<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>

CC BY-SA 4.0
2021-07-24T16:07:48.157Z
2021-07-27T04:47:35.553Z
2021-07-27T04:47:35.553Z
mnkyntigr
<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>

<scrnaseq><seurat><sctransform>
SCTransform Warning: in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
# SCTransform Warning: in theta.ml(y = y, mu = fit$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

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## 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)."


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