<p>You should use normalized data. The function has a parameter slot=&quot;data&quot; that let's you select to access the normalized data.</p>
<p>Any reason for rounding to 1 decimal place here? I imagine that would have a substantial impact on the correlation you compute.</p>

CC BY-SA 4.0
2019-05-19T15:31:16.590Z
2021-05-12T01:18:59.403Z
2021-05-12T01:18:59.403Z
TimStuart
<p>I want to perform a correlation test between genes in on my single cell RNA seq data set. I perfomed the differential expression analysis using the <strong>Seurat</strong> version 2 package, after performing stages of normalisation, scaling, PCA, TSNE analyses and clustering. My correlation plot is far from what I expect and I want to know if the expression values obtained with <strong>Seurats</strong> V2's <strong>FetchData</strong> function (imputed, scaled, or raw) may have an effect on correlation analysis' outcome. This is what I did:</p>

<pre><code># load Seurat v2
library(Seurat)

# load corrplot package
library(corrplot)

# obtain expression values of the genes I want to test rom the Seurat object
expression_matrix &lt;- FetchData(object, vars.all = c("gene1", "gene2", "gene3"))

expression_matrix &lt;- round(expression_matrix , 1)

cor_expression_matrix &lt;- cor(expression_matrix)

corrplot(cor_expression_matrix , is.corr = T, method = "square")
</code></pre>

<p>Which gene expression values should be best used for the analysis of correlation between genes: imputed, scaled, or raw?</p>

<p>Thank you in advance for your kind response.</p>

<seurat><single-cell><correlation>
Which values from Seurat::FetchData function are to be used for correlation analysis between genes?
# Which values from Seurat::FetchData function are to be used for correlation analysis between genes?

(Asked by: username_0 on 2019-05-15)

I want to perform a correlation test between genes in on my single cell RNA seq data set. I perfomed the differential expression analysis using the Seurat version 2 package, after performing stages of normalisation, scaling, PCA, TSNE analyses and clustering. My correlation plot is far from what I expect and I want to know if the expression values obtained with Seurats V2's FetchData function (imputed, scaled, or raw) may have an effect on correlation analysis' outcome. This is what I did:
# load Seurat v2
library(Seurat)

# load corrplot package
library(corrplot)

# obtain expression values of the genes I want to test rom the Seurat object
expression_matrix <- FetchData(object, vars.all = c("gene1", "gene2", "gene3"))

expression_matrix <- round(expression_matrix , 1)

cor_expression_matrix <- cor(expression_matrix)

corrplot(cor_expression_matrix , is.corr = T, method = "square")

Which gene expression values should be best used for the analysis of correlation between genes: imputed, scaled, or raw?
Thank you in advance for your kind response.

**Tags:** seurat, single-cell, correlation

---

## 1 answers

### Answer by username_1 on 2019-05-19. Score: 0

You should use normalized data. The function has a parameter slot="data" that let's you select to access the normalized data.

Any reason for rounding to 1 decimal place here? I imagine that would have a substantial impact on the correlation you compute.


---

