Abstract: Gut microbiome is related to many major human diseases, and it is of great significance to study the differences in the structure of gut microbiome under different conditions. Due to the phenomenon of zero expansion of the flora data, geometric mean of pairwise ratios (GMPR) was firstly used to normalize the gut microbiome data. This study proposes an improved Spectrum algorithm using the type 2 diabetes mellitus (T2DM) dataset being taken as example. Firstly, a similarity matrix based on feature weighting was used, which can avoid ignoring the weights occupied by the different eigenvalue sizes corresponding to each sample/feature in that sample; secondly, the Laplacian matrix was replaced by the Hessian matrix, which can avoid the sensitivity problem of traditional spectral clustering; the original K-means algorithm was replaced by the ISODATA clustering algorithm, the number of clustering centers K can be effectively adjusted. The experimental results showed that normalized mutual information (NMI) is 0.423, Davies-Boulding index (DBI) is 4.751, the Calinski-Harabasz index (CH) is 25.541, Rand index (RI) is 0.835 and the adjusted Rand index (ARI) is 0.019, which was improved compared with the effect before the improvement, and the algorithm could identify the structural differences in the intestinal flora of different types of patients, unearth the key bacteria of the gut microbiome.
Key words: gut microbiome; similarity matrix; Laplacian matrix; cluster; type 2 diabetes mellitus
（Acta Laser Biology Sinica, 2022, 31(5): 440-449）