Machine learning allows us to realize the seemingly insurmountable goal of predicting the fluctuations of entire microbiotas due to the specific effects of bio-stimulators. While traditional ecologists may find it too difficult to consider every unique microbial relationship in an ecosystem, machine learning programs uses numerical analysis to not only quickly determine these associations but also use them to predict overall population shifts caused by stimuli. For our modelling purposes we choose Weka, a software with strong classification capabilities, to establish accurate connections between every genera of bacteria in our soil.
Team:NCTU Formosa/Dry Lab/Microbiota Prediciton
Construction
Weka predicts overall shifts in microbiota by determining how a target genus is altered, then using correlation values between the target genus and other genera to calculate how the rest of the bacteria change.
This is where Weka’s support of classification is useful to us, as it can quickly determine which genera affect each other and which do not. We begin with our analyzed NGS data – specifically, a heat map detailing correlation values of bacteria in our soil sample. Weka allows us to set our own conditions to detect correlation between two genera of bacteria and will filter the results to yield all the pairs of bacteria determined to be correlated, either positively or negatively. Here, we take June's correlation heatmap of the top 20 bacteria as an example:
Figure 1: Temperature growth curve model progress
Next, We define positive correlation as any Spearman's Correlation value above 0.7, and negative correlation as any value below -0.7. For example:
Once we’ve determined the significantly correlated pairs of bacteria, Weka can further establish the exact nature of these relationships. Not all bacterial relationships exhibit linear regression, so Weka then takes the correlated pairs and plots the data of each pair into a graph to determine the true nature of each association.
$$y=-4.1248x^2+0.2061x+0.0008;\ R^2=0.725$$
Figure 2: Example of positive non-linear correlation
$$y=-0.061x+0.0003;\ R^2=0.714$$
Figure 3: Example of negative linear correlation
Results
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