I have RNA-seq data of purified cell type A (one sample, no replicate) and microarray data of a mixture (cell type A and B). I would like to perform gene expression deconvolution to estimate the proportion of each cell type and the gene expression of cell type B in the mixture. However, the datasets are based on different platforms. I need convert cell type A's RNA-seq data to microarray data before doing expression deconvolution. How to do this conversion?
What I mean is: As described in the limma package: page69, "In the limma approach to RNA-seq, read counts are converted to log2-counts-per-million (logCPM) and the mean-variance relationship is modelled either with precision weights or with an empirical Bayes prior trend. In either case, the RNA-seq data can be analyzed as if it was microarray data. "
What I concern: 1.I am not sure if I can use these two methods for my task, as they are designed for differentially expressed gene analysis. 2. I feel that "the counts are converted to logCPM values using edgeR’s cpm function" --simple log2 cpm trasformation seems not enough for my task. After doing this transformation, will I be able to treat the transformed data as they were from microarray? 3.If I use voom, I only have 1 sample, then the design matrix is 1?