I have just pushed a new update to the
http://microbiomeprescription.azurewebsites.net/ website. The new page lists bacteria associated with specific symptoms and conditions.
Finding Gender specific bacteria shown above agrees with research
Right Thinking
Almost all published studies report “found average of patients X bacteria is statistically {higher|lower} than the controls”. If you look through my Condition templates, you will see that for some conditions, one study reported high and another low — dilemma!
Not dilemma, but a hint to the nature of the problem.
Repeating the old gospel
Over the last 6 years, I have been ascribing ME/CFS ets to a microbiome dysfunction – that is, a complex shifting of bacteria that alters the metabolites that the body receives. The key work is complex shifting. It is not, too high or too low — which is a naive simplistic thinking of the issue.
Statistical tests on averages on less sensitive than using the classic Chi2 test. My revision to use the logic of BoxPlot shifted my mind to use quantiles. With quantiles, you can apply Chi2 … and suddenly relationships appear!
How do I do this, first, I look over all microbiome samples, sort each and then divide them into four parts of equal size – recording the range of values. Now for a specific subset that has a condition, I count how many fall into each range — then I apply Chi2 to see if this is statistically significant.
Constructed Example.
We have 16 samples, so 4 in each group but for those suffering from Monty Pythonism, we see that we have 8 in the lowest and 8 in the highest. The result may be that the average between these patients and the control are identical. No Significance.
In this case we see two shifts happening that has a 1/1000 odds of being random
This does not make life simpler…. you cannot say “I have symptom X, I need to reduce bacteria Y”, You can say that the balance of bacteria Y is off and needs to be adjusted (as well as things it communicates with)
For people with EBV, we see lower levels of one species in general:
In other cases, the shifts are more complex.
http://microbiomeprescription.azurewebsites.net/ website. The new page lists bacteria associated with specific symptoms and conditions.
Right Thinking
Almost all published studies report “found average of patients X bacteria is statistically {higher|lower} than the controls”. If you look through my Condition templates, you will see that for some conditions, one study reported high and another low — dilemma!
Not dilemma, but a hint to the nature of the problem.
Repeating the old gospel
Over the last 6 years, I have been ascribing ME/CFS ets to a microbiome dysfunction – that is, a complex shifting of bacteria that alters the metabolites that the body receives. The key work is complex shifting. It is not, too high or too low — which is a naive simplistic thinking of the issue.
Statistical tests on averages on less sensitive than using the classic Chi2 test. My revision to use the logic of BoxPlot shifted my mind to use quantiles. With quantiles, you can apply Chi2 … and suddenly relationships appear!
How do I do this, first, I look over all microbiome samples, sort each and then divide them into four parts of equal size – recording the range of values. Now for a specific subset that has a condition, I count how many fall into each range — then I apply Chi2 to see if this is statistically significant.
Constructed Example.
We have 16 samples, so 4 in each group but for those suffering from Monty Pythonism, we see that we have 8 in the lowest and 8 in the highest. The result may be that the average between these patients and the control are identical. No Significance.
This does not make life simpler…. you cannot say “I have symptom X, I need to reduce bacteria Y”, You can say that the balance of bacteria Y is off and needs to be adjusted (as well as things it communicates with)
For people with EBV, we see lower levels of one species in general:
In other cases, the shifts are more complex.
