I already put all my dada into the QIIME2, and finished alpha rarefaction and alpha diversity. Now I want to compare the difference of alpha diversity in different group. I set a column, include all my goups, in metadata table. But I only want use some of groups in this cloumn. How can I operate this ?
Filtering data？ But，if I operate Metadata-based filtering，I should input feature table rather than rarefied_table， is it right？So, the alpha diversity will change.
Please help me. I want compare the differeance of the diversity both all of the groups and part of the group in a cloumn of metadata.
I resolved the problem seemingly, throug the reconstruction of the metadata. I added a new column, including samples which I need to compare. And,some samples, which I dont want to analysis , I let it empy in the cloumn. Is right?
[quote="YuZhang, post:2, topic:17741"]
throug the reconstruction of the metadata. I added a new column, including samples which I need to compare. And,some samples, which I dont want to analysis , I let it empy in the cloumn.
I use this way to display the difference of alpha diversity, but I can't use this measure to display beta diversity pcoa. The empty blank in new added clomn display as "nan". Even if I can hide them, the samples I want to compare takes up too little space. So what can Ido?
@YuZhang, I think a lot of these questions boil down to “what questions are you trying to ask of your data”, and many of them have been addressed previously on this forum.
A quick search for “diversity filter” brings up some great posts, including this fantastic discussion. Give it a read!
Below, find a couple of additional thoughts.
You have noticed that making some of your samples invisible does not produce the result you would expect if you made an emperor plot of filtered data.
PCoA results are calculated using all of the samples provided, and each sample’s projection on the plot is influenced by all samples in the data, not just the visible samples. As such, you should be careful when making some of your samples invisible. This can be a useful tool for simplifying your view of the data, but must be explained very clearly when sharing results; a PCoA projection of all samples with some of them invisible is not the same as a PCoA projection of only the visible data, and can easily be misinterpreted.
Probably not, but again, this depends on your data and the questions you’re trying to answer. Rarefaction normalizes data to reduce bias caused by samples with varying depths (i.e. total counts). This specific method of normalization may not be necessary, depending on your study and what diversity metrics you’re using, but some normalization is likely required.
Thank you , sir. I want to use the rarefaction normalizes data to compare the difference of alpha diversity in some group of my samples. That's mean I want a fig A1 including all groups in all my samples ,and fig A2,fig A3...including different groups in partial samples,respectively. Also , I want a pcoa graph B1 including all groups in all my samples ,and fig B2,fig B3...including different groups in partial samples,respectively..
I have read this post. I used the result of qiime diversity alpha by import the rarefaction normalizes data rarefied_table.qza .from core-metrics-phylogenetic . And, I change the metadata form as below:
So my alpha diversity can be compared separately in different group.
filtering and re-analyzing will be more useful if, e.g., you do the total analysis and see differences between some groups but your PCoA plots are a nasty tangled ball… then you could filter and re-run with subsets for ease of visualization.
make me realized I couldn't separate a PCOA graph into groups through metadata changes.
So,do I need to filter the feature data accroding the groups first ? Then, I should rarefied the data again through set the second sampling depth of core-metrics-phylogenetic to obtain ob rarefied_table-2.qza? But does these two sampling depth (first time is for alpha and second for beta pcoa) should to be same ?
And ,I know the process of rarefaction normalizes data is random,even if with the same depth I cant get the same result,right? So, I wanna know that alpha diversity and beta diversity (pcoa) are not using the same standardized data is that a problem?
If you want to calculate beta diversity and plot PCoA plots of subsets of your data, you will need to filter the “unwanted” data out - this is probably easiest to do by filtering a FeatureTable before you create your distance matrices.
Is there a particular reason you want to rarefy your data again? As you suggest here…