<p>Your calculations seem right, perhaps there was an error on their side. </p>

<p>I also looked at the number of samples reported, but they use the same amount of samples in each case. Because they are testing whether Linc-ZNF469-3 is different in several tissues, they should have used a multiple test correction for the p-value (although reporting the original values is usually also ok). </p>

<p>To answer your question in the comments. You can't correlate a categorical variable with a continuous  variable. Or they did another test or they are not explaining clearly what they did.</p>

CC BY-SA 4.0
2018-10-09T13:53:42.100Z
Thanks for the reply. For my question...I also had a look into some other papers. For eg: Check this table [https://www.nature.com/articles/s41598-017-04736-z/tables/1] They say in the paper like this; High RAD51-AS1 expression correlated with advanced FIGO stage, histological subtype, high histological grade, number of tumors and distant metastasis. I'm bit confused. Is it based on their percentage they say RAD51-AS1 expression is correlated with those variables?
stack_learner
CC BY-SA 4.0
2018-10-09T14:02:30.447Z
@user3351523 They could show a plot of like FIGO stage and the expression of the variable, and see a linear trend towards higher expression on certain stages, but without comparing the mean or the percentage/proportion (and not a correlation) that could be noise or illusions.
llrs
CC BY-SA 4.0
2018-10-09T13:47:51.733Z
2018-10-09T13:47:51.733Z
llrs
<p>I recently read the paper <a href="https://www.nature.com/articles/s41388-018-0293-1" rel="nofollow noreferrer">“A novel long non-coding RNA <em>linc-ZNF469-3</em> promotes lung metastasis through <em>miR-574-5p-ZEB1</em> axis in triple negative breast cancer”</a>. In this I see Table1 showing correlation of Linc-ZNF469-3 with different features in TNBC and non-TNBC patients. p-values are calculated based on Pearson's Chi-sq test.</p>

<p><a href="https://i.sstatic.net/puQ8D.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/puQ8D.png" alt="enter image description here"></a></p>

<p>For non-TNBC patients: I calculated the p-value with the Chi-sq test just to have a look if everything is right.</p>

<p>Recurrence:</p>

<pre><code>data &lt;- data.frame(x= c(40,62), y=c(56,65))
data
chisq.test(data, correct = F)

    Pearson's Chi-squared test

data:  data
X-squared = 1.1269, df = 1, p-value = 0.2884
</code></pre>

<p>Brain:</p>

<pre><code>data &lt;- data.frame(x= c(6,99), y=c(6,119))
data
chisq.test(data, correct = F)

    Pearson's Chi-squared test

data:  data
X-squared = 0.096461, df = 1, p-value = 0.7561
</code></pre>

<p>You can see that the Recurrence p-value doesn't match with what I see in the table. Others, like Brain, Bone, Liver, Lung match with the p-values I calculated.</p>

<p>And for TNBC patients, not a single p-value matches with my calculations. I don't even get Lung and Bone as having significant p-values with my calculations.</p>

<p>I did it for Lung as shown here:</p>

<pre><code>data &lt;- data.frame(x= c(25,143), y=c(9,35))
data
chisq.test(data, correct = F)

    Pearson's Chi-squared test

data:  data
X-squared = 0.8044, df = 1, p-value = 0.3698
</code></pre>

<p>But in the paper they mentioned:</p>

<blockquote>
  <p>We found that high linc-ZNF469-3 expression was significantly correlated to lung metastasis in TNBC patients (Table 1).</p>
</blockquote>

<p>Can anyone tell me what could be the wrong here? Is it my calculation wrong? or in the paper?</p>

CC BY-SA 4.0
2020-03-22T06:55:43.277Z
We found that high linc-ZNF469-3 expression was ignificantly correlated to lung metastasis in TNBC patients (Table 1). This conclusion  can not be reached. Metastasis across tnbc and non-tnbc require a different analysis e.g. t-test.
Subhash C. Davar
CC BY-SA 4.0
2018-10-09T11:07:43.110Z
Ouch. This probably warrants a correction or even retraction.
Konrad Rudolph
CC BY-SA 4.0
2018-10-09T11:31:14.167Z
Exactly. I have another question. Lets say Lung p-value in TNBC patients is significant. High linc-ZNF469-3 has 9 and low linc-ZNF469-3 has 25. How could one tell that high linc-ZNF469-3 expression was significantly correlated to lung metastasis in TNBC patients?
stack_learner
<rna-seq><r><gene-expression><correlation>
Why are my Chi-squared test results different from those in a published table?
# Why are my Chi-squared test results different from those in a published table?

(Asked by: username_0 on 2018-10-09)

I recently read the paper “A novel long non-coding RNA linc-ZNF469-3 promotes lung metastasis through miR-574-5p-ZEB1 axis in triple negative breast cancer”. In this I see Table1 showing correlation of Linc-ZNF469-3 with different features in TNBC and non-TNBC patients. p-values are calculated based on Pearson's Chi-sq test.

For non-TNBC patients: I calculated the p-value with the Chi-sq test just to have a look if everything is right.
Recurrence:
data <- data.frame(x= c(40,62), y=c(56,65))
data
chisq.test(data, correct = F)

    Pearson's Chi-squared test

data:  data
X-squared = 1.1269, df = 1, p-value = 0.2884

Brain:
data <- data.frame(x= c(6,99), y=c(6,119))
data
chisq.test(data, correct = F)

    Pearson's Chi-squared test

data:  data
X-squared = 0.096461, df = 1, p-value = 0.7561

You can see that the Recurrence p-value doesn't match with what I see in the table. Others, like Brain, Bone, Liver, Lung match with the p-values I calculated.
And for TNBC patients, not a single p-value matches with my calculations. I don't even get Lung and Bone as having significant p-values with my calculations.
I did it for Lung as shown here:
data <- data.frame(x= c(25,143), y=c(9,35))
data
chisq.test(data, correct = F)

    Pearson's Chi-squared test

data:  data
X-squared = 0.8044, df = 1, p-value = 0.3698

But in the paper they mentioned:

We found that high linc-ZNF469-3 expression was significantly correlated to lung metastasis in TNBC patients (Table 1).

Can anyone tell me what could be the wrong here? Is it my calculation wrong? or in the paper?

**Tags:** rna-seq, r, gene-expression, correlation
> **Comments:**
> **username_2**: Ouch. This probably warrants a correction or even retraction.  
> **username_0**: Exactly. I have another question. Lets say Lung p-value in TNBC patients is significant. High linc-ZNF469-3 has 9 and low linc-ZNF469-3 has 25. How could one tell that high linc-ZNF469-3 expression was significantly correlated to lung metastasis in TNBC patients?  
> **username_1**: We found that high linc-ZNF469-3 expression was ignificantly correlated to lung metastasis in TNBC patients (Table 1). This conclusion  can not be reached. Metastasis across tnbc and non-tnbc require a different analysis e.g. t-test.  

---

## 1 answers

### Answer by username_3 on 2018-10-09. Score: 2

Your calculations seem right, perhaps there was an error on their side.

I also looked at the number of samples reported, but they use the same amount of samples in each case. Because they are testing whether Linc-ZNF469-3 is different in several tissues, they should have used a multiple test correction for the p-value (although reporting the original values is usually also ok).

To answer your question in the comments. You can't correlate a categorical variable with a continuous variable. Or they did another test or they are not explaining clearly what they did.

> **Comments:**
> **username_0**: Thanks for the reply. For my question...I also had a look into some other papers. For eg: Check this table [https://www.nature.com/articles/s41598-017-04736-z/tables/1] They say in the paper like this; High RAD51-AS1 expression correlated with advanced FIGO stage, histological subtype, high histological grade, number of tumors and distant metastasis. I'm bit confused. Is it based on their percentage they say RAD51-AS1 expression is correlated with those variables?  
> **username_3**: @user3351523 They could show a plot of like FIGO stage and the expression of the variable, and see a linear trend towards higher expression on certain stages, but without comparing the mean or the percentage/proportion (and not a correlation) that could be noise or illusions.  

---

