Phyloseq Objects for different time points

Question

[ymj]

Hi

I am very new to this microbiome analysis so this might be a very simple question... I have a question related to the microbiome analysis I'm doing. I have microbiome data from 3 different timepoints (T1, T7, and T13), for I made each time a phyloseq object. For each of the timepoints there are the same number of samples (IAM01-IAM30) for which I have (for now) available: gender, age.

I now want to plot a t-SNE plot where I have colors per gender, age-group and timepoint but I don't know how to merge the phyloseq objects?

I made first a dataframe with the ASV in the columns and the samples in the rows, and then the metadata next to it, for each timepoint. This I would then merge, but the ASVs ofcourse vary (ASV1 for timepoint 1 phyloseq object is different from ASV1 in timepoint 7).

Can someone help me out? Ideally I would need to have this kind of dataframe (cell = relative abundances/counts)

Thank you very much in advance!

[andres.firrincieli]

I now want to plot a t-SNE plot where I have colors per gender, age-group and timepoint but I don't know how to merge the phyloseq objects?

Then, why did you analyzed each timepoint independently?

[ymj]

Hmm. I have a phyloseq object per time point seperately. I followed the DADA2 tutorial (starting from fastq files per sample, per time point) and ended up in this way in 3 different phyloseq objects. I still want to consider samples from different timepoints as being independent samples.

[andres.firrincieli]

this DADA2 tutorial?

[ymj]

Yes, in which I analyzed the timepoints seperately. So I'm thinking to do this DADA2 tutorial maybe again, on all fastq samples and then afterwards extract the timepoint?

[Ram]

ymj Do not delete posts that have received feedback.

Answer 1

[andres.firrincieli]

If you look at the tutorial, all samples are analyzed with DADA2. In the section Bonus: Handoff to phyloseq, the author construct a data.frame (samdf`) with the sample data:

samples.out <- rownames(seqtab.nochim)
subject <- sapply(strsplit(samples.out, "D"), `[`, 1)
gender <- substr(subject,1,1)
subject <- substr(subject,2,999)
day <- as.integer(sapply(strsplit(samples.out, "D"), `[`, 2))
samdf <- data.frame(Subject=subject, Gender=gender, Day=day)
samdf$When <- "Early"
samdf$When[samdf$Day>100] <- "Late"
rownames(samdf) <- samples.out

Then, samdf it is used here:

ps <- phyloseq(otu_table(seqtab.nochim, taxa_are_rows=FALSE), 
               sample_data(samdf), 
               tax_table(taxa))

to create a SINGLE phyloseq object ps and store all the information: Gender, Day, Subject, When regarding each sample

[ymj]

Thanks for your answer! I have now the results of the analysis on a single phyloseq object. :)

[ymj]

Thanks for your answer! I have now the results of the analysis on a single phyloseq object. :)

[Ram]

I've moved the comment to an answer. Please accept it to mark the post resolved.