I have two different data sets of RNA-Seq, after the RNA-seq analysis i found that, gene of my interest is showing FPKM value 0 (Diseased condition) first data set with fold change value 0 but in Second data set (Treatment data ) that particular gene is showing FPKM value as 265.756 with fold change 5.2.
So in terms of expression level considering FPKM value 0 in first data set , now how can i correlate the expression of that gene in Second data set in term of expression ?
Just by looking at the definition, a value of zero is expected when no reads are mapped (i.e., the number of fragments is zero).
For a discussion on low FPKM values see: https://www.biostars.org/p/6694/
In terms of expression, the gene is upregulated (considering the positive value of the fold change) in the second set (Treatment data) of data.
But I was not clear with you experimental design. What are you comparing the Diseased and treatment dataset to?And how many data sets are there in each condition?etc..
0 indicates that no reads were mapped. Thus, no observation/measurement was possible. I would not call that not expressed as this can have several reasons! Add your answer
@ Shuba Varshini Alampalli.. i would like to explain experimental design ... i have two different RNA-seq data... First one is Synovial fibroblast in Rheumatoid Arthritis and Second one is Treatment on Rheumatoid arthritis ... but the source of sample collection is different... here i m checking the gene expression in two different samples by considering the two different conditions....
so if number of fragments are zero, that means in that particular individual that gene is not present or its not expressed ??
Since you have mentioned the source of sample collection is different, I'm guessing you are treating each data set independently.
FPKM is one of the many simple normalization methods to remove the ambiguity of, for example, gene sizes and nucleotide composition of the genome, etc. in RNA-seq . It's basically adjustments where the intention is to bring the entire probability distributions (dataset) of adjusted values into alignment. While doing this, the method keeps one dataset as ideal and compares the other, so now the interpretations goes like the expression of the gene was upregulated/downregulated w.r.t the ideal dataset.
So now you should analysis how did you get the FPKM? I mean what data set did you use as control (ideal) or if you already had a control set and you are comparing it to the datasets you have generated now.
This paper :Li, Jun, et al. "Normalization, testing, and false discovery rate estimation for RNA-sequencing data." Biostatistics (2011): kxr031.---says---"RNA-Seq, like microarrays, is often used for comparative experiments, where expression measurements are collected for multiple samples, each of which is associated with an “outcome.” This outcome often takes 1 of 3 forms: (i) two class, such as tumor versus normal, (ii) multiple class, such as tumor type A versus tumor type B versus tumor type C, or (iii) quantitative, such as blood pressure."
I hope I haven't confused you and indeed helped you. :)
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