There are many medical lab reports that users may wish to upload. My first solution is show at the links below.

• Bioscreen
• Biovis Microbiome Plus
• DayTwo
• Diagnostic Solution GI-Map
• GanzImmun Diagnostics AG Befundbericht
• Genova Gi Effects
• Genova Parasitology
• InVitaLab
• Kyber Kompakt
• Medivere: Darm Mikrobiom Stuhltest
• Medivere: Darn Magen Diagnostik
• Medivere: Gesundsheitscheck Darm
• Metagenomics Stool (De Meirleir)
• Smart Gut
• Verisana
• Viome
• XenoGene Informe De Microbiota Intestinal

The solution was to extract the information into chunks.

A better solution is to get the actual reported values entered. At this point, we encounter the problems of different methodologies at different labs. Technically, the count of one lab cannot be reliably compare to the count at a different lab. There are some ways to mitigate this issue which is outside of the scope of this set of posts (coding).

Setting Up

https://github.com/Lassesen/Microbiome2/tree/master/LabDefUpload

For each of these labs we need to know what bacteria is reported — this would be used done by this new table. We add DisplayOrder so the bacteria will appear the same as on the printed or pdf report (makes entry easier!)

The rest results may be in any of the following presentations

To handle this, we add another table with verbal descriptions and links to images (optional)

Last thing is to enter the value for each. For simplicity, I opted to define ranges of value.

So, to recap this last table defines the range for a specific bacteria on a specific test that would be deem to be have some label. The data needs to be entered for each bacteria in each test.

In today’s code example, I enclose a download from my site of the lab tests with their taxons (which may be incomplete or incorrect). I did this by putting into a text file the taxons,displayorder, and then naming the file {labName}.{LabTestName}.txt. They will all be dropped into a /data/ folder.

This makes it easy for lab tests to be revised/updated (and hopefully, with a pull request for the updated date — this is open source, some payback to the project is expected behavior).

After the upload you should see

When the Levels and Ranges are added, things becomes a little more complex for processing. If you execute the following and save it as an .xml file (in the /data/ folder), I will write a demo program when I get a pull request on the data. Do it one by one for each LabTestName (GI-Map is shown below) –> GI-Map.xml

SELECT * from  LabTests T (NOLOCK) 
			JOIN LabTestStandards S (NOLOCK)
			ON T.LabTestId=S.LabTestId
			LEFT Join LabTestStdLevel L (NOLOCK)
			ON L.LabTestStandardsId = S.LabTestStandardsId
			LEFT JOIN LabTestLevel M (NOLOCK)
			ON L.LabTestLevelId=M.LabTestLevelId
			WHERE LabTestName='GI-Map'
			FOR XML AUTO

The result is XML to be dropped into a file

  
    
      
    
  
  
    
      
    
   .... etc ...

There is actually a stored procedure that does it for you:
ExportLabSettingsForSharing @LabName varchar(50)=’GI-Map’

There is also one that generates the data to use for data entry:

DisplayNewEntryForm @LabName varchar(50)=’GI-Map’

Bottom Line

This post definitely has a home work assignment — obtain the level data and names from existing reports that you have available (or can borrow from friends).

In my prior post on ubiome Json files, or taxon number based files, I gave code and the pattern to use. Unfortunately, not every one provides such easy to use data. Look at the following download file format (AmericanGut)

#taxon	relative_abundance
k__Bacteria;p__Bacteroidetes;c__Bacteroidia;o__Bacteroidales;f__Bacteroidaceae;g__Bacteroides	0.287722
k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__;g__	0.130347
k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Ruminococcaceae;g__	0.116602
k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Ruminococcaceae;g__Faecalibacterium	0.045869
k__Bacteria;p__Bacteroidetes;c__Bacteroidia;o__Bacteroidales;f__Porphyromonadaceae;g__Parabacteroides	0.042162
k__Bacteria;p__Bacteroidetes;c__Bacteroidia;o__Bacteroidales;f__Rikenellaceae;g__	0.039537
k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Lachnospiraceae;g__	0.034286

We have the name hierarchy (which unfortunately can vary slightly by lab). The names identify the tax_rank (which can also vary by lab!).

For this console application, I am going to end up with a data table consisting of:

• tax_rank (converted from f__, s__ etc)
• tax_name (extracted from the last name before the number)
• BaseOneMillion (done by multiplying the number by 1,000,000)

Source: https://github.com/Lassesen/Microbiome2/tree/master/taxonNameUpload

This data table is then uploaded and Sql goes thru a series of steps to attempt to match it. Things that are not matched are written to a file (like done in the prior post). In this case, the solution is much easier to patch the differences. Just add the name to the TaxonNames file with it’s appropriate taxon (no need to create a separate substitution table). The Sql attempts to match by taxon rank, name first, if that fails, it falls back to name alone.

For the test file that I used, there was a lot of mismatches

Spot checking for a few, I discovered that these are currently listed as having a parent of “unclassified Gammaproteobacteria” (118884)

For others, there is nothing that seems to be a match and the original data line does not clarify anything.

k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Clostridiaceae;g__SMB53	0.000927

Bottom Line

For a 103 line input we got 87 matches and the rest unclear. The sample code gives you the information on the challenges. You need to come up with your own resolutions (discard or patch)

P.S. Remember to update your SQL Server Schema to get the new sprocs and data types.

I have just added another console application. I am doing things in layers and downstream we could create a DLL with everything in it. At this point (especially for those wishing to port it to other languages), doing one feature in one console application is likely the best approach.

This installment takes the ubiome json file and uploads it to the tables.

This is the template to use for uploading tests that provides the standardized taxon number. The only different would be in file parsing.

https://github.com/Lassesen/Microbiome2/tree/master/ubiomeUpload

Input file structure

{
  "download_time_utc": "2019-06-24T22:46:36.000Z",
  "sequencing_revision": "1346982",
  "site": "gut",
  "sampling_time": "2019-06-12T00:00:00.000Z",
  "notes": "",
  "ubiome_bacteriacounts": [
    {
      "taxon": 1,
      "parent": 0,
      "count": 70967,
      "count_norm": 1000000,
      "tax_name": "root",
      "tax_rank": "root"
    },

After one upload, your data should look like this:

The code is simple, a single method that reads the file name, takes the JSON and makes an object. Walks the object and create a data table. Then calls a stored procedure with this data table and other information.

One thing that is also done is it writes a report on any taxon that it could not match to the ncbi microbiome hierarchy. “Different strokes for different folks”. Actually, more often a taxonomy got deprecated and ubiome has not updated their system.

In the execution folder, you will see a file containing the missing taxon

With the contents like:

If you open the json file, and search for it, you will see what they call it.

You could add this to the taxonHierarchy or ignore it. I searched for it by name and got an apparent match but with a different taxon number.

Resolving this disagreement is up to you. One option is a replacement table of ubiome’s taxon to ncbi taxon where you are confident that they are the same.

First you need to download the ncbi dump files first.

Go to ftp://ftp.ncbi.nlm.nih.gov/pub/taxonomy/

When you unzip the file, you will see the available data.

Load up the c# project at https://github.com/Lassesen/Microbiome2/tree/master/UploadTaxHier

Modify the DB Configuration string to point to your database. Run the application with the location of the dmp files above.

After the upload, we see the number of records that we have

That’s it! If you want to do periodic updates, I will leave that to the reader to do (and add a pull request). Remember this is open source!

Readers have expressed interest in some of my work being open sourced. The actual site would be described as an “evolved beta”, rather than subject people to quirks and kludges, I am proceeding as a redesign of a V.2 product. If you are interested, please FOLLOW (top left) to get updates as they happen.

The Repository is at:

https://github.com/Lassesen/Microbiome2

The first item that I want to get up for discussion is the core database tables – for review and comments. The Database diagram is shown below.

A few quick notes:

• Statistics were done as a separate table instead of the typical additional columns because trying multiple quantiles is seen as the way to go for non-parametric analysis. This becomes open ended with items like “Q2_18” – Quantile 2 of a 18 way quantization being possible. With that type of breakdown, we want to know if we are dealing with stale date, so we need to know the computation date.

Next post will deal with populating TaxonHierarchy and TaxonNames from ncbi downloads.

Thanks for joining me!

This is a companion site to the analysis site at: https://microbiomeprescription.com/

The intent of this site to assist people with health issues that are, or could be, microbiome connected. There are MANY conditions known to have the severity being a function of the microbiome dysfunction, including Autism, Alzheimer’s, Anxiety and Depression. See this list of studies from the US National Library of Medicine. Individual symptoms like brain fog, anxiety and depression have strong statistical association to the microbiome. A few of them are listed here.

The base rule of the site is to avoid speculation, keep to facts from published studies and to facts from statistical analysis(with the source data available for those wish to replicate the results). Internet hearsay is avoid like the plague it is.

The Microbiome as a Key to Health

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