Application of neural computing methods for interpreting phospholipid fatty acid profiles of natural microbial communities

Abstract

The microbial community compositions of surface and subsurface marine sediments and sediments lining burrows of marine polychaetes and hemichordates from the North Inlet estuary (near Georgetown, S.C. ) were analyzed by comparing ester-linked phospholipid fatty acid (PLFA) profiles with a back-propagating neural network (NN). The NNs were trained to relate PLFA inputs to sediment type outputs (e.g., surface, subsurface, and burrow lining) and worm species (e.g., Notomastus lobatus, Balanoglossus aurantiacus, and Branchyoasychus americana). Sensitivity analysis was used to determine which of the 60 PLFAs significantly contributed to training the NN. The NN architecture was optimized by changing the number of hidden neurons and calculating the cross-validation error between predicted and actual outputs of training and test data. The optimal NN architecture was found to be four hidden neurons with 60-input neurons representing the 60 PLFAs, and four output neurons coding for both sediment types and worm species. Comparison of cross-validation results using NNs and linear discriminant analysis (LDA) revealed that NNs had significantly fewer incorrect classifications (2.7%) than LDA (8.4%). For the NN cross-validation, both sediment type and worm species had 3 incorrect classifications out of 112. For the LDA cross-validation, sediment type and worm species had 7 and 12 incorrect classifications out of 112, respectively. Sensitivity analysis of the trained NNs revealed that 17 fatty acids explained 50% of variability in the data set. These PLFAs were highly different among sediments and burrow types, indicating significant differences in the microbiota.

FIG. 1

Effect of the number of hidden neurons on the cross-validation error. The optimum value of neurons was found to be 4 (arrow), where the training (closed circle) and testing (open circle) errors are the lowest and still similar. Each circle is the mean of 10 separately trained NNs, and each error bar represents the standard deviation of the mean.

FIG. 2

Relative (bars) and cumulative (solid squares) sensitivities of an optimized NN to specific PLFAs. The graph represents 1 of the 10 sensitivity analyses conducted. PLFAs are arranged in order of their importance to the prediction of target values. These PLFAs are listed in decreasing order of importance: (PLFAs 1 to 5) 16:1ω7c, 16:0, 18:1ω7c, 18:1ω9c, 18:0, (PLFAs 6 to 10) 16:1ω7t, cy17:0, 17:0, cy19:0(ω7,8), 20:0, (PLFAs 11 to 15) 22:4ω6, 10me16:0, 20:3ω3, poly19, i16:0, (PLFAs 16 to 20) 17:1ω8c/a17:0, 22:5ω6, 16:1ω5c, 17:1ω6c, 10me18:0, (PLFAs 21 to 25) 16:1ω13t, 18:3ω3/Br17:1/i18:0, 22:6ω3, 18:1ω7t, i15:0, (PLFAs 26 to 30) 18:3ω6/10me17:0, 15:1ω6c, Poly20, α15:0, 20:4ω6, (PLFAs 31 to 35) i17:0, 22:5ω3, br19:1, a16:0/16:1ω9c, 10me14:0, (PLFAs 36 to 40) br17:1ω7c, 16:2ω6/br15:0, 20:5ω3, 24:0, Poly17, (PLFAs 41 to 45) 14:0, 22:0, 11me18:0, 20:2ω6, 20:3ω6, (PLFAs 46 to 50) 19:1ω12c, Poly22, 20:1ω7c, 19:1ω8c, 18:2ω6, (PLFAs 51 to 55) br17:0, 15:0, i14:0, 19:1ω6c, 18:4v3/12me17:0, (PLFAs 56 to 60) 12me16:0, 20:1ω9c, 20:4ω3, mono F.A., br17:0.