Microbial characterization based on multifractal analysis of metagenomes

Abstract

Introduction: The species diversity of microbiomes is a cutting-edge concept in metagenomic research. In this study, we propose a multifractal analysis for metagenomic research.

Method and results: Firstly, we visualized the chaotic game representation (CGR) of simulated metagenomes and real metagenomes. We find that metagenomes are visualized with self-similarity. Then we defined and calculated the multifractal dimension for the visualized plot of simulated and real metagenomes, respectively. By analyzing the Pearson correlation coefficients between the multifractal dimension and the traditional species diversity index, we obtain that the correlation coefficients between the multifractal dimension and the species richness index and Shannon diversity index reached the maximum value when q = 0, 1, and the correlation coefficient between the multifractal dimension and the Simpson diversity index reached the maximum value when q = 5. Finally, we apply our method to real metagenomes of the gut microbiota of 100 infants who are newborn and 4 and 12 months old. The results show that the multifractal dimensions of an infant's gut microbiomes can distinguish age differences.

Conclusion and discussion: There is self-similarity among the CGRs of WGS of metagenomes, and the multifractal spectrum is an important characteristic for metagenomes. The traditional diversity indicators can be unified under the framework of multifractal analysis. These results coincided with similar results in macrobial ecology. The multifractal spectrum of infants' gut microbiomes are related to the development of the infants.

Keywords: chaos game representation (CGR); diversity index; gut metagenome; metagenome; multifractal.

Figure 1

Heat map of simulated metagenome of dataset 2, the abundance are 0.016928832, 0.30559462, 0.120814049, 0, 0.07959993, 0.00894306, 0.03682631, 0.37768779, 0.00570905, and 0.04789635. The dissolution of (I) is 256×256; (II) is an image magnified by a factor of 2 from the upper left part of (I); (III) is an image magnified by a factor of 2 from the upper left part of (II); and (IV) is an image magnified by a factor of 2 from the upper left part of (III). For better visibility, we regarded the number exceeding the threshold as the threshold, whose values are taken three times the mean value.

Figure 2

Linear fit of ln(M(ϵ,q)) and ln(ϵ), where ϵ is set to 2^-6,2^-7,2^-8,2^-10,2^-11,2^-12, and 2^-13, respectively.

Figure 3

Multifractal dimensions of simulated genome. Green asterisks represented the D(q) of samples simulated from high-diversity communities, and blue dots represented the D(q) of samples simulated from low-diversity communities. For each sample from the same community, the abundances are given in Table S1 (the last line in the table).

Figure 4

Pearson correlation coefficient of species richness and multifractal dimension D(q).

Figure 5

Pearson correlation coefficient of species’ Shannon diversity index and multifractal dimension D(q).

Figure 6

Pearson correlation coefficient of species’ Simpson diversity index and multifractal dimension D(q).

Figure 7

Multifractal dimension of the gut microbiome of the infant when he/she is 12 months old (12 M), 4 months old (4 M), and a newborn baby (baby), and her mother (M).

Figure 8

Boxplots of D(0), D(1), and D(2) of multifractal dimension of gut microbiomes of infants when they are 12 months old, 4 months old, and a baby, and that of the mother.

Figure 9

Mean values of multifractal dimension of 100 infants’ gut microbiomes when they are 12 months old (12 M), 4 months old (4 M), and a newborn baby (baby), and that of their mother (M).