RNAseq and microarray combined clustering

Question

[miesmi]

Hello

I have data from RNAseq and microarray and I want to make a HCA or PCA to determine grouping of data. I am wondering if anyone knows how I have to normalize rnaseq reads and microarray signals together.

Thaks in advance

[dsull]

In my opinion, it's worth trying (but be sure you have controls to assess whether clustering is due to rna-seq/microarray differences or biological differences).

Some ideas to reduce bias:

• Try quantile-normalizing your datasets.
• Try using MDS with non-parametric distance metrics (e.g. those based on spearman rather than pearson).

FYI, it's much easier to integrate microarray and rna-seq together when you compare downstream analyses (e.g. differential gene expression) rather than gene expression values.

[JC]

You should not, data is not quantified in the same way, therefore you will have a lot of bias.

[Papyrus]

There are tools such as the R package MOFA which have been developed to tackle this problem of low-dimensional reduction of different -omic technologies and kind of find out which of the data better explain the observed variability and such.

Answer 1

[miesmi]

Thanks to all,

after reading answers , I think that the better option will be calculate log fold changes in each plantform and then make correlation based analysis.