Dear all,
I have got some differentially expressed (DE) lncRNA and DE coding genes from an RNA-seq analysis (human). Then, I did a co-expression analysis to get the co-expressed DE coding genes with DE lncRNA. As the RNA-seq data (CPM was used) was not normal, I used the Spearman correlation test. However, almost all coding genes showed the negative and the positive correlation with the various lncRNA. I did the GO and pathway analysis of coding genes that co-expressed with lncRNA to find the biological function of corresponding lncRNAs. But, as the coding gene list (for both negative and positive correlations) was almost the same, I could not obtain the distinct results for coding genes with the positive and negative correlation. For solving the issue, I tried to restrict the analysis to cis-lncRNAs, so I obtained the coding genes near the lncRNA (300 kbp, upstream and downstream), but these near coding genes showed both negative and positive correlation with various lncRNAs. Could you please kindly advise me in this regard?
Thanks
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seta
Hi Seta,
If I understand correctly, you have RNA-seq data in at least two conditions and you want to see if the change of expression in the protein coding genes is correlated (positively or negatively) with the change of expression of neighboring ncRNA.
I have recently done something similar in yeast with the focus on antisense ncRNA. In yeast, there is usually only one protein-coding gene associated with anti-sense ncRNAs. This 1:1 relationship allows to make a very simple analysis where you compare the log2FC between two conditions for the antisense ncRNA (y-axis in this case) with the log2FC of their sense protein coding genes (x-axis). In my example, this simple graphical analysis allows to see that anti-sense ncRNA tend to be up-regulated without much impact on their sense protein coding gene expression 
I think that the situation in human is probably more complex, but if you can work out a 1:1 relationship for ncRNA and protein coding genes (perhaps by splitting ncRNA into categories: upstream, antisense, downstream), then it will be easier for you to study and analyze their relationship with their protein coding genes.
Hope this helps,
Carlo
Hi Carlo,
Thanks for your explanation. Splitting ncRNA into different categories is a good idea, I also saw the ncRNA classification into 5 categories (intergenic, antisense, intronic, bidirectional, enhancer RNA). Could you let me know how I can annotate my lncRNA as the corresponding categories? the lncRNAs are as Ensembl ID format.
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