Tagged: differential-expression
28 posts found
GO Enrichment Analysis Mistakes: Backgrounds, FDR, and Bias
A 2022 PLOS Computational Biology study found 43% of GO enrichment analyses skip multiple test correction. Here is what that means and how to do it right.
RNA-Seq Results Checklist for Publication and Peer Review
Reviewers reject RNA-seq papers for predictable reasons: missing FDR correction, version-less methods, inaccessible data. A checklist that prevents it.
How to Write an RNA-Seq Methods Section: Template and Checklist
A reviewer-proof RNA-seq methods section is shorter than you think but far more specific. Templates, required elements, and what reviewers always flag missing.
PyDESeq2 vs DESeq2 in R: Validation and Scanpy Workflow
Does PyDESeq2 really match R DESeq2? A tutorial on validating results against R, running PCA with scanpy and AnnData, and exporting DEGs for enrichment.
Publication-Ready RNA-Seq Plots in R with ggplot2
Reviewer-ready RNA-seq plots in R: volcano with gene labels, z-score heatmap with annotation bars, PCA with variance explained, and journal export settings.
Bacterial RNA-Seq Pipeline: Operons, Counts, and DESeq2
Most bulk RNA-seq pipelines fail silently on bacterial data. Here is what changes for operons, GTF feature mismatches, and DE analysis in prokaryotes.
ORA vs GSEA in R: clusterProfiler Pathway Analysis Tutorial
ORA and GSEA answer different questions. A working clusterProfiler tutorial with FDR correction, proper backgrounds, and side-by-side result interpretation.
Why Your DESeq2 Log2 Fold Change Cutoff Of Zero Is Wrong
What log2 FC means in RNA-seq, how to convert it to fold change, choose a defensible DESeq2 cutoff, apply lfcShrink, and avoid noisy DEG lists.
Pathway Enrichment Analysis: GSEA and ORA in R and Python
Pathway enrichment end to end: GSEA and ORA in R with clusterProfiler and fgsea, plus the Python equivalent with gseapy, across MSigDB, KEGG, and GO sets.
DESeq2 Tutorial in R: Count Matrix to Volcano Plot
A complete DESeq2 tutorial in R: loading counts, building the design formula, running DE, applying lfcShrink, generating a volcano plot, and exporting results.
DESeq2 Contrasts: Multiple Conditions and Multi-Factor Designs
Three conditions, paired designs, two-factor experiments, and time courses: how to build the design formula, specify contrasts, and avoid common mistakes.
RNA-Seq Plots: Volcano, MA, and Heatmap in R and Python
Publication-ready RNA-seq plots in R and Python: volcano with ggplot2/ggrepel, MA plots, and DEG heatmaps with pheatmap and seaborn, plus 300 dpi export.
RNA-Seq Batch Correction: ComBat-Seq vs RUVSeq vs sva
How to choose a batch-effect correction tool: ComBat-Seq, RUVSeq, and sva compared, including unknown batch sources and reporting it in your methods.
Salmon to Differential Expression in Python with PyDESeq2
A pipeline-focused PyDESeq2 tutorial: load Salmon quant.sf into a count matrix, fit a DeseqDataSet, run Wald tests, apply apeGLM shrinkage, export DEGs. No R.
How to Run DESeq2 in R from Salmon quant.sf Files
DESeq2 in R from Salmon counts: import quant.sf with tximeta, build a DESeqDataSet, run the Wald test, apply apeglm shrinkage, and export a ranked DEG table.
Volcano and MA Plots in R: DESeq2 and ggplot2 Tutorial
Publication-quality volcano and MA plots from DESeq2 results in R: ggplot2 from scratch, ggrepel gene labels, EnhancedVolcano, and how to read them.
PyDESeq2 Tutorial: Differential Expression Analysis in Python
The complete PyDESeq2 reference in Python: DeseqDataSet, DeseqStats, apeglm shrinkage, multi-factor designs, multiple contrasts, and pandas result filtering.
DESeq2 Tutorial in R: From Count Matrix to Results
Step-by-step DESeq2 in R: build a DESeqDataSet, understand size factors and dispersion, run DESeq(), interpret the results columns, then shrink and filter DEGs.
tximport vs tximeta: Import Salmon quant.sf into DESeq2
Import Salmon quant.sf into R with tximeta and tximport: build a tx2gene table, fix ID-mismatch errors, and set up a DESeqDataSet for multi-factor designs.
Cell Line RNA-Seq Experimental Design: 7 Failure Modes
Seven cell line RNA-seq design failures—including passage drift, mycoplasma, serum lots, and pseudoreplication—with prevention and QC checks.
What Is GSEA? Gene Set Enrichment Analysis Explained
Gene Set Enrichment Analysis explained: ranked gene lists, enrichment scores, NES, FDR, leading-edge genes, and an fgsea tutorial in R.
When to Use edgeR vs DESeq2 vs limma-voom
edgeR vs DESeq2 vs limma-voom for RNA-seq: compare models, normalization, low-count behavior, complex designs, and when to use each method.
How DESeq2 Actually Works (Without the Math Overload)
The negative binomial model, size factors, dispersion shrinkage, and what each output column really means: DESeq2 explained for working researchers.
How to Detect and Correct RNA-Seq Batch Effects in DESeq2
How to detect batch effects with a PCA plot and correct them in DESeq2 using a design covariate, ComBat-seq, and limma removeBatchEffect for visualization.
RNA-Seq Count Matrix Explained: Raw Counts vs TPM vs FPKM
Raw counts, TPM, FPKM, and DESeq2-normalized values each represent expression differently. What each one is, why it matters, and which to use downstream.
RNA-Seq Experimental Design: 5 Mistakes That Break DESeq2
Replicates, confounders, paired designs, and pseudoreplication: the experimental design decisions that decide whether your DESeq2 results hold up.
Reference Genome Types for RNA-Seq: Does the Choice Change Results?
Compare GENCODE, Ensembl, RefSeq, and UCSC reference annotations for RNA-seq and learn how genome assembly and GTF choice change counts and DEGs.
What Are Batch Effects in RNA-Seq? Causes and Examples
What batch effects are, why they happen in bulk RNA-seq, and how they quietly corrupt your differential expression results — the concepts to grasp first.