<p>By reading <a href="https://hemberg-lab.github.io/scRNA.seq.course/construction-of-expression-matrix.html#cell-qc" rel="nofollow noreferrer">this tutorial</a>:</p>

<blockquote>
  <p>Wells with few reads/molecules are likely to have been broken or failed to capture a cell, and should thus be removed.</p>
</blockquote>

<p>This answers the first part of the question.</p>

<p>For the second part, i.e. how to decide cutoff, it still seems a bit arbitrary to me, but some authors trim the cells on the left tail of the highest mode, in order to remove other modes (likely due to noise) on the left tail, considering the distribution of cells dependent on the sum of reads (molecules per cell, first plot).</p>

CC BY-SA 3.0
2017-12-28T17:39:18.547Z
2017-12-28T17:39:18.547Z
gc5
<p>I am running a notebook with example for the <a href="https://github.com/pkathail/magic" rel="noreferrer">MAGIC</a> algorithm.</p>

<p>In the data preprocessing step, a filtering operation is required to filter out cells with a small count of transcripts.</p>

<p>My question is twofold:</p>

<ul>
<li>Why filtering out cells with a small count of transcripts? </li>
<li>How to decide the cutoff, i.e. the minimum number of transcripts each cell should have to not be removed from the dataset?</li>
</ul>

<p>I attach some plots from the original notebook, which display some distributions of the single-cell rnaseq dataset.</p>

<p><a href="https://i.sstatic.net/UtHCB.png" rel="noreferrer"><img src="https://i.sstatic.net/UtHCB.png" alt="Plots MAGIC"></a></p>

CC BY-SA 3.0
2017-12-20T20:48:21.600Z
In my experience it's helpful to explore the data (ie. make some heatmaps etc) and look for trends that are associated with low molecule counts.  For example, cells with low umi's may look weird or cluster separately.  You can use these to guide your cutoff.
GWW
CC BY-SA 3.0
2017-12-20T21:18:20.530Z
@GWW can you elaborate more on that? Do you mean for example to plot the two principal components and see where the different cells cluster? And maybe if I have a cluster composed by only cells with limited library size, exclude that cluster? Otherwise, which plots will you use? Thanks
gc5
CC BY-SA 3.0
2017-12-20T21:29:36.803Z
I suggest a heatmap of the most variable genes.  PCA may be harder to see.
GWW
<rna-seq><filtering><single-cell>
Single-cell RNA sequencing (scRNA-seq): filtering cells by transcript counts, how to choose cutoffs?
# Single-cell RNA sequencing (scRNA-seq): filtering cells by transcript counts, how to choose cutoffs?

(Asked by: username_0 on 2017-12-20)

I am running a notebook with example for the [MAGIC](https://github.com/pkathail/magic) algorithm.

In the data preprocessing step, a filtering operation is required to filter out cells with a small count of transcripts.

My question is twofold:

* Why filtering out cells with a small count of transcripts?
* How to decide the cutoff, i.e. the minimum number of transcripts each cell should have to not be removed from the dataset?

I attach some plots from the original notebook, which display some distributions of the single-cell rnaseq dataset.

[![Plots MAGIC](https://i.sstatic.net/UtHCB.png)](https://i.sstatic.net/UtHCB.png)

**Tags:** rna-seq, filtering, single-cell
> **Comments:**
> **username_1**: In my experience it's helpful to explore the data (ie. make some heatmaps etc) and look for trends that are associated with low molecule counts.  For example, cells with low umi's may look weird or cluster separately.  You can use these to guide your cutoff.  
> **username_0**: @GWW can you elaborate more on that? Do you mean for example to plot the two principal components and see where the different cells cluster? And maybe if I have a cluster composed by only cells with limited library size, exclude that cluster? Otherwise, which plots will you use? Thanks  
> **username_1**: I suggest a heatmap of the most variable genes.  PCA may be harder to see.  

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## 1 answers

### Answer by username_0 on 2017-12-28. Score: 1 | Accepted answer

By reading [this tutorial](https://hemberg-lab.github.io/scRNA.seq.course/construction-of-expression-matrix.html#cell-qc):

> Wells with few reads/molecules are likely to have been broken or failed to capture a cell, and should thus be removed.

This answers the first part of the question.

For the second part, i.e. how to decide cutoff, it still seems a bit arbitrary to me, but some authors trim the cells on the left tail of the highest mode, in order to remove other modes (likely due to noise) on the left tail, considering the distribution of cells dependent on the sum of reads (molecules per cell, first plot).


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