This is a part of a series of Technical Notes on Microbiome Analysis

For a while I have been using a variation of this concept for 16s samples that I have reviewed. The concept is very simple to a statistician:

Percentiles is converting data into a native uniform distribution. If you sample for 1000 boxes where each box has 100 balls numbered 1-100, then you expect the distribution of the balls samples to be uniform. It they are not, then something is definitely unfair.

Concept

With the microbiome things are a little more complex because a high in a single strain may push it species into high and thus the genus into high. We could do independent levels, for example species only or genus only. The problem is that the population size starts to drop and thus the sensitivity decreases as a result.

I happen to have a small collection of shotgun samples processed through CosmosID. Their report give percentile for most of what they measure. Getting accurate percentiles requires large sample sizes.

Below I have charted the results with single percentile ranges from reports that have between 2000 and 5000 different biological units reported. I have charted using different approach (the kitchen sink and then select taxological levels).

All of these samples are from people with health issues. Note that the numbers come from rounding so 100% is just 99.5 to 100 (and not 99.5 to 100.5) so the spikes at 100 is likely twice as high.

Kitchen Sink

Filter to Species Only

Genus Level

Family Level

Bottom Line

Comparing different levels can be informative, to illustrate, we have species below with good uniformity until we hit the high levels.

Looking at the genus level for the same sample, the pattern is very different.

In this case, we drilled down into these high species and got a predominance of Corynebacterium species that fell into our 100% range (99.5-100 percentiles).

Taxonomy Name	Abundance
Anaerococcus mediterraneensis	0.005611
Anaerococcus prevotii	0.006486
Bacteroides rodentium	0.001238
Corynebacteriaceae bacterium ‘ARUP UnID 227’	0.000437
Corynebacterium ammoniagenes	0.000586
Corynebacterium aurimucosum	0.1573
Corynebacterium callunae	0.00013
Corynebacterium camporealensis	0.002243
Corynebacterium casei	0.000726
Corynebacterium comes	0.000391
Corynebacterium diphtheriae	0.0755
Corynebacterium endometrii	0.001051
Corynebacterium flavescens	0.001684
Corynebacterium humireducens	0.00053
Corynebacterium imitans	0.001024
Corynebacterium jeikeium	0.01813
Corynebacterium lactis	0.000437
Corynebacterium liangguodongii	0.000558
Corynebacterium minutissimum	0.03511
Corynebacterium phocae	0.000865
Corynebacterium pseudotuberculosis	0.000233
Corynebacterium renale	0.000493
Corynebacterium resistens	0.001182
Corynebacterium riegelii	0.001321
Corynebacterium segmentosum	0.007016
Corynebacterium simulans	0.3615
Corynebacterium singulare	0.01858
Corynebacterium sp. NML 98-0116	0.001024
Corynebacterium stationis	0.000577
Corynebacterium striatum	0.04709
Corynebacterium timonense	0.001321
Corynebacterium urealyticum	0.00107
Corynebacterium uterequi	0.000642
Corynebacterium yudongzhengii	0.000689
Cutibacterium acnes	0.002298
Dehalococcoides mccartyi	0.006123
Dermabacter jinjuensis	0.01404
Dermabacter vaginalis	0.001265
Fastidiosipila sanguinis	0.003536
Finegoldia magna	0.06368
Helcococcus kunzii	0.00014
Homo sapiens	1.985
Lawsonella clevelandensis	0.003154
Mycobacterium gallinarum	0.000261
Mycobacterium sp. DL592	0.00013
Mycobacterium sp. ELW1	0.001107
Mycobacterium sp. EPa45	0.002298
Mycobacterium sp. PYR15	0.008328
Mycolicibacterium aichiense	0.000223
Negativicoccus massiliensis	0.001935
Peptoniphilus harei	0.04272
Peptoniphilus sp. ING2-D1G	0.000893
Porphyromonas asaccharolytica	0.06443
Porphyromonas bennonis	0.000521
Propionibacterium freudenreichii	0.000465
Schaalia radingae	0.001089
Streptococcus pyogenes	0.00241
Streptococcus sp. NCTC 11567	0.000149
Sutterella stercoricanis	0.000149
Tessaracoccus timonensis	0.00094
uncultured Chroococcidiopsis sp.	0.000242
uncultured Rhizobium sp.	0.000772

We could also produce single value statistical measures — for example Chi². We have an a priori expected value of 1% in each bucket.

IMHO, percentages of percentiles is likely more effective in evaluating an individual person’s gut microbiome. It seems to be able to separate the noise from what is significant, for example Corynebacterium cited above where the cause is a proliferation of species and not dominance of one species.

This has since cascaded into an Eubiosis Index.

Technical Note: An Eubiosis Index for the Microbiome