<p><em>Answer from @llrs, converted from comment:</em></p>
<p>Probably is a question for the maintainer but I would <em>guess</em> that is a variance of the gene in the control dataset. So, if in normal samples the gene has a standard deviation of 0.5 this is the expected too in the tumoral cells. calculate the sd only on the control samples. So if the healthy/control samples have such variation the altered ones should have these or more according to the article (if I understand it correctly).</p>
<p><code>std &lt;- apply(data[, controls], 1, sd, na.rm = TRUE); quantile(std, 0.25)</code> should be much faster and efficient</p>
<p>But  I would check it in <a href="https://support.bioconductor.org" rel="nofollow noreferrer">support.bioconductor</a> with the authors/maintainers.</p>

CC BY-SA 4.0
2021-05-12T02:18:06.593Z
2021-05-12T02:18:06.593Z
gringer
<p>I'm running the <a href="https://www.bioconductor.org/packages/release/bioc/html/pathifier.html" rel="nofollow noreferrer">pathifier</a> approach against C2 pathway curated
database for a specific microarray dataset.</p>
<p>As I was reading the documentation of the pathifier, in order to configure it properly for my dataset, I saw that for the <code>min_std</code> argument they suggest to use the technical noise.</p>
<blockquote>
<p>min_std:   The minimal allowed standard deviation of each gene. Genes
with lower standard deviation are divided by min_std instead of their
actual standard deviation. (Recommended: set min_std to be the
technical noise).</p>
</blockquote>
<p>But how am I going to calculate it ? At first run I just used the value <code>0.2254005</code> from the example they provide with that package, but this might not be the right for my dataset.</p>
<p>Update:</p>
<p>So from <a href="http://www.weizmann.ac.il/complex/compphys/software/yotam/pathifier/help.htm" rel="nofollow noreferrer">here</a> it says: <code>The minimal standard error (min_std) was set as the first quartile of the standard deviation in the data</code>. How am I supposed to calculate this first quartile of the std? Any idea? Should I calculate the sd() for each gene and then find the first quartile?</p>
<p>What do you think about this <a href="https://pastebin.com/ni5uKhpX" rel="nofollow noreferrer">function</a>?</p>

<r><microarray><pathway>
Pathifier bioconductor package recommends to use technical noise for an argument. How to determine it?
# Pathifier bioconductor package recommends to use technical noise for an argument. How to determine it?

(Asked by: username_0 on 2018-01-10)

I'm running the [pathifier](https://www.bioconductor.org/packages/release/bioc/html/pathifier.html) approach against C2 pathway curated
database for a specific microarray dataset.

As I was reading the documentation of the pathifier, in order to configure it properly for my dataset, I saw that for the `min_std` argument they suggest to use the technical noise.

> min\_std: The minimal allowed standard deviation of each gene. Genes
> with lower standard deviation are divided by min\_std instead of their
> actual standard deviation. (Recommended: set min\_std to be the
> technical noise).

But how am I going to calculate it ? At first run I just used the value `0.2254005` from the example they provide with that package, but this might not be the right for my dataset.

Update:

So from [here](http://www.weizmann.ac.il/complex/compphys/software/yotam/pathifier/help.htm) it says: `The minimal standard error (min_std) was set as the first quartile of the standard deviation in the data`. How am I supposed to calculate this first quartile of the std? Any idea? Should I calculate the sd() for each gene and then find the first quartile?

What do you think about this [function](https://pastebin.com/ni5uKhpX)?

**Tags:** r, microarray, pathway

---

## 1 answers

### Answer by username_1 on 2021-05-12. Score: 1

*Answer from @llrs, converted from comment:*

Probably is a question for the maintainer but I would *guess* that is a variance of the gene in the control dataset. So, if in normal samples the gene has a standard deviation of 0.5 this is the expected too in the tumoral cells. calculate the sd only on the control samples. So if the healthy/control samples have such variation the altered ones should have these or more according to the article (if I understand it correctly).

`std <- apply(data[, controls], 1, sd, na.rm = TRUE); quantile(std, 0.25)` should be much faster and efficient

But I would check it in [support.bioconductor](https://support.bioconductor.org) with the authors/maintainers.


---

