Using Systems Biology to understand Immunosenescen
  • Using Systems Biology to Understand Immunosenescence
  • Background
    • Introduction
    • 1.1. Aging in Society and in the Individual
    • 1.2. Aging and its Molecular Mechanisms
    • 1.3.The Remodeling of the Immune System: Immunosenescence
    • 1.4. Changes in the Immune System Related to Immunosenescence
    • 1.5 Chronic Inflammation During Aging: Inflammaging
    • 1.6 The Immune Risk Phenotype (IRP)
    • 1.6. Systems Biology
  • Objectives
  • Methods
    • Overall Methodology
    • 3.1 Survey of Studies
    • 3.2. Reannotation of Probes in Microarrays
    • 3.3. Data acquisition and pre-processing
    • 3.4. Creation of age-representative samples: AgeCollapsed
    • 3.5. Detection of Highly Age-Related Transcripts: AgingGenes
    • 3.6. Lifetime Co-Expressed Transcript Analysis: AgingNet
    • 3.7. Detection of Change Points in Age-Related Modules
  • Results
    • 4.1. Survey and Data Acquisition
    • 4.2. Reannotation of Platforms
    • 4.3. AgeCollapsed Pre-Processing and Creation
    • 4.4. Assessment of the Agreement of the Relationships of Transcripts with Age between the Sexes
    • 4.5. AgingGenes and AgingNet Reviews
    • 4.6 Aging Co-Expression Network: AgingNet
  • Discussion
    • Main Regards
    • AgingGenes
    • Análise de Co-Expressão: AgingNet
  • Conclusions
    • Final Regards
  • Citations
    • References
  • Appendix
    • Supplementary Files
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  1. Methods

3.4. Creation of age-representative samples: AgeCollapsed

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All studies were transformed into a new dataset called ageCollapsed, in which all age-matched samples, regardless of study origin, were combined into a single representative sample of that age. Once the studies were aggregated through their common transcripts, the expression values were corrected for the batch effect (in this case, the studies) using the Combat package (JOHNSON; LI; RABINOVIC, 2007) from R. Then, it was combined age-matched samples are measured by the median expression value of each transcript. The median was used as it is a metric less susceptible to outliers. Once the age samples were collapsed, the age-representative samples were reordered in ascending order and each transcript treated as a time series, as exemplified in Figure 3.7. All subsequent analyzes were performed on ageCollapsed.

Figura 3.4. Criação de amostras representativas das idades. Esquema ilustrando o colapsamento de amostras com a mesma idade através da mediana de cada gene.