<p>Given that TP53 is the most significant and is already known to have driver mutations in cancer it would seem to be the logical choice.</p>

CC BY-SA 4.0
2019-02-18T14:57:23.307Z
Thank you, does that mean I must ignore MAP1S as a candidate even though it has significant p-value but non-significant q-value?
Zizogolu
CC BY-SA 4.0
2019-02-18T14:59:57.417Z
Nothing I wrote suggests that you *must* ignore MAP1S, your question indicated you only wanted a single candidate. If you're fine with more then take more.
Devon Ryan
CC BY-SA 4.0
2019-02-18T10:18:39.267Z
2019-02-18T10:18:39.267Z
Devon Ryan
<p>You can't know which is the driver and which is the carrier. At most you can say that a specific gene deviate more of the expected underlying hypothesis. See also <a href="https://stats.stackexchange.com/a/101290/105234">other</a> <a href="http://onlinestatbook.com/2/advanced_graphs/q-q_plots.html" rel="nofollow noreferrer">resources</a> <a href="https://support.sas.com/documentation/cdl/en/procstat/63104/HTML/default/viewer.htm#procstat_univariate_sect040.htm" rel="nofollow noreferrer">online</a>.</p>

<p>You also seem to ignore other genes that deviate more of your null hypothesis. I recommend to plot the histogram of the p-values to see if the distribution is uniform</p>

CC BY-SA 4.0
2019-02-20T15:34:43.213Z
I think it's pretty clear from the QQ-plot that these p-values are uniformly distributed (except for the twenty or so in the tail of the distribution)?
winni2k
CC BY-SA 4.0
2019-02-20T16:01:51.390Z
@winni2k Usually the QQ-plot is centered around 0 (if using `qqnorm`), in this plot it is not centred, also a expected and observed pvalue above 1 indicates that this is not a normal QQ-plot. That's why I recommended the histogram.
llrs
CC BY-SA 4.0
2019-02-20T16:57:28.747Z
Ah! I see. I think the x and y-axes must be -log10(p-value) as shown for example [here](https://stats.stackexchange.com/questions/92124/how-to-interpret-a-qq-plot-of-p-values), and not quantiles as would be reported by qqnorm. #missingaxislabels
winni2k
CC BY-SA 4.0
2019-02-19T08:27:39.450Z
2019-02-19T08:27:39.450Z
llrs
<p>This looks to be a pretty good Q-Q plot. My recollection of doing Q-Q plots is that a good Q-Q requires a "more or less" linear relationship for the model to be considered okay. Again this model looks good, its 1:1, albeit it does deviate a little (for the gene of interest). TP15 falls outside the 1:1.</p>

<p>@Devon Ryan states TP15 has good biological credentials, which is clearly important. It maybe worth checking  MAP15 because this is "more or less" within your model (for my Q-Q plots definitely within the model :-) ). </p>

<p>In summary, its just my opinion - that on the basis of the Q-Q plot the driver is the not the expected candidate.</p>

CC BY-SA 4.0
2019-02-19T01:28:59.560Z
2019-02-19T01:36:34.230Z
2019-02-19T01:36:34.230Z
M__
<p>I have a qqplot of my whole genome sequencing data; A plot is for showing possibly significant driver genes. I tried to read about qqplot though but people only say about the skewedness while I want to know from these two genes which one are more likely to be driver</p>

<p><a href="https://i.sstatic.net/7Otcg.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/7Otcg.png" alt="enter image description here"></a></p>

<variant-calling><mutations><wgs>
Which gene I should select from this qqplot
# Which gene I should select from this qqplot

(Asked by: username_0 on 2019-02-18)

I have a qqplot of my whole genome sequencing data; A plot is for showing possibly significant driver genes. I tried to read about qqplot though but people only say about the skewedness while I want to know from these two genes which one are more likely to be driver

[![enter image description here](https://i.sstatic.net/7Otcg.png)](https://i.sstatic.net/7Otcg.png)

**Tags:** variant-calling, mutations, wgs

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## 3 answers

### Answer by username_1 on 2019-02-18. Score: 3

Given that TP53 is the most significant and is already known to have driver mutations in cancer it would seem to be the logical choice.

> **Comments:**
> **username_0**: Thank you, does that mean I must ignore MAP1S as a candidate even though it has significant p-value but non-significant q-value?  
> **username_1**: Nothing I wrote suggests that you *must* ignore MAP1S, your question indicated you only wanted a single candidate. If you're fine with more then take more.  

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### Answer by username_2 on 2019-02-19. Score: 2

You can't know which is the driver and which is the carrier. At most you can say that a specific gene deviate more of the expected underlying hypothesis. See also [other](https://stats.stackexchange.com/a/101290/105234) [resources](http://onlinestatbook.com/2/advanced_graphs/q-q_plots.html) [online](https://support.sas.com/documentation/cdl/en/procstat/63104/HTML/default/viewer.htm#procstat_univariate_sect040.htm).

You also seem to ignore other genes that deviate more of your null hypothesis. I recommend to plot the histogram of the p-values to see if the distribution is uniform

> **Comments:**
> **username_3**: I think it's pretty clear from the QQ-plot that these p-values are uniformly distributed (except for the twenty or so in the tail of the distribution)?  
> **username_2**: @winni2k Usually the QQ-plot is centered around 0 (if using `qqnorm`), in this plot it is not centred, also a expected and observed pvalue above 1 indicates that this is not a normal QQ-plot. That's why I recommended the histogram.  
> **username_3**: Ah! I see. I think the x and y-axes must be -log10(p-value) as shown for example [here](https://stats.stackexchange.com/questions/92124/how-to-interpret-a-qq-plot-of-p-values), and not quantiles as would be reported by qqnorm. #missingaxislabels  

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### Answer by username_4 on 2019-02-19. Score: 1

This looks to be a pretty good Q-Q plot. My recollection of doing Q-Q plots is that a good Q-Q requires a "more or less" linear relationship for the model to be considered okay. Again this model looks good, its 1:1, albeit it does deviate a little (for the gene of interest). TP15 falls outside the 1:1.

@Devon Ryan states TP15 has good biological credentials, which is clearly important. It maybe worth checking MAP15 because this is "more or less" within your model (for my Q-Q plots definitely within the model :-) ).

In summary, its just my opinion - that on the basis of the Q-Q plot the driver is the not the expected candidate.


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