Tag: data-analysis

Why fasting glucose is often the first value to drift upward, how to tell a real dawn rise from a sensor artifact, and how to analyze your own overnight CGM traces.

A practical comparison of mass spectrometry, affinity panels, and aptamer arrays as purchasable proteomics services, with the file formats, tools, and failure modes you should expect if you plan to analyze the data yourself.

A high fasting glucose usually reflects overnight hepatic glucose output and the pre-waking hormone surge. How to check it in your own CGM export.

The thresholds that define a high glucose value, why a CGM reading and a lab draw disagree, and how to analyze your own glucose export instead of reacting to single numbers.

Current out-of-pocket prices for continuous glucose monitors, the cost per day of usable data, and what the data looks like when you export it yourself.

A file-by-file walkthrough of RNA-seq output: FASTQ, BAM, quant.sf, count matrices, and QC reports, plus the pipeline and the interpretation rules we would use on your own data.

A practical pipeline for turning vendor raw files from a plasma proteomics run into an FDR-controlled protein quantification matrix you can model, with the tools, flags, and failure modes named.

A practical guide to choosing between untargeted and targeted metabolomics providers, what you get in the deliverable, and how to process the data yourself in mzML, XCMS, and MetaboAnalyst.

A practical pipeline for taking raw whole-genome sequencing data from FASTQ to annotated variants, structural variants, pharmacogenomic diplotypes, and polygenic scores, with the QC steps that decide whether any of it means anything.

A blood panel measures a few dozen analytes in one tube at one moment. Here is what that covers, why single results mislead, and how to store and analyze your own results over time.

A working method for reading your own labs: get the data as structured records, compare against your own history rather than the population reference interval, and use the reference change value to tell a real shift from noise.

A fasting glucose of 99 mg/dL is one number from one morning. Here is how to compute your own morning baseline from CGM data, what the dispersion means, and where the measurement itself is the limiting factor.

What fasting, post-meal, and 24-hour continuous glucose numbers look like in people without diabetes, what the reference data say, and how to analyze your own CGM export.

What CGM error looks like in the data: interstitial lag, compression artifacts, sensor-to-sensor bias, and a protocol for measuring your own sensor's accuracy against fingersticks using the raw exports.

List prices for SomaScan 7K and 11K at core facilities run $800–900 per sample before shipping, plate minimums, and analysis. Here is where that money goes, how the assay works, and how to decide between SomaScan, Olink Reveal, and a targeted panel.

What a CGM measures, which over-the-counter sensor to buy and how to export the raw data as CSV. Covers the metrics and failure modes that matter.

What post-meal glucose looks like in people without diabetes, where the 140 and 180 mg/dL thresholds come from, and a protocol for extracting real numbers from your own CGM export.

A practical map of the software that turns raw multi-omics files into results you can query, with the commands, formats, and failure modes that matter for a single person's data.

A working guide to taking personal proteomics data from raw instrument files or NPX tables to a quantitative matrix, QC'd and normalized, and then to within-person statistics you can defend.

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 continuous glucose monitor measures how you eat. It does not cause fat loss. What the data supports, and how to run your own meal experiments.

Overnight glucose normally falls to its lowest point of the day a few hours after sleep onset. This page explains what a normal nocturnal curve looks like, how to analyze your own CGM data for real low events, and how to recognize the compression artifacts that mimic them.

A practical guide to wearing a CGM without diabetes. Covers which sensors to buy, how accurate they are and what a normal glucose curve looks like.

A practical guide to choosing a continuous glucose monitor as a non-diabetic athlete, getting the raw data out, and analyzing it without over-reading sensor noise.

A working method for turning a raw Libre or Dexcom export into an ambulatory glucose profile, per-meal response curves, and metrics you can defend — including what the numbers can and cannot tell you.

A working guide to pulling your lab data out of patient portals, normalizing it into one machine-readable table, computing derived values yourself, and telling a real change from assay noise.

DNA age tests read cytosine methylation at a few hundred CpG sites and regress it onto chronological age. Here is how the assay works, which clocks are worth running, what the error bars really are, and how to process the raw data yourself.

A practical workflow for taking your RNA-seq FASTQ files to transcript quantification, then using web-based analysis tools for the parts they do well. Includes commands, parameters, cost estimates, and the failure modes that waste the most time.

What the post-meal glucose numbers are for people without diabetes, where the thresholds come from, and how to compute peak, time-to-peak, and incremental AUC from your own CGM export.

A CGM measures the output of your glucose system. It does not measure the input. What a sensor can and cannot tell you about insulin sensitivity.