A Hat In Time Beta Download
Cynthia Figarsky
Beta is a time signal service in the VLF range in Russia, operated by the Russian Navy.[1] It is controlled by All-Russian Scientific Research Institute for Physical-Engineering and Radiotechnical Metrology.[2] There are 6 transmitter stations, which take turns transmitting time signals and other communications.
The time code consists of a series of signals on multiple frequencies. Transmission starts on the hour. Each time a new frequency is selected, there is 1 minute of low power while the transmitter is adjusted, then full-power transmissions begin.
I am trying to find an appropriate command for running beta diversity data (Bray-Curtis) over time. Currently, the command in the Qiime2 tutorial covers just two-time points. Would you please let me know if there is any command that can handle beta diversity data more than two-time points (three or more time-points)?
Hello @Barandouzi,
I am not sure what tutorial you are referring to but beta-group-significance can be run on any number of "Groups"(In this case time points). However you might be able to use beta-correlation if your timepoint metadata is numeric.
Thanks for your answer. I looked at the link you sent, but there is no option that we can put time or timepoints as a variable in the commands. I am interested in knowing how beta diversity changes among groups (not paired) over time (e.g., T1, T2, T3).
Thanks for your reply. The tutorial that you cited is what I am looking for!
I have one more question. In the tutorial, the outcome variable is beta diversity. How can I put beta diversity as predictor or independent variable in the model (mixed effect model)?
On top of q2-longitudinal which is a fantastic tool that comes pre-packaged with the core QIIME 2 install, there's also an additional plugin you can try for longitudinal microbiome data that I personally find super useful: q2-gemelli.
Thanks for your answer. I had a quick look at the link that you provided. In their results (below figure), beta diversity has not been considered as an independent variable since we do not have a p-value for it in the table. If we want to have beta diversity as predictor/independent variable in the model, what we should do?
Hi @Barandouzi,
Yes, I didn't mean to suggest that the plugin would be answering your question about placing beta diversity as an independent variable, only that it is a very useful tool for longitudinal microbiome data analysis. The answer to your question is a bit more complicated.
The first thing is, from an experimental design perspective, it would be pretty hard to manipulate "beta diversity" in a way that it would be a true independent variable, which is probably why in most tools the expectation is that you are trying to look at changes in beta diversity in response to some other signal. Can you describe your specific design a bit for us, would certainly help us guide to what you can maybe do.
Beta diversity describes the (dis)similarity between 2 communities, so, unlike alpha-diversity it doesn't have a "within" sample definition. One possible option would be to take the values of Axis 1 or Axis 2 from your PCA/PCoA plot and treat those as "independent variables" scores, then you could add those to your regular linear regression formula. The limitation is that these would be based on reduced data and may not necessarily catch the signal you may be interested in.
Thanks for your answer. I have a longitudinal design with three time points (same subjects) and I would like to know the relationship of beta diversity (independent variable) and education (independent variable) with fatigue (dependent variable) over time (fatigue = beta diversity + education). As you mentioned, it is easy to run this model (mixed effect model) for alpha diversity but not for beta diversity. I have also the same issue for taxonomy analysis. I appreciate any potential option/solution for longitudinal analysis when microbiome data (diversity and taxonomy) are independent variables.
This kind of really depends on how you've processed your data so far and what tool you want to use downstream. Are you planning on adding Ruminococous as a covariable with some other variables? Or are you interested in seeing what taxa in general are associated with your response variable? There are a ton of tools out there for doing this kind of differential abundance testing, several which have QIIME 2 plugins. I once put a bunch of these tools in a spreadsheet, this is by no means comprehensive but maybe of help.
One thing you should be aware of that with microbiome data it is not so straight forward to just grab the relative abundance value of one taxa and add it in your model. See discussion on this topic here, here, and here for example. You'll want to carefully pick an appropriate normalization step, or select an appropriate ratio of taxa.
Also, since you were asking specifically for data at the genus level. In QIIME 2, you'll first want to collapse your OTU/ASV table down to the genus level using the qiime taxa collapse action. This gives you a new feature-table that is now collapsed at the genus level. You can then use this table to extract the specific genus you are interested in. There are a lot of ways of extracting the specific genus, but that also depends on what plugin or platform you are planning on using. So if you can give us some more details about what you are trying to do we can help you get there. I personally would just export this table into R and do all my customization there but not sure what you will be doing specifically.
The new beta dependency column has broken all of my boards! I am in real estate and have workflows for everytime a client goes under contract. We have boards set with dependencies set to strict. In the past all we do is create a new board and change the start date at the top and all our dates autopopulate. From there, since every board is different we then change individual dates when needed and have been able to do so. With the change last night I can no longer change dates without changing to flexbile or no action. I need a work around for all our boards!!! Is there a way to not use the beta dependency column with the lag dates and keep the old version that worked??
Having the same issues, this has to be the absolute WORST update they have released. On top of that there is no way to turn it off or revert back to how it was previously?! This is making all our boards absolutely useless and we pretty much have no use for Monday.com platform anymore if this is going to be the case moving forward. Our company is at a standstill with timelines right now its so frustrating.
I understsand that could be a work around but changing the lag is WAY more cumbersome. I am at least a little tech-y and love process systems but my team is not. I have been working hard to simplify the boards for them so it is easier to use/change dates. But I am not going to be able to get them to go in find the dependency column, open it find the lag, then look at the date vs the date you need it to be then + or - days. We need the solution to change the date.
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Biomarkers of brain Aβ amyloid deposition can be measured either by cerebrospinal fluid Aβ42 or Pittsburgh compound B positron emission tomography imaging. Our objective was to evaluate the ability of Aβ load and neurodegenerative atrophy on magnetic resonance imaging to predict shorter time-to-progression from mild cognitive impairment to Alzheimer's dementia and to characterize the effect of these biomarkers on the risk of progression as they become increasingly abnormal. A total of 218 subjects with mild cognitive impairment were identified from the Alzheimer's Disease Neuroimaging Initiative. The primary outcome was time-to-progression to Alzheimer's dementia. Hippocampal volumes were measured and adjusted for intracranial volume. We used a new method of pooling cerebrospinal fluid Aβ42 and Pittsburgh compound B positron emission tomography measures to produce equivalent measures of brain Aβ load from either source and analysed the results using multiple imputation methods. We performed our analyses in two phases. First, we grouped our subjects into those who were 'amyloid positive' (n = 165, with the assumption that Alzheimer's pathology is dominant in this group) and those who were 'amyloid negative' (n = 53). In the second phase, we included all 218 subjects with mild cognitive impairment to evaluate the biomarkers in a sample that we assumed to contain a full spectrum of expected pathologies. In a Kaplan-Meier analysis, amyloid positive subjects with mild cognitive impairment were much more likely to progress to dementia within 2 years than amyloid negative subjects with mild cognitive impairment (50 versus 19%). Among amyloid positive subjects with mild cognitive impairment only, hippocampal atrophy predicted shorter time-to-progression (P < 0.001) while Aβ load did not (P = 0.44). In contrast, when all 218 subjects with mild cognitive impairment were combined (amyloid positive and negative), hippocampal atrophy and Aβ load predicted shorter time-to-progression with comparable power (hazard ratio for an inter-quartile difference of 2.6 for both); however, the risk profile was linear throughout the range of hippocampal atrophy values but reached a ceiling at higher values of brain Aβ load. Our results are consistent with a model of Alzheimer's disease in which Aβ deposition initiates the pathological cascade but is not the direct cause of cognitive impairment as evidenced by the fact that Aβ load severity is decoupled from risk of progression at high levels. In contrast, hippocampal atrophy indicates how far along the neurodegenerative path one is, and hence how close to progressing to dementia. Possible explanations for our finding that many subjects with mild cognitive impairment have intermediate levels of Aβ load include: (i) individual subjects may reach an Aβ load plateau at varying absolute levels; (ii) some subjects may be more biologically susceptible to Aβ than others; and (iii) subjects with mild cognitive impairment with intermediate levels of Aβ may represent individuals with Alzheimer's disease co-existent with other pathologies.
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