12 posts
A practical pipeline for normalizing personal proteomics data, from raw intensity matrices through log transformation, method selection, batch correction, and longitudinal re-normalization when new draws arrive.
A working guide to setting up an R and Bioconductor project around a personal molecular profile: loading your VCF, quantifying your own RNA-seq across timepoints, normalizing proteomics, and joining biomarker and glucose time series into one analyzable object.
A step-by-step guide to turning transcript quantifications into plots that tell you something: sample-level QC, longitudinal gene trajectories, differential expression views, and pathway summaries, with the R and command-line code to produce them.
A working pipeline for taking a personal WGS VCF into R: GDS conversion, quality metrics, ancestry PCA against 1000 Genomes, variant annotation, and polygenic score computation, with the failure modes that silently corrupt each step.
A practical walkthrough of differential expression analysis in R, from transcript quantification through DESeq2 and limma models, with the design decisions and failure modes that determine whether the gene list means anything.
A practical guide to computing principal components from RNA-seq counts in R. Covers the transform, gene selection and what each component means.
A working pipeline from salmon quantification files to a differential expression table and pathway results, written for one person's longitudinal blood transcriptome rather than a two-group lab experiment.
A step-by-step guide to quantifying, normalizing, modeling, and interpreting your own RNA-seq data in R, from FASTQ files through differential expression, pathway scoring, and blood cell deconvolution.
A working guide to running DESeq2 on bulk RNA-seq counts. Covers the count matrix, dispersion, shrinkage and the sanity checks that catch most mistakes.
How to use R and Bioconductor as the annotation, statistics, and integration layer for a personal molecular dataset: VCFs, RNA-seq counts, proteomics, and continuous glucose data, with the parts you should not do in R.
How to run a genome-wide association study in R end to end, from VCF to QC to association testing to polygenic scores, and what you can and cannot do with a single genome.
A working pipeline for taking Olink, SomaScan, or DIA-NN output into R: QC, missingness, normalization, and longitudinal within-person modeling of a single individual's plasma proteome.