Entering edit mode
8.3 years ago
z991ace
•
0
I am working on cancer related gene expression data from RNA-seq(RPKM). What I want to do is finding some important gene from gene expression data set, such as genes highly related to tumor recurrence.
But I found that still most of paper working on this kind of research used data set from Microarray, instead of RNA-seq. It has been long time since RNA-seq introduced, and as I know, RNA-seq data is generally less noisy than that of Microarray. Also, now it seems to be enough(although much less than Microarray) RNA-seq data is available, at least for tumor analysis.
Is there any other reason for using Microarray instead of RNA-seq? Are there some critical cons in using RNA-seq gene expression data?
I think RNA-Seq still costs more per sample that microarrays. Choosing method for identifying differentially expressed genes can be a bit of a hassle as well - see this article for more details.
Sorry for insufficient question. I just meant the step 'analysing data', which do not need to consider about the cost. There are many RNA-seq tumor-related gene expression data publicly available, most data of analysis is based on microarray even recently. I think it is better to use 'less noisy' data for analysis if the number of samples are sufficient for test. I wonder whether there are some points to make RNA-seq data analysis difficult.