Question: Statistical test for replicates
0
gravatar for JC
4 weeks ago by
JC0
JC0 wrote:

Dear all,

I have some libraries with its replicates. To know how similar are they (between replicates) I made a Venn diagram of unique sequences showing this results:

Worse

Having that I wondered if removing those sequences with one read would improve the diagram. The result of doing that is the following:

Better

Now I would like to know if there is enough similitude between both replicates. There is a statistical test that I could apply to know this? If not there is some method that I could apply to know if I should remove more sequences with more reads o leave it as it was before?

Thank you everyone

Edit: The libraries come from a smallRNA-seq and I mapped the reads against the genome before doing the Venn diagrams.

rna-seq • 199 views
ADD COMMENTlink modified 4 weeks ago by Michele Busby1.8k • written 4 weeks ago by JC0

Please see How to add images to a Biostars post and follow the guide to add images properly.

ADD REPLYlink written 4 weeks ago by Ram17k

What exactly do you want to test, i.e. what is the question of interest here and what do the different pairs represent?

ADD REPLYlink modified 4 weeks ago • written 4 weeks ago by Friederike2.0k
2
gravatar for Michele Busby
4 weeks ago by
Michele Busby1.8k
United States
Michele Busby1.8k wrote:

When you sequence small RNA you often get reads that are the degradation products of longer mRNA. These might be what you are seeing. It will be better or worse depending on the protocol. If it is just a size selection there will be a lot of junk. If you are looking for miRNA there is an enzyme that preferentially pulls out the phosphorylated (?) cap on the 5' (?) end of the molecule.

To test if the libraries are good do a scatter plot of the replicates against one another using the count of each small RNA in log space. i.e. point j is at the log of the count of reads mapping to small RNA j in replicate 1 against the log of the count of small RNA j in replicate 2.

If the counts are similar between the libraries you are good to go. If they are the technical reps it should be almost a straight line with some spread near the bottom. If they are biological reps it should look more dispersed.

ADD COMMENTlink written 4 weeks ago by Michele Busby1.8k
1
gravatar for Devon Ryan
4 weeks ago by
Devon Ryan82k
Freiburg, Germany
Devon Ryan82k wrote:

Making Venn diagrams of unique sequences is not going to be useful, just delete those and continue on with the actual analysis (e.g., mapping or assembling the files).

ADD COMMENTlink written 4 weeks ago by Devon Ryan82k

Hello Ryan,

I already map them (before doing the Venn diagram). But I would like to confirm that the aligned reads are reliable.

Thank you for your answer.

ADD REPLYlink written 4 weeks ago by JC0

If you want to ensure that the alignments are reliable then filter by a reasonable MAPQ.

ADD REPLYlink written 4 weeks ago by Devon Ryan82k

Hello,

Then is fine to trust in reads that only appear once in all the library of smallRNA-seq?

ADD REPLYlink written 4 weeks ago by JC0

The reads are what they are, don't try to fight with a little sequencer noise. If a particular transcript only gets 1 count in one sample it'll just get ignored anyway.

ADD REPLYlink written 4 weeks ago by Devon Ryan82k

Good job on the images, OP! I saw you trying a bunch of solutions and at last hit the right solution!

ADD REPLYlink modified 4 weeks ago • written 4 weeks ago by Ram17k

Sorry for not doing it before Ram and thank you for your advice.

ADD REPLYlink modified 4 weeks ago • written 4 weeks ago by JC0

No problem, JC. Thanks for putting in the effort!

ADD REPLYlink written 4 weeks ago by Ram17k
1
gravatar for b.nota
4 weeks ago by
b.nota4.6k
Netherlands
b.nota4.6k wrote:

A better approach to see if replicates are similar, is to quantify your reads per small RNA (with e.g., featureCount). Then calculate the pairwise correlation between all your samples. The technical replicates would correlate best with each other and would indicate that your experiment went well (technically).

ADD COMMENTlink modified 4 weeks ago • written 4 weeks ago by b.nota4.6k
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