Answers.Body: <p>The explanation can be found in link posted by <strong>ATpoint</strong> in his comment:</p>
  <blockquote>
  <p>I get at least 10 posts on GitHub, GitMemory and Biostars just on the top page.</p>
  <ul>
  <li><a href="https://github.com/satijalab/seurat/issues/1378" rel="nofollow noreferrer">https://github.com/satijalab/seurat/issues/1378</a></li>
  <li><a href="https://github.com/satijalab/seurat/issues/2578" rel="nofollow noreferrer">https://github.com/satijalab/seurat/issues/2578</a></li>
  <li><a href="https://github.com/satijalab/seurat/issues/1378" rel="nofollow noreferrer">https://github.com/satijalab/seurat/issues/1378</a></li>
  <li><a href="https://www.biostars.org/p/450394/" rel="nofollow noreferrer">https://www.biostars.org/p/450394/</a></li>
  </ul>
  </blockquote>
  <p>The first author of the SCTransform paper, Christoph Hafemeister had posted this answer in a similar query on the satijalab GitHub page <a href="https://github.com/satijalab/seurat/issues/1378" rel="nofollow noreferrer">https://github.com/satijalab/seurat/issues/1378</a></p>
  <p>&quot;These warnings are related to the estimation of the theta parameter of the Negative Binomial regression model used to describe the molecule count distribution of each gene. When a gene has very few non-zero observations estimation of that parameter is not reliable and that's what the message indicates. This is not a problem, since the estimated theta parameters are not used directly for normalization but are regularized (sharing information across genes and thus lowering uncertainty).&quot;</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2021-07-24T16:07:48.157Z
Answers.LastActivityDate: 2021-07-27T04:47:35.553Z
Answers.LastEditDate: 2021-07-27T04:47:35.553Z
Answers.UserDisplayName: mnkyntigr
Body: <p>Running SCTransform on my seurat object produces the warning:</p>
  <pre><code>Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
  </code></pre>
  <p>What does this warning mean? Can I trust the output?</p>
  <p>SCTransform runs through the data producing this warning until the progress bar reaches 100%. The resulting seurat object <strong>does</strong> have an SCT assay and I have successfully done downstream analysis with it. I am concerned that my SCTransform has not successfully done what it is supposed to do. Is there a way to cross-check?</p>
  
Tags: <scrnaseq><seurat><sctransform>
Title: SCTransform Warning: in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
ThreadText: # SCTransform Warning: in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
  
  (Asked by: username_0 on 2021-07-24)
  
  Running SCTransform on my seurat object produces the warning:
  
  ```
  Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
  
  ```
  
  What does this warning mean? Can I trust the output?
  
  SCTransform runs through the data producing this warning until the progress bar reaches 100%. The resulting seurat object **does** have an SCT assay and I have successfully done downstream analysis with it. I am concerned that my SCTransform has not successfully done what it is supposed to do. Is there a way to cross-check?
  
  **Tags:** scrnaseq, seurat, sctransform
  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_0 on 2021-07-24. Score: 2
  
  The explanation can be found in link posted by **ATpoint** in his comment:
  
  > I get at least 10 posts on GitHub, GitMemory and Biostars just on the top page.
  >
  > * <https://github.com/satijalab/seurat/issues/1378>
  > * <https://github.com/satijalab/seurat/issues/2578>
  > * <https://github.com/satijalab/seurat/issues/1378>
  > * <https://www.biostars.org/p/450394/>
  
  The first author of the SCTransform paper, Christoph Hafemeister had posted this answer in a similar query on the satijalab GitHub page <https://github.com/satijalab/seurat/issues/1378>
  
  "These warnings are related to the estimation of the theta parameter of the Negative Binomial regression model used to describe the molecule count distribution of each gene. When a gene has very few non-zero observations estimation of that parameter is not reliable and that's what the message indicates. This is not a problem, since the estimated theta parameters are not used directly for normalization but are regularized (sharing information across genes and thus lowering uncertainty)."
  
  
  ---
  
  