Filter Controls or Not to Filter Controls

[colinbrislawn (Colin J Brislawn)]

I really like the term 'cross-indexing' to differentiate issues on the sequencers from environmental 'cross-contamination.'

Given that it's hard to differentiate cross-contamination and true biological similarity, my lab has mostly focused on cross-indexing, as it's a tractable problem.

@Stream_biofilm

how would you calculate the correct minimum read count to use for an entire feature table?

From your monoculture positive control that you included in the run.

For example, if you have a batch of human samples, you may include a saltwater microbe as a positive control. After the run has finished, you can easily evaluate cross-indexing in two different ways.

1. Did my positive control end up in uman samples?
2. Did my most abundant Human reads end up in my positive control?
3. (Bonus!) Is my ASV denoising algorithm doing a good job resolving my single, known microbe in my positive control?

Based on these observed frequency of cross-indexing error, you can identify any samples that are massively contaminated, and also measure the baseline cross-indexing error.

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But how do you remove this cross-indexing error from all other samples? The solution that I think would work best is outlined by @ebolyen here:

@ebolyen

However, I wonder if you would be able to scale the control's counts to a sample's total frequency. It won't be perfect as there'll still be sequences which the control didn't see, but could have, if it had happened to have more sequencing depth, but that's a problem with a qualitative filter anyhow.

Given that filtering is inelegant, maybe it's better to leave in contamination and control for it statistically, as @mortonjt suggested:

@mortonjt

If you think that you are dealing with some sort of bias that is widely distributed across your samples, check out some of the compositional statistics available in skbio and gneiss.