Tag: deseq2

A working guide to RNA-seq analysis for one person's data: library choices, depth, QC thresholds, and how to analyze repeated samples.

A step-by-step pipeline for turning bulk RNA-seq FASTQ files into a gene-level count matrix, differential expression results, and pathway scores, with the specific tools, flags, and QC thresholds we use.

A step-by-step guide to turning your own blood RNA-seq FASTQ files into a normalized expression matrix, cell-type estimates, and pathway scores you can track across timepoints.

A working pipeline for taking personal bulk RNA-seq from FASTQ to quantified transcripts, quality-controlled expression matrices, and interpretable gene-level signals, with the commands and parameters we use.

A practical pipeline for taking your own whole-blood RNA-seq from FASTQ to a quantified, QC'd expression matrix you can track over time, with the parameters, checks, and failure modes that matter.

A working guide to taking raw RNA-seq counts from your own samples through filtering, effective-length correction, TMM or median-of-ratios scaling, and variance stabilization, with the diagnostics that tell you whether the normalization held.

A working guide to gene set enrichment analysis on personal RNA-seq: building a defensible ranked list with DESeq2, running fgsea against MSigDB, reading the enrichment plot, and using GSVA when you have no contrast.

A working pipeline for taking personal bulk RNA-seq from raw FASTQ files to quantified expression, quality checks, reference comparison, and longitudinal analysis, with the specific tools, flags, and failure modes.

A step-by-step guide to going from FASTQ files to gene-level counts, cell-composition estimates, and longitudinal comparisons using your own transcriptome data.

A practical walkthrough from FASTQ to gene-level counts, cell-composition estimates, and pathway scores, written for someone analyzing their own blood RNA-seq rather than a two-group experiment.

A practical guide to designing, quantifying, and modeling a longitudinal RNA-seq series from one person — sampling schedule, salmon quantification, spline and impulse models, trajectory clustering, and the confounders that will ruin the result if you ignore them.

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 pathway table in R. Covers salmon, DESeq2 and fgsea, plus the confounders behind most false pathway hits.

A working transcriptome analysis workflow for one person's whole-blood RNA-seq. Covers QC, salmon quantification, normalization and pathways.

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. Covers salmon quantification, gene selection, row scaling and clustering.

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. 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.

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.