18 posts
A concrete walkthrough of bulk RNA-seq: where to get a real example dataset, what the files contain, how to quantify with salmon, and how to get to a gene-level table you can interpret, including the case where you only have your own samples.
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.
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 concrete walkthrough of the RNA-seq workflow from FASTQ to interpreted results: quality control, pseudoalignment with salmon, count import with tximport, differential expression with DESeq2, and the sanity checks that catch most errors.
A working guide to clustering bulk and single-cell RNA-seq: normalization choices that decide your dendrogram, gene-module discovery across timepoints, choosing k defensibly, and the failure modes that produce confident nonsense.
A working pipeline from FASTQ to a publication-grade expression heatmap: salmon quantification, tximport, variance stabilization, gene selection, row scaling, clustering, and the sanity checks that keep you from reading noise.
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 step-by-step guide to producing a volcano plot from a count matrix with DESeq2 and ggplot2, choosing thresholds that mean something, and reading the resulting shape correctly, including the single-cell and mass-spectrometry variants.
A working guide to taking personal RNA-seq FASTQ files through quality control, quantification, sample sanity checks, and longitudinal within-person expression analysis, with the commands and the failure modes.
A working pipeline for taking whole-blood RNA-seq FASTQ files to a gene-level count matrix, differential expression across timepoints, pathway scores, and cell-type composition, with the parameters and failure modes that matter.
A working guide to turning RNA-seq FASTQs into the handful of plots that matter: sample PCA, distance heatmaps, MA and volcano plots, and longitudinal gene trajectories, with the commands, parameters, and failure modes.
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: which transform to use, how many genes to feed in, how to figure out what each component means, and when PCA is the wrong tool.
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: building the count matrix, filtering, size factors, dispersion, Wald versus likelihood ratio tests, log fold change shrinkage, and the sanity checks that catch most mistakes.
A post hoc |log2FC| > 1 filter is the wrong tool. Test against a fold-change threshold directly with DESeq2's lfcThreshold or edgeR's glmTreat, shrink effect sizes before ranking, and pick the threshold from your own replicate structure.