Suppression of Ribosome Biogenesis Underlies Aging-Like Symptoms Following COVID-19: Multi-Omics Investigations with Aging Clock (RiboAge)

Data S1. Genes set used for WGCNA. Data S2. Matrix for mRNA (TPM). The TPM (Transcripts Per Million) matrix, generated from bulk RNA-seq raw data through upstream processing and normalization, records the normalized expression level of each gene per sample. This matrix is suitable for investigating gene expression-age relationships via linear modeling across different groups, as well as for analyses such as WGCNA. Data S3. Matrix for mRNA (Count). The count matrix generated from bulk RNA-seq raw data following standard upstream processing, which documents the expression count of each gene per sample. This matrix is suitable for differential expression analysis using Deseq2. Data S4. Matrix for protein. The protein expression matrix, derived from the upstream processing of raw mass spectrometry proteomics data, records the abundance of each protein in every sample. This matrix is suitable for differential expression analysis, linear regression modeling, and subsequent investigations following processing with DEP2. Data S5. Matrix for metabolitics. The metabolite expression matrix, derived from the upstream processing of raw metabolomics data, records the relative abundance of each metabolite in every sample. This matrix is suitable for differential analysis within the MetaboAnalyst 6.0 platform. Following normalization, it can be utilized for linear regression modeling and subsequent in-depth investigations. Data S6. Matrix for clinical data.