[pinin4fjords]

Hi all,

I just wanted to add a +1 for some of the issues mentioned in this thread with Cufflinks 2. My data are from an Illumina 78bp paired-end protocol, with 7 and 5 replicates in two conditions.

I was previously using 1.3, with a command like:

cuffdiff -c 0 -p 4 -u -b <GENOME_FILE> -o <OUT_DIR> -L <LABELS> -M <MASK FILE> --library-type fr-unstranded merged.gtf <CONDITION 1 CUFFLINKS OUTPUTS> <CONDITION 2 CUFFLINKS OUTPUTS>

I was already using -c 0, since I have genes that are on/off between conditions and were being excluded due to low read counts in the 'off'. This produced results that seemed plausible, but I like the sound of the quoted improvements in 2.0- particularly the improved reporting of FPKMs.

Cuffdiff 2.0 with the same options produced the extreme numbers of NOTESTs described above. After reading this thread I first set --min-outlier-p to 0, which did not solve the problem. Then I removed -b (--frag-bias-correct), which produces results closer to (though even more conservative) than 1.3.

My conclusion: this beta not yet ready for widespread use.

[Cole Trapnell]

Hi all,

I just wanted to add a +1 for some of the issues mentioned in this thread with Cufflinks 2. My data are from an Illumina 78bp paired-end protocol, with 7 and 5 replicates in two conditions.

I was previously using 1.3, with a command like:

cuffdiff -c 0 -p 4 -u -b <GENOME_FILE> -o <OUT_DIR> -L <LABELS> -M <MASK FILE> --library-type fr-unstranded merged.gtf <CONDITION 1 CUFFLINKS OUTPUTS> <CONDITION 2 CUFFLINKS OUTPUTS>

I was already using -c 0, since I have genes that are on/off between conditions and were being excluded due to low read counts in the 'off'. This produced results that seemed plausible, but I like the sound of the quoted improvements in 2.0- particularly the improved reporting of FPKMs.

Cuffdiff 2.0 with the same options produced the extreme numbers of NOTESTs described above. After reading this thread I first set --min-outlier-p to 0, which did not solve the problem. Then I removed -b (--frag-bias-correct), which produces results closer to (though even more conservative) than 1.3.

My conclusion: this beta not yet ready for widespread use.

We are testing a pre-release that fixes the issues reported so far, at least in our hands. Can you try:

http://cufflinks.cbcb.umd.edu/downloads/cufflinks-2.0.1.Linux_x86_64.tar.gz

(No mac build or source yet - we were only able to reproduce this with our pre-compiled linux binary anyways)

[pinin4fjords]

We are testing a pre-release that fixes the issues reported so far, at least in our hands. Can you try:

http://cufflinks.cbcb.umd.edu/downloads/cufflinks-2.0.1.Linux_x86_64.tar.gz

(No mac build or source yet - we were only able to reproduce this with our pre-compiled linux binary anyways)

Okay, will do. Do you still recommend the min-outlier-p option? Have corrections been made to Cuffdiff only, or is it necessary to re-run the whole Cufflinks sequence?

[NicoBxl]

I've exactly the same problem with the -b param.

[glados]

Cole, can you please take a look at this boxplot of one of the genes. I don't think the error bars are due to variances in my samples.

[Cole Trapnell]

Hmm, we see that sometimes when one or more of the libraries have extremely low sequencing yield, for example. Can you show me a csDendro plot with replicates=T? It's important to verify that the replicates for each sample really segregate together. It might also be helpful to see a dispersion plot for each condition. If you're worried about revealing sample names you can email it to [email protected].

[gesdy]

Hi Cole,
I used the cuffdiff version you posted, and now it works.
I repeated the same analysis with the new version and the 1.3 (cuffdiff)
I got 43 genes called significant with cuffdiff2 and 940 with cuffdiff1.3
I have just one replicate for each conditions.
Do you think it's normal?
here my command line:

cuffdiff -b genome.fa -p 10 -u genes.gtf

any suggestions?
thank you very much!
Mat

[pinin4fjords]

I also confirm that the numbers produced using the new version at #32 look more sensible (using -b and min-outlier-p).

[Cole Trapnell]

Hi Cole,
I used the cuffdiff version you posted, and now it works.
I repeated the same analysis with the new version and the 1.3 (cuffdiff)
I got 43 genes called significant with cuffdiff2 and 940 with cuffdiff1.3
I have just one replicate for each conditions.
Do you think it's normal?
here my command line:

cuffdiff -b genome.fa -p 10 -u genes.gtf

any suggestions?
thank you very much!
Mat

Hi Mat,

That sounds fair to me - Cuffdiff 2 is far more conservative at the gene level than was 1.3 (as detailed on the site), and with a single replicate per sample, even more so. This isn't too surprising to me.

[Cole Trapnell]

I also confirm that the numbers produced using the new version at #32 look more sensible (using -b and min-outlier-p).

That's excellent - thanks to you and others in this thread for the helpful feedback. The official release of 2.0.1 is imminent.

[sdodson]

Hi,

I'm a novice in RNA-seq analysis, and I have a question regarding Cuffdiff...

We generated our RNA-seq data using the Illumina HiSeq2000, and have been following the data analysis protocol for Tophat, Cufflinks, ect. from Nature Protocols. We are currently using Tophat v1.4.1 and Cufflinks v1.3.0. Our Cuffdiff output shows 10371 of 27080 genes are NOTEST. In an effect to decrease this value, can we re-run our data through the newest version of Cuffdiff without first re-running it through the newest version of Cufflinks? Or is something else we can change to minimize the number of NOTEST genes? (such as using -c 0 ?)

Thanks!

[billstevens]

That's excellent - thanks to you and others in this thread for the helpful feedback. The official release of 2.0.1 is imminent.

Hi Cole,

Thanks for all the notes. However, I'm getting the same issue here. I have 46,000 reads that test, and 40,000 that have NOTEST. I am using the -b option, but not -c 0, and not the multi-read correct or frag bias. I'm using Cufflinks 2.0.1 and Tophat 1.4. Was the shortcoming in regards to -b addressed in the newest Cufflinks?

[MeixiaZhao]

Hello,
I used the latest version cufflinks 2.0.2, comparing with my previous results from 1.3.0, the results are unbelievable:
Gene level: I got 340 significant genes with cuffdiff2 and 2692 with cuffdiff1.3;
Isoform level: I got 272 significant isoforms with cuffdiff2 and 11380 with cuffdiff1.3;
My samples are single replicate.
Any solutions to judge the results? Or which version should I use for later analysis?
Thanks a lot!

[billstevens]

Hello,
I used the latest version cufflinks 2.0.2, comparing with my previous results from 1.3.0, the results are unbelievable:
Gene level: I got 340 significant genes with cuffdiff2 and 2692 with cuffdiff1.3;
Isoform level: I got 272 significant isoforms with cuffdiff2 and 11380 with cuffdiff1.3;
My samples are single replicate.
Any solutions to judge the results? Or which version should I use for later analysis?
Thanks a lot!

I've spoken to Cole about this extensively, and he says the new version is just much more accurate. It is an absolute shock going from one to the other.

[jwaage]

Hi all; I too, have some similar problems - I'm running the newest cuffdiff (2.0.2) on a two-sample, two-replicates illumina run using

cuffdiff --mask-file xxx.gtf -upper-quartile-norm

genes.diff and isoforms.diff are fine (although not many are called DE), but splicing.diff and promoters.diff only have NOTEST, LOWDATA or FAIL.

Any suggestions? I've tried running with -c 1, but to no avail.

All the best,
Johannes Waage
Uni of. Copenhagen