Interactions with Bacteroides thetaiotaomicron

Data Source:

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There are several ways of doing association. At the bottom is trying linear regression on the percentiles. Doing linear regression of actual counts produce few results. For a blog post describing the process see Bacteria interacting with bacteria.

Above this is the association using Kalthrop-Moldrup Limits (Patent Pending) to determine non parametric association between High Levels and Low Levels. High levels may cause changes in other bacteria (for example, due to amount of natural antibiotics released) but no effect with low levels. Similarly the reverse.

Chi2 is truncated to 10000. Example Expected Count was 13.4, observed was 540


Calculated from Fy(y)=a + (slope) Fx(x). F(x) are custom monotonic functions. See this post for technical notes on computation

RankBacteriaSlope
Relative Impact
Percentage ExplainedImpact
genus Anaerovorax 56.8% 25.3% 14.4%
genus Asteroleplasma 57.4% 20.5% 11.8%
genus Caldicoprobacter 51.6% 27.5% 14.2%
genus Peptoclostridium 46.5% 22.5% 10.5%
genus Subdoligranulum 53.9% 20.7% 11.2%
no rank unclassified Barnesiella 52.6% 22.8% 12%
no rank unclassified Eubacterium (in: firmicutes) -47.8% 20.7% -9.9%
no rank unclassified Roseburia -55% 23.4% -12.9%
species Bacteroides faecis 97.6% 63.7% 62.1%
species Bacteroides sp. 4072 89.7% 28.6% 25.7%
species Bacteroides sp. AR29 125.2% 35.8% 44.9%
species Bacteroides sp. dnLKV9 74% 44.9% 33.3%
species Barnesiella sp. EBA4-14 53.5% 22.5% 12%
species Butyricimonas faecihominis 53.9% 21.9% 11.8%
species Eubacterium sp. SA11 -49.4% 22.4% -11%
species Gordonibacter urolithinfaciens 49.5% 17.9% 8.9%
species Odoribacter splanchnicus 49.2% 20.5% 10.1%

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