The voluntary group included 42 subjects (22 men, 3 left-handed) who were clustered by age into three subgroups: young (18 35 years old), middle-aged (36 50 years old), and elderly subjects (51 80 years old).
Demographics clustered by age groups with patients' information of age, gender, weight, body size, and injected activity.
At P7, samples were clustered by age, then by brain region.
Globally, samples in both regions were almost perfectly clustered by age group, reflecting distinct patterns of gene expression at each age.
Potential outbreaks were detected from clustering by age, gender, race/ethnicity, resident ZIP code, hospital, time of visit, and chief complaint.
The clustering by age was statistically significant (P test in UniFrac: P = 0.007), although the clusters did not represent evolutionary divergence of bacteria between the age classes (Unifrac significance: P = 0.104).
Strikingly, heat map analysis of differentially expressed genes in KD and control coronary artery tissues showed that gene expression in KD patients did not cluster by age, time since onset of KD, or treatment, supporting the analysis of the 8 KD tissue samples together (Fig. 2b and c).
The results extend prior work by showing that not only do clusters differ by age, gender, marital status, education and income but they also differ in terms of work status and residential locality, which explained unique variance in membership even after controlling for all other factors.
Within each district, both temporary and permanent child residents in the child health clinics and day care centres were clustered by seven age groups (age 0 1, 1 2,...., 6 7) and by sex.
First, we calculated the number of cases of readmission and the number of expected cases of readmission for each neighborhood cluster, adjusted by age group quartiles (40 51, 52 62, 63 74, 75+) and gender (male, female).
