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