Tweet
I have got FPKM and some genes have really low FPKM and I am going to do stat analysis for all the genes; my question is the analysis should be like microArray analysis and need some cut-off numbers? any suggestion for this?
-
-
You could implement an approach like the one described in this paper: http://www.ploscompbiol.org/article/...l.pcbi.1000598 in order to estimate the background RPKM/FPKM and arrive at a cutoff. It's based on looking at false positive and false negative rates based on a set of known genes and a set of control regions for which there is no evidence that they are being expressed.
-
Love this - good data definitely starts from good input, and poor input can only give relatively poor data. I particularly like the mention of Nanodrop/absorbance based methods for quantification. It's such a toss up if you'll get an accurate reading or what amounts to a randomly generated number, and a lot of library/sequencing related issues can be traced back to poor quant.07-01-2026, 11:43 AM
-
I’m not a sequencing expert. I’m a purification scientist who uses NGS to evaluate workflows my group develops. With this perspective, we think about the sample first and the NGS workflow second. The sequencer is an exceptionally honest reporter, but it can only report on what you give it, so whether you get clean, interpretable data from an NGS workflow is largely determined before you begin.
Here are nine questions we think about, in roughly the order they matter, before...
Comment