text: user12360: I've done a differential expression analysis with transcriptomic information (microarrays and RNAseq) from different datasets by applying for some of them t-test and for the rest Wald-test. I've got many coincidences for these significant genes so I want to recalculate my p-values (or my adjusted p-values, obtained by the BH method) using all these datasets. What is the proper method to do this (if possible)? I am completely new in this field so any additional information would be appreciated.
  
  user-1: 
  What is the proper method to do this (if possible)?
  
  Please use dedicated software for RNA-seq and microarray analysis and not naive tests such as the t-test. For microarray one commonly uses limma and for RNA-seq either DESeq2, edgeR or limma-voom, all are available as Bioconductor packages in R.
  
original_text: 12360: <p>I've done a differential expression analysis with transcriptomic information (microarrays and RNAseq) from different datasets by applying for some of them t-test and for the rest Wald-test. I've got many coincidences for these significant genes so I want to recalculate my p-values (or my adjusted p-values, obtained by the BH method) using all these datasets. What is the proper method to do this (if possible)? I am completely new in this field so any additional information would be appreciated.</p>
   -1: <blockquote>
  <p>What is the proper method to do this (if possible)?</p>
  </blockquote>
  <p>Please use dedicated software for RNA-seq and microarray analysis and <strong>not</strong> naive tests such as the t-test. For microarray one commonly uses <code>limma</code> and for RNA-seq either <code>DESeq2</code>, <code>edgeR</code> or <code>limma-voom</code>, all are available as Bioconductor packages in R.</p>
  