A method and marker combination for determining the maturity of the gut microbiota in infants and young children
A technology for intestinal flora, infants and young children, applied in the fields of genomics, biological systems, proteomics, etc., can solve the problem of a large range of baseline standards, which is not conducive to improving the accuracy, precision and robustness of the method, and is not conducive to intestinal bacteria. Problems such as accurate assessment of group status
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Embodiment 1
[0182] In this embodiment, human infants are used as research objects.
[0183] 1.1 Baseline Data Acquisition
[0184] Screen samples based on the following principles:
[0185] a. The body mass index (BMI) of the selected individuals is within three standard deviations of the WHO Child Growth Standards.
[0186] b. The selected individuals were born naturally, were breastfed before weaning, and did not use antibiotics one week before sampling.
[0187] In this case, a total of 79 metagenomic sequencing data of the intestinal flora of Chinese infants and young children aged 0-36 months were selected, of which 55 data were randomly selected for model training, and the other 24 data were used for model testing. The data came from samples age in months, with 3-month age as the interval classification, the distribution is as follows Figure 2 to Figure 4 shown; the data of 3 unselected baselines were also selected to illustrate the use of models and standards.
[0188] Analyze...
Embodiment 2
[0210] 2.1 Baseline Data Acquisition
[0211] In this example, based on the samples used in Example 1, the quantitative information of gene composition and its diversity and richness index were selected, and a total of 24,452 candidate markers were selected as the candidate marker library. These genetic components include gene ontology, orthologs, enzymes and the biochemical reactions they catalyze.
[0212] 2.2 Selection of markers
[0213] 1) Filter candidate markers with smaller standard deviations;
[0214] Calculate the standard deviation of each marker in the sample in the standardized candidate marker library of the 55 training samples, and select the marker whose standard deviation is greater than the standard deviation of the 75% quantile, that is, remove the markers that have little difference in the sample There are 6,113 markers in total. The standard deviation distribution of all candidate markers is as follows Figure 11 shown.
[0215] 2) Filter the candida...
Embodiment 3
[0229] 3.1 Baseline Data Acquisition
[0230] In this example, based on the samples used in Example 1, the quantitative information of metabolic pathways and their diversity and richness indices were selected, and a total of 1,505 candidate markers were selected as the candidate marker library.
[0231] 3.2 Selection of markers
[0232] 1) Filter candidate markers with smaller standard deviations;
[0233] Calculate the standard deviation of each marker in the sample in the standardized candidate marker library of the 55 training samples, and select the marker whose standard deviation is greater than the standard deviation of the 75% quantile, that is, remove the markers that have little difference in the sample There are 377 markers in total. The standard deviation distribution of all candidate markers is as follows Figure 17 shown.
[0234] 2) Filter the candidate markers that are less correlated with the age of the sample provider;
[0235] Calculate the Pearson corre...
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