Use of rarefacted Table

[m_max]

Hi, I have a question regarding the use of rarefacted tables. I know that such tables are commonly used only for diversity analysis while are not used as base for other analyzes like differential abundance or network construction. By reading the paper of Swiis and colleagues (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5335496/) it seems that the use of rarefacted tables is not a bad choice cause it not lead to an increase of FDR while it should be adopted in cases in which groups with large (~10×) differences in the average library size are present.

I would like to ear some considerations from the community

[jwdebelius (Justine Debelius)]

Hi @m_max,

Welcome!

There's been a fair bit of discussion around rarefaction here (or maybe Im just involved in a lot of them). It's maybe good to go look into those threads since they'll give you a wide variety of view points. Here are a few to start that I found pretty quickly.

How to normalize bacterial abundance for unequal sample size?

Dear All, Ive executed QIIME2 analysis for my four different group. Each group consists of unequal sample size. Group A: 11 Group B: 9 Group C: 31 Group D: 12 and the sequence counts of each sample varies from 182,014 to 4,194. And I got more abundance in Group C for most of the taxa. In which way I can normalize the samples and get the abundance of taxa? Should I do any FPKM/RPKM or log2 normalization method ? Is there any R or some other tools to normalize OTU abundance.

Rarefraction not advantageous to detect differences in beta-diversity

Hi @Alfred.Burian, Thanks for bringing this up! Without wading too far into this debate, I will point out that rarefying is not always inadmissible, particularly for unweighted UniFrac and other beta diversity methods based on presence/absence of features. This is actually also shown in Fig 4 of the article that you posted, though for other distance methods other normalization strategies perform better. Second, what you describe is the qiime diversity core-metrics-phylogenetic method, which r…

Making sense of diversity estimates

What would you make of a dataset that, when you run said OTU table through Vegan to calculate distances using defaults (Method==Bray, Binary = FALSE) and depending on whether you filter based on per-sample ASV abundances, you get very different pictures? For example: Data is processed with Cutadapt and DADA2 defaults, then... Situation-1. incorporate all reads of any abundance > 1 Situation-2. Require per-sample ASVs abundance > 100 Now calculate distances, and run metaMDS... The distances…

Filter by group or entire dataset?

In general, would it be advisable to filter by group if your dataset contains multiple sample types? For example, when working with data from one sample type, I typically filter features that are not found in >95% of samples. I’d like to do this same type of filtering but the dataset I’m working with now is a composite from multiple body sites and environmental samples. All were run on the same lane, so I know that cross-talk might be a problem, and I can handle that by filtering features that …

Are there alternatives to rarefying for alpha diversity estimation?

Hi Nick ! I disagree with the use of a rarefied table for alpha diversity estimation. Is there a better explanation why one should use a rarefied table for alpha-diversity estimates?

Alpha/beta diversity: filtering datasets, additional metadata post analysis and missing metadata values

Hi Qiime community, I am preparing to run the alpha and beta diversity metrics on my 16S V4 dataset but had a couple questions about the set up to answer the questions of my study. My study consists of sampling bacteria populations in lake water samples across 3 lakes and from 3 size fractions per lake ( whole water, >20um and <20um). Each lake was sampled once a week for several months. First I would like to look at how the diversity of the bacteria populations vary between lakes and between…

Best,
Justine

[m_max]

Hi Justine,
thanks for your reply. I read the posts you suggest but my doubts still remain. In particular, a didn’t find some clear explanation about why should be not admissible to use a rarefacted table as a base for downstream analyzes like differential abundance testing or network construction. In the paper published by Swiis, it is only reported that rarefaction could lead to loss of sensitivity but not in an increased probability of false positive results. By searching the forum, I found a post (Filter Feature Table) in which @Nicholas_Bokulich strongly discourage to perform rarefaction before differential abundance testing but no reasons were provided. Furthermore, I performed my last analysis on both rarefacted and not rarefacted tables obtaining similar results. what do you think about?

[jwdebelius (Justine Debelius)]

HI @m_max,

McCurdie et al and Weiss et al clearly demonstrates an increased false positive rate with kruskal wallis testing on rarified data. Id suggest reading both papers more closely. Furthermore, as discussed in several of the posts I linked, microbiome data is compositional (I suggest reading the links in the first answer). Analyses that don’t consider compositionality are therefore methodologically inappropriate and therefore should be avoided where possible. You may have gotten lucky with incorrect methods (a stopped clock is right at least twice a day), but its still not an appropriate method. Id recommend q2-composition as a good starting place for correct analysis of differential abundance data.