Interactions with Alistipes

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
class Lentisphaeria 48.5% 16.9% 8.2%
family Barnesiellaceae 48.8% 21.4% 10.4%
family Odoribacteraceae 66.1% 40.2% 26.6%
family Rikenellaceae 98.6% 97.7% 96.3%
family unclassified Clostridiales 44.9% 18.3% 8.2%
family Victivallaceae 48.9% 16.9% 8.2%
genus Barnesiella 47.9% 20.4% 9.8%
genus Butyricimonas 50% 22.7% 11.4%
genus Caldicoprobacter 51.5% 18.9% 9.7%
genus Odoribacter 61.8% 33.9% 21%
order Victivallales 48.6% 16.9% 8.2%
species Alistipes finegoldii 74.6% 21.5% 16%
species Alistipes indistinctus 62.3% 33.9% 21.1%
species Alistipes putredinis 78.5% 48.8% 38.3%
species Alistipes shahii 57.8% 30.8% 17.8%
species Alistipes sp. RMA 9912 50.5% 22.6% 11.4%
species Barnesiella intestinihominis 51.1% 23.4% 12%
species Butyricimonas paravirosa 60.2% 30.4% 18.3%
species Odoribacter splanchnicus 62.4% 30.6% 19.1%
species Peptoniphilus sp. DNF00192 -48% 22.7% -10.9%

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