An update (6th October 2018):
You should abandon RPKM / FPKM. They are not ideal where cross-sample differential expression analysis is your aim; indeed, they render samples incomparable via differential expression analysis:
Please read this: A comprehensive evaluation of normalization methods for Illumina high-throughput RNA sequencing data analysis
The Total Count and RPKM [FPKM] normalization methods, both of which are still widely in use, are ineffective and should be definitively abandoned in the context of differential analysis.
Also, by Harold Pimental: What the FPKM? A review of RNA-Seq expression units
The first thing one should remember is that without between sample normalization (a topic for a later post), NONE of these units are comparable across experiments. This is a result of RNA-Seq being a relative measurement, not an absolute one.
Hello BIOTECH.DEEPTI911!
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Dear Vijay,
Yes I completely agree that similar questions have been asked by others but the details they have provided is different form my question. I need the explanation for density plots so for this I have mentioned my work plan. It would be great if you will open this coversation.
Regards,
Deepti Mittal Ph.D. Research Scholar C/O Dr. Gautam Kaul Division of Biochemistry NDRI, Karnal.
I have opened the question again
Thank you Kevin for your comment. It is very helpful to increase my knowledge, but I want to know that what if I will take RPKM values instead of FPKM for DEG's analysis, would this approach be fine? Also I want to ask that I am using the topmost differentially expressed genes, is this approach is fine? I do not have much exposure of bioinformatics so I am little afraid of doing the normalization and all those steps again. I have done the RNA-seq through outsourcing and they have provided me FPKM values. I am in a big trouble as my guide is expecting a lot from me and I just got stuck at this point :(
No, RPKM is just as bad as FPKM for this purpose. You should tell the outsourcing company that FPKM is not suitable for differential expression comparisons.
Ok I should not use FPKM as this will be a bad decision. I will use the DEseq2 for differential gene expression for better analysis. Thank you so much to enlighten me. Your valuable suggestions helped me a lot :)
Okay, but, wait. For DESeq2, you will require the raw counts, i.e., the counts prior to normalisation. Do you have them?
Yes Kevin I do have the raw files i.e. Fasta files for my data, but I don't know how to analyse them:(
Deepti Mittal Ph.D. Research Scholar C/O Dr. Gautam Kaul Division of Biochemistry NDRI, Karnal.
If it is a preliminary study, then do not worry about it. What are you hoping to do with the results?
If an answer was helpful you should upvote it, if the answer resolved your question you should mark it as accepted.
Yes Kevin, I am just interested to find out the differentially expressed genes and major pathways involved for my treatment condition. Many authors have done the preliminary studies (that too with microarray) on that particular treatment but I am studying it in terms of global cellular outcomes by utilizing the power of RNA-seq. That is why I am majorly focusing on that part only, but the reason I am so much concerned about the major bioinformatics involved as I want to learn each and every part of the transcriptomics.
Okay, sounds interesting, and I imagine that the third-party company only provided data on protein coding RNAs? With RNA-seq, one has the ability to do a wide range of analyses:
Yes Kevin exactly. As I do have the raw files with me I can do wonder with this data. A whole lot of information. I just need to get into its deeper insights and that's the reason I am keen to look at each and every single part of it :)
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