deconvMe data

We generated sample-matched readouts from PBMCs of chronic Hepatitis C virus-infected patients for DNAm using Illumina EPIC arrays, gene expression using RNA-seq, and proportions of selected immune cells using flow cytometry. This repository contains the processed TPM and count matrices for RNAseq, the beta matrix and two matrices quantifying the methylated and unmethylated probes, and a metadata table that gives information on sample IDs, gender, and flow-cytometry-derived immune cell-type fractions:<br>RNA-seq:tpm.csvcounts.csvDNAm:meth.csvunmeth.csvbeta.csvmetadata + flow cytometry (long format, FACS values given in %):meta_flow.csv<br><br>Raw <i>fastq</i> files from the RNA sequencing have been processed using the nf-core rnaseq pipeline version 3.10. The main steps include a manual inspection of quality using <i>FastQC</i>, followed by pseudo-alignment and gene quantification using <i>Salmon</i>. Next to raw gene expression counts, this method generates transcript per million (TPM) counts, which are normalized by gene length and library size and were used as input for deconvolution methods.DNAm data by Illumina EPIC arrays generated raw <i>idat</i> files that have been processed with <i>RnBeads</i> 2.0 using the default settings during preprocessing, which removes methylation sites with too many missing values, cross-reactive probes, or those that overlap with known single-nucleotide polymorphisms (SPNs). Additionally, we removed all CpGs with NA values in any sample. The final dataset consisted of 724,082 CpGs. We removed CpGs located on sex chromosomes as they would confound the analysis.