When is better to pool samples?

Question

[ste.lu]

Hi All,

I was thinking about a pipeline to build but I have a doubt on how to set a step in it. I'll try to keep the concept as broad as possible because I am also interested in the math behind it and other applications.

I have lot of samples which have to go through the same process and, in the end, I want to pool all the samples together. What is the best way: a. pool them and send them through the process b. send them through the process and pool the results

what are the benefits and drawbacks of each?

Thank you

[GenoMax]

Without knowing full details of what you are trying to do b would be better since you are parallelizing your processing and can go through everything faster than a.

[ste.lu]

Completely agree with your answer in terms of computational power. But what about the Math/statistics behind it, are they exactly the same thing? It always depends on the task I am talking about?

[GenoMax]

It always depends on the task I am talking about?

Likely. If operations you are doing are independent of each other (e.g. splitting a billion sequence file into 100 chunks and starting 100 alignments against the same reference genome as opposed to one alignment job) then doing b would always be preferable/faster (as long as you have resources available) as the resulting alignments can be merged later. But if an operation is dependent on content (e.g. an assembly job where a sample was sequenced on multiple lanes/flowcells) then pooling the data would be required before starting the job to avoid biases.

I can't comment on the theoretical implications of a vs b but someone else may do that.