A step-by-step guide to taking a protein abundance matrix, building a defensible ranking statistic, and running over-representation and rank-based enrichment in R, including how to read a negative enrichment score.
A comparison of the companies that will sequence or map your genome, what data each one hands back, and how to judge an offer by coverage, read length, and file deliverables rather than marketing copy.
What whole-genome sequencing results contain, which files matter, how to interpret a variant classification, and where the analysis stops and a clinician starts.
A working guide to building the handful of plots that matter for a personal molecular dataset: coverage tracks, variant pileups, copy-number profiles, expression MA and volcano plots, and a genome-wide circular summary. With real commands, file formats, and the failure modes that produce misleading pictures.
A practical guide to wearing a continuous glucose monitor around training: sensor placement, interstitial lag, exporting raw CSV data, the metrics worth computing, and the artifacts that will fool you.
A working guide to running KEGG enrichment on personal RNA-seq: quantification, identifier mapping, GSEA versus over-representation, reading the maps, and the failure modes that make most pathway results meaningless.
What array, exome, and whole-genome testing each measure, which files to insist on, and a concrete pipeline for calling and annotating your own variants.
A working guide to producing star-allele diplotypes and CPIC phenotypes from your own sequencing data with PharmCAT, Cyrius, and PyPGx, and to reading a commercial PGx report critically.
A working guide to turning RNA-seq quantifications into an annotated, versioned gene table: choosing an annotation, building tx2gene, attaching biotypes and symbols, functional mapping, and cell-type annotation for single-cell data.
A working pipeline from FASTQ to a ranked, deduplicated pathway table in R: salmon and tximport, DESeq2 ranking statistics, fgsea against MSigDB, and the confounders (cell composition, globin, gene length) that produce most false pathway hits.
A working transcriptome analysis workflow for a single person's whole-blood RNA-seq: QC, selective-alignment quantification with salmon, gene-level counts, normalization across timepoints, cell-composition correction, and pathway interpretation.
A working guide to pulling your lab results out of a patient portal as structured data, normalizing them, and analyzing them with the right statistics: reference intervals, analytical and biological variation, and reference change values.
A technical account of what a comprehensive blood panel measures, what the CMP's 14 analytes do and don't tell you, and how to get from a panel of numbers to data you can analyze yourself.
Standard continuous glucose monitors measure glucose only. Abbott's Libre Duo 10 Day adds a beta-hydroxybutyrate electrode on the same filament. Here is what each sensor measures, what the data looks like, and how to analyze it.
What is in a VCF, how to filter it down to variants worth reading, how to read a single variant end to end, and what a negative result does not rule out.
A genome kit is worth buying only if it ships you 30x PCR-free whole-genome sequencing and the underlying files. Here is the spec sheet, the QC commands to run on delivery, and what drugstore DNA kits measure.
A genome report is a filtered summary built on top of your variant calls. Here is what goes into one, which files matter more than the PDF, and what sequencing costs in 2026.
A continuous glucose monitor does not cause fat loss, but it is a good instrument for measuring how you eat. Here is what the data supports, how to run your own meal experiments, and the failure modes that will fool you.
A working guide to HMDB's access paths: the licensed REST API, the per-record XML endpoints, and the bulk downloads. Includes a streaming parser, a DuckDB mirror, and mass-based lookup with ppm windows.
There are two products sold as a 'methylation report': an MTHFR-style SNP panel from genotyping data, and a genome-wide DNA methylation array read out as epigenetic clocks. They measure different things, and only one of them is a measurement of your current biology.