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However, very few marine microbial association patterns are well investigated in this field.
Mining the marine microbial association patterns and diversity is a key for exploiting the marine resources.
The present study reports the development of a novel method called HC-sNMF to detect the marine microbial association patterns.
In F. Liu et al.'s paper " Mining seasonal marine microbial pattern with greedy heuristic clustering and symmetrical nonnegative matrix factorization," they developed a novel method called HCsNMF to detect the marine microbial association patterns.
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HC-sNMF provides new insights into the natural history of microbes, finding the relationship among microbes and environmental factors and trying to determine the microbial association pattern difference among seasons and which environmental factors might have the greatest influence on the varying diversity.
The microbial association (or cooccurrence) patterns can offer new insight into the structure of complex microbial communities, revealing the niche spaces shared by community members and identifying habitat affinities or shared physiologies that could guide more experimental settings.
The association patterns between marine microbial species and environment factors are hidden in these large amount sequences.
The four seasonal marine microbial association communities detected by s-NMF were shown in Figure 5.
Using the microbial community and gene expression datasets, we demonstrated that eLSA uniquely captures additional time-dependent associations, including local and time-delayed association patterns, when compared to ordinary correlation methods, such as PCC.
Yet, a microbial association is frequently suspected.
We also observed other association patterns between CA and GE.
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