Given that, how different are the distributions? Say there are 3 conditions, where all samples were randomly assigned to. It would be fine to have one batch with 30/40/50 samples from each treatment (assuming they are run in a randomized order). However, it wouldn't be good to have a distribution of something like 50/0/60. Even worst would be 10/0/100.
I'm talking about the extreme case (e.g. 10/0/100). So is it correct to say that, even if the pooled QC was created from all of the samples in the experiment, the resulting data would have issues because of the unbalanced distribution of the samples (after correction, normalization, etc...)?
If this is the case, what happens during the experiment as a result of the imbalance that leads unusable/low-quality data?
Thank you so much for your feedbacks! Learning so much from you all
I am currently trying to upload an mzxml data file as a dataset on XCMS online, and whenever I try I keep on getting the message "Warning: There is/are zero file(s) to save."
Does anyone know why I am constantly getting this message, and how to fix this issue?
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It is always a good idea to randomize samples across batches. There are special cases where you might want to perform block randomization, such as what Jan described. I guess you could say that the goal is to make systematic/technical variation orthogonal to biological variation.
Given that, how different are the distributions? Say there are 3 conditions, where all samples were randomly assigned to. It would be fine to have one batch with 30/40/50 samples from each treatment (assuming they are run in a randomized order). However, it wouldn't be good to have a distribution of something like 50/0/60. Even worst would be 10/0/100.
For pooled QCs. It's a good idea to create the pooled samples and put them with the biological samples as soon as possible. This ensures they will capture as much technical variation as possible i.e. all the variation from sample storage, defrosting, extraction, running etc. This is especially important when batches are processed separately (extracted separately or stored in different freezers).
It's not an experiment where we have 'paired samples', so we are okay with respect to that.
I wonder if it would be okay if an experiment set up where:
There are multiple batches.
Within each batch, samples with different conditions are not evenly distributed.
A pooled sample consisting of small amounts of all samples in batches is created. QC samples are analyzed in addition to the samples in each batch (which ideally would allow for inter + intra batch drift).
After the batch correction (inter + intra) using QC samples, will the quality of the data be okay given that the distribution of the conditions were not even?
Last post by Jan Stanstrup -
Yes... If you have "paired samples" where you are not interested in the differences within the pairs it can reduce variance to put them in the same batch. For example if you have several persons you might consider having the same person in the same batch for all time course samples.
But as you say. Spread evenly conditions and randomize within the batch.
Use plenty of QC samples so that you afterwards can correct for potential intra- and inter-batch drifts.
I have been thinking about setting up an experiment with around 600 samples (3 batches of 200) with 3 different conditions. Would it be optimal to set up the batches such that the proportion of 3 different conditions are the same throughout the different batches?
Last post by johannes.rainer -
Hm, that's interesting. Could be that the error comes from smooth, not from pickPeaks. Could you try your code again with only pickPeaks (i.e. skip the smooth step)?
If so, we'll have to add the msLevel. parameter also to the smooth method -
