Regression After Gene Expression
- To explore ways of selecting features from a gene expression study for prediction of a patient characteristic.
- To investigate the pros and cons of features based on the principal components of the gene expression.
The gene expression data comes from,
Sayed N, Huang Y, Nguyen K, Krejciova-Rajaniemi Z et al. An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging. Nat Aging 2021 Jul;1:598-615.
GEO archive as GSE168753. File GSE168753_processed_data.csv.gz from https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE168753.
- 312 individuals
- Profiled using Illumina HumanHT-12 V4.0 expression beadchip
- Processed data has expression for 5000 genes
- Subject characteristics include, age, sex, BMI