (2021). Methods of mathematical oncology : Fusion of mathematics and biology, osaka, japan, october 26-28, 2020 (1st ed. 2021 ed.). Singapore: Springer Nature Singapore : Imprint: Springer.
  
  ```
  
  > **Comments:**
  > **username_1**: Your answer could be improved with additional supporting information. Please [edit] to add further details, such as citations or documentation, so that others can confirm that your answer is correct. You can find more information on how to write good answers [in the help center](/help/how-to-answer).  
  
  ---
  
  ### Answer by username_4 on 2022-06-09. Score: 0
  
  I am a beginner too. I will try the galaxy (training):
  
  > https://training.galaxyproject.org/training-material/topics/transcriptomics/tutorials/ref-based/tutorial.html
  
  
  ---
  
  