Wed, 11/20/2013 at 10:29 AM
Customer is an ophthalmologist. He says he had RNA-seq experiments performed with a company and data analyzed in another company for a tissue and a cell line. He was left with an Excel sheet with some useful information and would like to go deeper with the data. He would like to create a defined list of RNAs. He wants to look within the data set at genes and how they are expressed in comparison to other genes within the same tissue and cell line, to help determine whether the cell line is a good representative model for the tissue.
Thu, 11/21/2013 at 5:33 AM
AccuraScience LB: RNA-seq data can preserve more precise quantitative expression information than microarrays. Could you explain more about the "defined set of RNAs" you want to build? Considering the large number of genes expressed in the samples (in a typical tissue or cell line, there could be ~6000 genes being expressed), coming up with this defined set could be a challenge. There will always be some genes that are highly expressed and others lowly expressed, thus comparing the expression levels across different genes might not bring a lot of benefit to defining this set (unless there are specific patterns of expression of multiple genes you would be looking at). What might work as an alternative approach is for us to categorize the genes based on biological processes or molecular functions (e.g., some genes are cardiovascular developmental genes; some are kinases), which might ease the process of defining the gene set. If we get to know more about the project, we might be able to inject more ideas about how bioinformatics might help. A remaining concern I have is, considering the large number of genes being expressed, there will always be some genes whose expression patterns differ between the tissue and the cell line. It won't be easy to set the bar for determining whether the cell line is a good enough representative model for the tissue. A classical example of similar types of studies is iPSCs vs ESCs comparison. It can get messy, and a conclusive determination could be hard to reach.
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Note: LB stands for Lead Bioinformatician. An AccuraScience LB is a senior bioinformatics expert and leader of an AccuraScience data analysis team.
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