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.