![]() More severe involvement may lead to progressive central neurologic deficits (dysarthria, ataxia, cranial nerve palsies, cognitive impairment) or to ischemic injury to the kidney, intestine, and/or digits. Hypertension and hepatosplenomegaly are often found. Vasculitis, which usually begins before age ten years, may manifest as early-onset ischemic (lacunar) and/or hemorrhagic strokes, or as cutaneous or systemic polyarteritis nodosa. Inflammatory features include intermittent fevers, rash (often livedo racemosa/reticularis), and musculoskeletal involvement (myalgia/arthralgia, arthritis, myositis). For example, I prefer to filter out from the dataset any bacterial ASV with overall frequency less than 100.Adenosine deaminase 2 deficiency (DADA2) is a complex systemic autoinflammatory disorder in which vasculopathy/vasculitis, dysregulated immune function, and/or hematologic abnormalities may predominate. If you are comparing your results with other studies with ASVs and the same type of samples, and still your alpha diversity looks to be unusually high, chech if you performed filtering step to get rid of rare ASVs. So alpha diversity lowered to the literature values, but all significant differences in alpha diversity between my groups of samples were unchanged, so I just sent it as a response and paper was accepted with ASVs. ![]() When I submitted the manuscript with ASVs one of the reviewers commented that my alpha diversity is higher than in the literature, and I partially reproduced my analysis with OTUs. The same alpha diversity metrics will be different between OTU and ASV based studies, although the patterns and significant differences between samples within the study may be the same or similar. Many authors performed their studies by clustering reads to 97% OTUs, meanwhile in Qiime2 after denoising you will get an ASV table, wich is different from OTUs. ![]() When comparing data from the literature with your analysis, performed in Qiime2, pay attention to the pipelines used in the literature. Qiime feature-table rarefy -i-table table.qza -p-sampling-depth 20000 -o-rarefied-table rarefied_sampleĬan anyone help me to understand what's going wrong? To compare the alpha diversity between public data and my data I have sampled the table with: When I remove it, the values decrease but remain above expected (Very similar to that present in HMP reads). I include the parameter "-p-min-fold-parent-over-abundance 3" because qiime was removing >60% of the reads as chimeras. i-demultiplexed-seqs paired-end-reads.qza I think the most likely step to have problems is the denoising step: So I think something in my script in causing this. To check if the problem comes from sequencing, I run the analysis with a sequencing file from HMP and, despite having lower values than mine (simpson: 0,985 shannon: 7,5), they are still higher than expected. But when I analyse my data from Illumina reads (V3-V4 2x300 Median Quality for all positions > 32), I found values very different from this to all my samples (simpson > 0.99 shannon > 9). This values found analysing public data are very similar to literature. biom files from HMP (>400 samples) and found the mean values: ![]() So, to don't spend much time analysing public data, I downloaded. I'm analysing Human gut microbiome samples and I want to compare alpha diversity metrics to values found in literature. ![]() I started working with microbiomes a short time ago, so I don't have much knowledge about it and this is my very first analysis on Qiime2. ![]()
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