<p>It means the distribution is heavily skewed and has a 'long-tail', thus the variance is much greater than the mean. In other words most values are around zero, with a small number of values having extreme values.</p>
<p>[<img src="https://i.sstatic.net/Puv45.png" alt="ePower laws distribution 2" /></p>
<p>You need to check the goodness of fit because that is saying the deviation from a power-laws distribution is just significant. So it ain't quite power-laws, but the general gist of a 'long-tailed distribution' is correct.</p>
<p>Its a good result and a nice analysis, but you are not quite there at present (needs to be &gt;0.05). Check the negative binomial distribution.</p>
<hr />
<p>I've seen you data, it looks very good and the power series is not a bad explanation of the data. There are two possibilities for the goodness of fit being 0.035</p>
<ol>
<li>The goodness of fit is low because of 'outliers' at the beginning of the distribution</li>
<li>An alternative distribution, such as a negative binomial distribution fits better</li>
<li>Check it is 0.035 not 0.35 because that changes everything.</li>
</ol>
<p>The best way to do this is to plot the power series expected distribution onto the graph and look where the fit 'fails'.
If it is not fitting at the beginning of the distribution thats is about outliers and easily solvable. If it is not really capturing the shape then its probably a slightly different distribution.</p>
<p>If its point 1 then just remove the very first point in the graph and try again and check the p-value. Or take the mean of the clump of three points next to the first point and try again (or just remove those three point). Don't do any more than that and the technical word is &quot;outlier&quot; analysis. In your write-up you must explain the issue around any 'outliers' and that is perfectly acceptable. If it is a power-series distrupted by an outlier(s) there is formal analysis to demonstrate an 'outlier' using outlier analysis if any one ever asks you.</p>
<p>If its point 2. then use e.g. negative bionomial or the gamma distribution (alpha &lt;1) and check the goodness of fit.</p>
<p>You want a goodness of fit &gt; 0.05 and preferably &gt;0.1 to be sure its the right distribution. There are formal statistics to do this, but that is the main one.</p>
<p>My guess is there are outliers at the beginning because your sample size is not large enough to identify the variation correctly, hence the fit is low. In 'long-tails' its easy to have a 'low sample size' because of the way they work. Thus in this case mapping a distribution is a perfect way to compensate for the sampling.</p>

CC BY-SA 4.0
2020-10-16T15:55:08.613Z
Thank you for your reply. I have now attached my result graph and I would really appreciate it if you could have a look at it
Priya
CC BY-SA 4.0
2020-10-16T19:20:26.293Z
Hi @Priya I've modificed the answer above
M__
CC BY-SA 4.0
2020-10-17T12:41:57.923Z
Thank you very much for explaining it so nicely.
Priya
CC BY-SA 4.0
2020-10-26T10:19:26.347Z
@Michael, it is not clear to me whether alpha here represents skewness or something else. Similarly, goodness of fit interpretation does not seem to help.
Subhash C. Davar
CC BY-SA 4.0
2020-10-26T11:00:55.660Z
The power laws distribution is skewed, but alpha will be 'more skewed' as the value increases. The value you have will be optimal for your the data set you plotted. I'm not sure what you mean by goodness of fit "doesn't seem to help".
M__
CC BY-SA 4.0
2020-10-16T14:52:59.177Z
2020-10-16T19:34:31.937Z
2020-10-16T19:34:31.937Z
M__
<p>I did a Pearson co-expression analysis for generating networks for my tissue-specific (chondrocytes) RNA-seq data. I used R package poweRlaw to check for the power-law distribution. We got the following values:</p>
<pre><code> Xmin = 14
 goodness of fit = 0.035
 α = 4.76
</code></pre>
<p><a href="https://i.sstatic.net/xoJtW.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/xoJtW.png" alt="enter image description here" /></a></p>
<ol>
<li>My question is what does alpha (α) of more than 4 indicates?</li>
<li>Is it is okay to have such values?</li>
</ol>
<p>Thank you</p>

<rna-seq><r><correlation>
Power law distribution alpha values
# Power law distribution alpha values

(Asked by: username_0 on 2020-10-16)

I did a Pearson co-expression analysis for generating networks for my tissue-specific (chondrocytes) RNA-seq data. I used R package poweRlaw to check for the power-law distribution. We got the following values:
 Xmin = 14
 goodness of fit = 0.035
 α = 4.76



My question is what does alpha (α) of more than 4 indicates?
Is it is okay to have such values?

Thank you

**Tags:** rna-seq, r, correlation

---

## 1 answers

### Answer by username_1 on 2020-10-16. Score: 3 | Accepted answer

It means the distribution is heavily skewed and has a 'long-tail', thus the variance is much greater than the mean. In other words most values are around zero, with a small number of values having extreme values.

[![ePower laws distribution 2](https://i.sstatic.net/Puv45.png)

You need to check the goodness of fit because that is saying the deviation from a power-laws distribution is just significant. So it ain't quite power-laws, but the general gist of a 'long-tailed distribution' is correct.

Its a good result and a nice analysis, but you are not quite there at present (needs to be >0.05). Check the negative binomial distribution.

---

I've seen you data, it looks very good and the power series is not a bad explanation of the data. There are two possibilities for the goodness of fit being 0.035

1. The goodness of fit is low because of 'outliers' at the beginning of the distribution
2. An alternative distribution, such as a negative binomial distribution fits better
3. Check it is 0.035 not 0.35 because that changes everything.

The best way to do this is to plot the power series expected distribution onto the graph and look where the fit 'fails'.
If it is not fitting at the beginning of the distribution thats is about outliers and easily solvable. If it is not really capturing the shape then its probably a slightly different distribution.

If its point 1 then just remove the very first point in the graph and try again and check the p-value. Or take the mean of the clump of three points next to the first point and try again (or just remove those three point). Don't do any more than that and the technical word is "outlier" analysis. In your write-up you must explain the issue around any 'outliers' and that is perfectly acceptable. If it is a power-series distrupted by an outlier(s) there is formal analysis to demonstrate an 'outlier' using outlier analysis if any one ever asks you.

If its point 2. then use e.g. negative bionomial or the gamma distribution (alpha <1) and check the goodness of fit.

You want a goodness of fit > 0.05 and preferably >0.1 to be sure its the right distribution. There are formal statistics to do this, but that is the main one.

My guess is there are outliers at the beginning because your sample size is not large enough to identify the variation correctly, hence the fit is low. In 'long-tails' its easy to have a 'low sample size' because of the way they work. Thus in this case mapping a distribution is a perfect way to compensate for the sampling.

> **Comments:**
> **username_0**: Thank you for your reply. I have now attached my result graph and I would really appreciate it if you could have a look at it  
> **username_1**: Hi @Priya I've modificed the answer above  
> **username_0**: Thank you very much for explaining it so nicely.  
> **username_2**: @Michael, it is not clear to me whether alpha here represents skewness or something else. Similarly, goodness of fit interpretation does not seem to help.  
> **username_1**: The power laws distribution is skewed, but alpha will be 'more skewed' as the value increases. The value you have will be optimal for your the data set you plotted. I'm not sure what you mean by goodness of fit "doesn't seem to help".  

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