correlation analysis of a single variable versus multiple variables
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4 weeks ago

I have 6 folders. Each one contains 7 datasets of a specific type of cancer (RNA-Seq) and 7 datasets of normal tissue (healthy control for that type of cancer). A total of 84 datasets.

I want to investigate my gene of interest in each type of cancer.

I believe the best strategy is to merge the FPKM columns and perform correlation analyses of my gene of interest (GOI) versus all other genes in both cancer and normal tissues. As a result, I will have 14 correlation tables, one for each type of cancer and one for each control tissue. Then, I can investigate the pathways related to the genes most correlated with my GOI and write my biological interpretation.

Am I in the right direction? Is this a good strategy to answer to my question? My results will be strong enough for a publication?

I could compare the cancer and control datasets to investigate the DEGs and the pathways activated by the DEGs, but my gene of interest doesn't have a strong pattern of gene expression and I'm afraid it won't be in the list of the statistically significant DEGs, that's why in preferring the correlation analysis approach.

correlation r RNA-Seq • 176 views
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My results will be strong enough for a publication?

If you plan to publish a few correlation tables for a single gene then the answer is most likely no. Beyond that I doubt one can make a statement about a publication at a point where you are still planning the analysis, without having any results yet (I guess).

I find it hard to understand what the actual question is that you aim to answer. Can you elaborate?

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My gene of interest has been implicated in opposing functions. In some types of cancer, it is claimed to be protective, in others it seems to be deleterious.

The subject is poorly explored, the publications to date only show experiments aimed to evaluate specific pathways.

There is no large scale analyses, evaluating the pathways associated with this gene in different types of cancer.

Since it is a low expressed gene, it's difficult to find it in the list of DEGs in cancer (fold change > 1), so I believe that correlation analyses followed by enrichment analyses in several types of cancer can give me enough information to write a paper with interesting information.

Thank you very much for your attention.