I'm working with with DESeq2 to make a DE analysis between samples in two different conditions . After differential expression analysis I'm interested to perform two further steps:

1) I want to plot a heatmap showing the expression of the differential expressed genes in the different samples analyzed.

2) I'm interested to check a potential correlation (Spearman correlation) between DE genes expression and a specific variable.

Which type of normalization is suggested for these two type of analyzes? I'm currently using vst() normalization function to perform the correlation test ( cor.test() function) while using the normalized matrix retrieved with counts(dds, normalized=TRUE) to better highlight the expression differences inside the heatmap. Is my approach correct?
Thanks for the help.

See the workflow, I prefer VST for heatmaps.

https://www.bioconductor.org/packages/devel/workflows/vignettes/rnaseqGene/inst/doc/rnaseqGene.html#gene-clustering