<p>FPKM/TPM values are generally log-normally distributed. Reference : <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1240081/" rel="nofollow noreferrer">Gene expression profiling in single cells from the pancreatic islets of Langerhans reveals lognormal distribution of mRNA levels</a></p>

CC BY-SA 3.0
2017-09-14T19:10:14.477Z
Sorry, I added a reference. I'm not sure why mRNA levels follow that distribution but it has been established. As they mention in the paper, it's a fairly common distribution in nature. On the other hand, 3' capture methods of single-cell RNA seq such as DropSeq usually yield a negative binomial distribution, presumably because of the high drop out rates of this method of transcript capture.
Alon Gelber
CC BY-SA 3.0
2017-09-17T17:03:12.953Z
Well done. The answer looks WAY better with the reference. If you would like to push it even further. This type of information you have written in the comment you can add to answer as well to give a complete picture.
Kamil S Jaron
CC BY-SA 3.0
2017-09-18T22:19:03.503Z
If some data follows a log-normal distribution, does it mean that it would follow normal distribution after you take the log of the values? That's not what I am seeing...
user5054
CC BY-SA 3.0
2017-09-19T18:20:37.977Z
It should...what does expression look like for a gene you know is expressed in your sample after you log normalize your data?
Alon Gelber
CC BY-SA 3.0
2017-09-01T14:31:20.233Z
2017-09-17T17:00:08.440Z
2017-09-17T17:00:08.440Z
Alon Gelber
<p>Cuffdiff is one the tools that deals FPKM data for analysis of differential expression.By default they use the negative binomial with some condition specific parameter to avoid biases. There are some other models also available in Cuffdiff. Check the following documents for more details,</p>

<p><a href="http://cole-trapnell-lab.github.io/cufflinks/cuffdiff/index.html#library-types" rel="nofollow noreferrer">http://cole-trapnell-lab.github.io/cufflinks/cuffdiff/index.html#library-types</a></p>

<p><a href="https://www.reddit.com/r/bioinformatics/comments/3bx3em/fpkm_vs_raw_read_count_for_differential/" rel="nofollow noreferrer">https://www.reddit.com/r/bioinformatics/comments/3bx3em/fpkm_vs_raw_read_count_for_differential/</a></p>

CC BY-SA 3.0
2017-09-18T22:19:54.080Z
FPKM data is not composed of integers, it is interesting that they are using negative binomial distribution.
user5054
CC BY-SA 3.0
2017-09-18T12:09:48.757Z
2017-09-18T12:09:48.757Z
arup
<p>I know that the discrete raw counts from the RNA-Seq data are usually modeled by a negative binomial or a Poisson distribution, but what I am working on are the FPKM (Fragments Per Kilobase of transcript per Million mapped reads) values which represent a transformation of the raw values. They are continuous except many actual 0s. What is the probability distribution FPKM values are likely to follow, or are assumed to follow? Even after taking logarithm twice (after replacing 0s with 1, of course), the data does not follow a normal distribution since there are a lot of 0s.</p>

CC BY-SA 3.0
2017-08-31T03:07:38.783Z
I think you may want to look into zero-inflated distributions?
Daniel Standage
CC BY-SA 3.0
2017-09-01T14:45:40.480Z
Could you show or provide some sample data to explain with? What have you read about limma, DESeq2, DESeq ?
llrs
<rna-seq><r>
What is the best distribution to model the FPKM values from normalized RNA-Seq data?
# What is the best distribution to model the FPKM values from normalized RNA-Seq data?

(Asked by: username_0 on 2017-08-31)

I know that the discrete raw counts from the RNA-Seq data are usually modeled by a negative binomial or a Poisson distribution, but what I am working on are the FPKM (Fragments Per Kilobase of transcript per Million mapped reads) values which represent a transformation of the raw values. They are continuous except many actual 0s. What is the probability distribution FPKM values are likely to follow, or are assumed to follow? Even after taking logarithm twice (after replacing 0s with 1, of course), the data does not follow a normal distribution since there are a lot of 0s.

**Tags:** rna-seq, r
> **Comments:**
> **username_1**: I think you may want to look into zero-inflated distributions?  
> **username_2**: Could you show or provide some sample data to explain with? What have you read about limma, DESeq2, DESeq ?  

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## 2 answers

### Answer by username_3 on 2017-09-01. Score: 5

FPKM/TPM values are generally log-normally distributed. Reference : [Gene expression profiling in single cells from the pancreatic islets of Langerhans reveals lognormal distribution of mRNA levels](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1240081/)

> **Comments:**
> **username_3**: Sorry, I added a reference. I'm not sure why mRNA levels follow that distribution but it has been established. As they mention in the paper, it's a fairly common distribution in nature. On the other hand, 3' capture methods of single-cell RNA seq such as DropSeq usually yield a negative binomial distribution, presumably because of the high drop out rates of this method of transcript capture.  
> **username_4**: Well done. The answer looks WAY better with the reference. If you would like to push it even further. This type of information you have written in the comment you can add to answer as well to give a complete picture.  
> **username_0**: If some data follows a log-normal distribution, does it mean that it would follow normal distribution after you take the log of the values? That's not what I am seeing...  
> **username_3**: It should...what does expression look like for a gene you know is expressed in your sample after you log normalize your data?  

---

### Answer by username_5 on 2017-09-18. Score: 0

Cuffdiff is one the tools that deals FPKM data for analysis of differential expression.By default they use the negative binomial with some condition specific parameter to avoid biases. There are some other models also available in Cuffdiff. Check the following documents for more details,

<http://cole-trapnell-lab.github.io/cufflinks/cuffdiff/index.html#library-types>

<https://www.reddit.com/r/bioinformatics/comments/3bx3em/fpkm_vs_raw_read_count_for_differential/>

> **Comments:**
> **username_0**: FPKM data is not composed of integers, it is interesting that they are using negative binomial distribution.  

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