Most of the content was originally posted on https://cfsremission.com/ with the pages on the left being a restructuring of selected posts from over a thousand posts on that site.
Recommended Site For Testing
If you have ME/CFS or other financially disastrous condition, there is always a nasty cost factor for testing. My usual recommendation is for the cheapest, high quality provider that provides information for upload to my analysis site. Some sites provide a mountain more of information — but the benefit from that extra information is almost nothing (and it adds $$$$ and complexity).
uBiome.com is shutting down. This had been my personal usual site because using a variety of techniques, the cost was $25/sample. Don’t order from there.
Thryve is what I am starting to use. Their reports may be processed here for independent suggestions. I would also recommend
By uploading, you consent to allow your microbiome data and symptoms to be made available to citizen scientists for further discoveries.
Required consent is cited above. 3rd party is responsible to obtain consent.
The structure is simple:
The key is issued by us and identifies where the data is coming from (“source”)
logon and password are the authentication pair that you generate. These are used for logging on. Logon and Password should be the same for all samples from the same user (so we can display on a timeline).
Probability is an estimate whether something may help, not how much it can help. The relative help between two items is rarely found in any study.
Probability is based on the number of studies reporting that something shifts a bacteria in the desired way. A single report that Blue Cheese reduces Xeonella may get a value of 0.1 while 10 reports that barley reduces Boozella would get a value of 1.0 (if 10 reports were the maximum number of reports reported).
Studies often contradict each other – typically caused by a confounder that the study ignored. https://en.wikipedia.org/wiki/Confounding. To address this, we aggregate the number of reports with scaling. For example:
6 reports showing desired changes
2 reports showing undesired changes
Could be computed as 6-2=4. Due to this increase uncertainty, we do other methods for example:
exp (log(6/8) + log(2/8))
Once we compute all of these numbers, we then scale them so the maximum value is +1.
At the top you will see two select choices and a link to the associated library page. All of the data is downloaded so you can quickly explore the patterns without having to wait for the next bacteria to load.
The page has been reorganized and a chart added (in place of a ton of quantile numbers). The chart can show the log(value) or the value.
If you are logged in, it will also display all of your gut samples. I will be adding the ability to select which samples from the compare sample pages in the near future as well as plotting against precise percentile over time.
The report file reports only at the strain level, no genus or family levels are given. These total sums up to 100%. The smallest resolution appear to be 0.02% That is 1 in 5,000 bacteria. This is a lot lower resolution than other providers ( 1 in 160,000 is seem in some other reports with a good sample). There is something odd about a large number of bacteria being at 0.02 or 0.04 percent.
It appears that FASTQ downloads from them (alleged to be available if requested) is the prefered way to get better data.
One bacteria was listed as:”(Bifidobacterium catenulatum/Bifidobacterium gallicum/Bifidobacterium kashiwanohense/Bifidobacterium pseudocatenulatum)”
which is with more current tests are 4 different strains.
Bottom Line: Won’t Do
There are too many problems with the data. I have spent almost an entire day fighting it. If they provide a FASTQ file, I have unload for those processed through SequentiaBiotech web site.
To use their CSVs:
They must provide the official Taxon Numbers in the Excel File
They must provide the full hierarchy with numbers at each level
Without those, their data will pollute the existing contributed base too much. There are no acceptable kludge arounds for these defects.
This weekend I added distribution charts for a variety of aggregations from microbiome data ( End Products, matches to Published Medical studies for conditions, uBiome metabolites, select ratios reported in the literature). This give a much better visualization whether there is a potential issue or not.
I followed that up with distribution charts for all of the taxa in the uploaded samples. Some taxa produce ‘sweet curves’ and others indigestion to someone that likes graphical elegance.
Sweet Genus Allisonella
One of many ugly: order Neisseriales
Togging the charts between actual value and log(value) often reveal a lot more about the behavior of a bacteria. Look at genus Roseburia. Two very different inflection points (change of behavior) appears — depending on how you look at it.