Humans are human. Often a sample will get uploaded multiple times (perhaps the spouse uploaded the same data). Duplicate sample data can generate bias. In this post I will look at detecting duplicates and then eliminating all of them except for one (the last one uploaded). This is a recommended step before computing statistics as discussed in this prior post.

So how do you detect duplicate dataset easily? As a long time SQL developer I can think of some complex left joins and other pain, but as a statistician there is a simple elegant cheat – use standard deviation of the count column to create a hash that uniquely (at least until you get a few billion samples) identifies a sample.

Create Proc RemoveDuplicateUploads As
Declare @Rows int =1
Declare @ToDelete as Table (LabResultsId int)
While @Rows > 0
BEGIN
	Insert into @ToDelete(LabResultsId)
	Select Min(OldestId) From (
		SELECT R.[LabTestId],Min(T.LabResultsId) as oldestId
		, Cast(1000 * STDEV([BaseOneMillion]) as Int) as Hash
		FROM  [dbo].[LabResultTaxon] T (NOLOCK)
		JOIN [dbo].[LabResults] R (NOLOCK)
		ON T.LabResultsId=R.LabResultsId
		Group By R.LabTestId ) HashTable
	Group by Hash, LabTestId Having Count(1) > 1
	-- We cycle until we have deleted all
	SET @Rows=@@ROWCOUNT
	-- We need to delete the taxon before the test
	Delete from [dbo].[LabResultTaxon] Where LabResultsId in (Select LabResultsId from @ToDelete)
	--Now delete the test
	Delete from [dbo].[LabResults] Where LabResultsId in (Select LabResultsId from @ToDelete)
 END

The steps explained:

• First we get a hash for each test’s measurements (as Hash)
• Then we find all of the test data for a specific test that have the identical Hash and we have 2 or more tests with those values
• We insert the OLDEST TestId into a table
• We then delete the Taxon level data
• We then delete the Test level data (we can’t delete the test and it’s data is deleted).
• Once that is done, we repeat because there may be more duplicates – remember we only removed the oldest one.

That’s it. A needed cleanup of donated data to keep our data clean and unique.

For my last of the 4 installment per weekend, I will look at computing statistics. I have found non-parametrics analysis to get more interesting results, but classic core statistics are also helpful to understand the nature of each taxonomic layer.

Data Schema Update

We are dealing with this section of the schema which has another table added.

For thoses who are not statisticians, terms like Kurtosis may sound like a medical condition and not a statistical measure. I have enclosed links to Wikipedia articles on each of these items below.

• Mean
• Median
• Mode
• Minimum
• Maximum
• Standard Deviation
• Variance
• Skewness
• Kurtosis

The code sample does just one LabTest and one Quantitle. For a production implementation, I would do iterate over

Select LabTestId from LabTests

And quantiles = {3,4,5,6..}

The code is at:
https://github.com/Lassesen/Microbiome2/tree/master/TaxonomyStatistics

Remember to update database schema too.

Bottom Line

That’s it for this weeks installments. Homework on this one is whether you should include Zero (none-found) values or not. This is actually a complex question which may depend on what percentage of samples have a specific taxonomic unit.

The prior posts dealt with uploading text files – perhaps a csv file or a json file. Sometimes reports are sent as PDF. Today I received a good example of a PDF to show how to upload it.

An example of the file is shown below.

We use the Taxon Name and Rank process as done in this earlier post. The trick is pretty simple: decompose the file using itext7 or earlier versions. And then create an appropriate data table (same format as in the earlier post), and use the same store procedure.

As before, not found bacteria are written to a file for later analysis of exception. In this case there was only two items not found :-).

<XenoGene>
  <Table>
    <taxon_rank>species</taxon_rank>
    <taxon_name>Burkholderia sp. CCGE1002</taxon_name>
    <BaseOneMillion>100</BaseOneMillion>
  </Table>
  <Table>
    <taxon_rank>species</taxon_rank>
    <taxon_name>tsetse mealybug Leptospira santarosai</taxon_name>
    <BaseOneMillion>100</BaseOneMillion>
  </Table>
</XenoGene>

That’s it. Inspect the source to see the details. Note: For different reports, you will need to build custom parsers.

Source is at: https://github.com/Lassesen/Microbiome2/tree/master/uploadXenoGene

Part of being open sources is being open-source on where you are getting information. For myself, my primary criteria is published peer-reviewed scientific papers. You must determine your own criteria (for example, using unpublished studies referenced and summarized in patent filings).

Citations are the connectors between bacteria, probiotics, medical conditions, lab measurements etc. They are the evidence chain of how things are related.

In this post I will describe column by column what I feel should be in your citation table as a minimum:

• CitationId – unique identifier used in relationships : int
• Title – The title of the study or document: nvarchar(255)
• CitationJson – A summary using the PubMed pattern: nvarchar(max)
• RawText – A pure text version of the document. It will be used for Text Mining and manual inspection : nvarchar(max)
• RawDocument – A binary version of the document. Often this will be a PDF file or may be an image. varbinary(max)
• RawDocumentMimeType – The mime type for the RawDocument: varchar(60)
• Summary – This is typically the PubMed summary for the study: nvarchar(max)
• PMID:  PubMed ID : int
• PMCID: PubMed Citation Id: varchar(14)
• DOI: Digital Object Identifier (DOI) : varchar(60)

Ideally, a component should extract the RawDocument into RawText. For example, OCR for images and various converters for PDF. This type of conversion is outside of the scope of this blog series. They can be done (I have written such professionally).

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

Below are the class diagrams:

For some background on the PubMed API calls, see this page. The

The following shows the response searching for “Corgi”.
https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi?db=pubmed&term=corgi&retmode=json&retmax=10

{
    "header": {
        "type": "esearch",
        "version": "0.3"
    },
    "esearchresult": {
        "count": "101",
        "retmax": "10",
        "retstart": "0",
        "idlist": [
            "31194653",
            "31172540",
            "30577744",
            "29794371",
            "29681557",
            "29355995",
            "28942296",
            "28933509",
            "28855429",
            "28566164"
        ],
        "translationset": [
        ],
        "translationstack": [
            {
                "term": "corgi[All Fields]",
                "field": "All Fields",
                "count": "101",
                "explode": "N"
            },
            "GROUP"
        ],
        "querytranslation": "corgi[All Fields]"
    }
}

Running it is simple, just give it the name of the condition (it defaults to 100 citations, but you can increase it. I usually run with 10,000 citations at a time for each bacteria taxonomy name).

CitationConsole “Some Unusual Condition”

What is the net result? Running it with “SIBO”, we see in the database.

So we have our data ready for either text mining or manual review.

Home Work

We can now fill up our citation tables, in some cases with the complete text (excluding appendices) as well as a short summary. How are you going to process the data?

• Manual review?
• Use Text Data Mining? See this page to start.

Processing this data is where there is a major effort needed.

We want to view relationships between microbiome and various medical conditions. There is also a need to know the impact of various probiotics on various conditions.

The International Classification of Disease (ICD) is the equivalent as taxon numbers for bacteria and is a well defined comprehensive list of medical conditions. We may need to add alternative names for some of the conditions to match common usage.

If something is not there (it should be), we can always add our own codes and diagnosis. For example there is no “SIBO” or ” Small intestine bacterial overgrowth” item, scanning on like we see that the code of A04.9 is often used. We would insert that into the ICDAltName table (likely two rows, one for each variation).

First we need to download some files as shown below.

The file that we wish to use is shown below.

This file is the parameter to pass to the C# console application that will upload and populate the table. Most of the magic happens in SQL.

We end up with tables as shown below. In the next posts we look at connecting conditions to probiotics to studies, and conditions to microbiomes to studies.

Example of the data after uploading is shown below. There are around 44 thousand IDC codes – fortunately, they are in a hierarchy.

The source code is at: https://github.com/Lassesen/Microbiome2/tree/master/UploadICD

You just need to give the location of the two files downloaded above

uploadICD.exe D:\Downloads\2019-ICD-10-CM-Tables-and-Index\icd10cm_index_2019.xml D:\Downloads\2019-ICD-10-CM-Tables-and-Index\icd10cm_tabular_2019.xml

Homework Item

We have some 44 thousand items which are in a hierarchy if you use the code. There are many ways to handle that on a web site — what approach will you do?

This is just a variation of the uBiome upload because thryve provides the taxon numbers.

Example of their download file

"taxon_id","rank","name","parent","count"
"165179","species","Prevotella copri","Prevotella","36128"
"838","genus","Prevotella","Prevotellaceae","36982"
"171552","family","Prevotellaceae","Bacteroidales","40738"

The data format is automatically detected. Classes in the code converted to Interfaces for eventual consolidation into a DLL containing all of the uploads.

I have not updated the folder name

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

Remember that the database schema has also been updates.

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!