Tagged: tutorial
46 posts found
Cell Line Pathway Analysis: Methods and Comparisons
Choose valid RNA-seq designs for pathway comparisons across cell lines, including additive adjustment, stratified effects, and treatment-by-cell-line interactions.
How Are Biological Pathways Connected?
Learn how shared genes, leading-edge drivers, transcriptional regulation, and co-expression connect pathways and how to build an interpretable pathway network.
How to Build a Biological Pathway Diagram
Build a scientifically honest pathway diagram by defining scope, typing nodes and edges, mapping identifiers, overlaying data, and exporting a reproducible graph.
How to Do Pathway Analysis with RNA-Seq
A defensible RNA-seq pathway workflow: design contrasts, define the tested gene universe, rank genes, choose databases, control FDR, and audit driver genes.
How to Interpret Pathway Analysis Results
Learn how to read pathway scores, FDR, direction, leading-edge genes, redundancy, topology evidence, and missing results without overstating the biology.
How to Compare Pathways Across Datasets
Compare pathway results across contrasts, studies, platforms, and species without losing direction, provenance, uncertainty, or missing values.
Pathway Enrichment vs Pathway Activity
Pathway enrichment tests gene-set membership or rank shift; pathway activity models signed target responses. Learn why the scores differ and how to interpret each.
Pathway Visualization in Python
Build pathway lollipop plots, evidence matrices, ranked-gene barcodes, overlap networks, and regulator footprints in Python without hiding direction, FDR, or missing data.
What Is a Biological Pathway?
A clear, technical guide to biological pathways, gene sets, pathway diagrams, signed graphs, networks, and how NotchBio keeps each evidence layer distinct.
What Is Pathway Analysis?
Pathway analysis explained: choose ORA, ranked enrichment, topology analysis, or activity inference from the input and biological question.
RNA-Seq Read Length: Why Trimming Removes All Your Reads
Why RNA-seq trimming can discard every read, how read length and paired-end mismatches produce empty FASTQ files, and what to check before you run a pipeline.
How to Read a FastQC Report: Warnings, Failures, and Fixes
A senior bioinformatician walks through the FastQC sections that real beginners miss, with screenshots and decisions to make at each step.
STAR vs Salmon vs HISAT2: A Hands-On Benchmark
A hands-on RNA-seq aligner benchmark: working STAR, Salmon, and HISAT2 commands, real runtime and memory numbers, and how much the DEG list actually changes.
Salmon From FASTQ to Counts: A Complete Tutorial
A complete Salmon tutorial with decoy-aware indexing, quantification flags explained, tximport into R, DESeq2 integration, and QC checks at every step.
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.
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.
GTF vs GFF3 Files: Format Differences and RNA-Seq Fixes
GTF and GFF files from the same database often disagree, prokaryotic files lack exon features, AGAT fixes some and breaks others. A practical field guide.
Nextflow RNA-Seq Tutorial: Build Your First Pipeline
A minimal Nextflow DSL2 RNA-seq pipeline in under 80 lines: three processes, channel wiring, Docker config, and how to read the execution report and DAG output.
Reducing GO Term Redundancy: simplify, rrvgo, and What Works
After enrichment you get hundreds of overlapping GO terms. A tutorial on clusterProfiler simplify, rrvgo, REVIGO, and a custom uniqueness-score fallback.
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.
fastp vs Trimmomatic vs BBDuk: A Benchmark on RNA-Seq Reads
A side-by-side benchmark of fastp, Trimmomatic, and BBDuk on paired-end RNA-seq data: speed, post-trim quality, mapping rate, and downstream DEG impact.
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.
Bulk RNA-Seq Deconvolution: CIBERSORTx and MuSiC Tutorial
Estimate cell type proportions from bulk RNA-seq using CIBERSORTx and MuSiC. Reference selection, batch correction, validation, and result interpretation.
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.
How to Build a DESeq2 Count Matrix in Python
Python tutorial: parse featureCounts output, aggregate Salmon quant.sf, build a tx2gene map, and save a DESeq2-ready integer count matrix with pandas.
STAR RNA-Seq Alignment Tutorial: FASTQ to Gene Counts
Complete STAR tutorial: download genome and GTF, build an index with the right sjdbOverhang, run paired-end alignment, and load GeneCounts into R for DESeq2.
How to Build a Decoy-Aware Salmon Index for RNA-Seq
Step-by-step Salmon tutorial: download GENCODE references, build a decoy-aware index, run salmon quant with gcBias and seqBias, and verify mapping rates.
FASTQ Quality Control: FastQC, fastp, and MultiQC Workflow
Bulk RNA-seq QC end to end: run FastQC on raw reads, trim adapters with fastp, rerun QC, and aggregate everything into one MultiQC report, with parallel runs.
How to Download RNA-Seq FASTQ Data from GEO and SRA
Download bulk RNA-seq FASTQ files from GEO and SRA: prefetch, fasterq-dump, pysradb metadata, batch downloads, and fixes for the most common errors.
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.
RNA-Seq PCA and Sample Clustering in Python: QC Tutorial
Python tutorial: normalize RNA-seq counts, run PCA with scikit-learn, build a sample distance heatmap, and spot outliers before differential expression.
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.
Set Up an RNA-Seq Conda Environment on Ubuntu or macOS
Install Miniforge, conda, bioconda, R 4.4, and DESeq2 for bulk RNA-seq: reproducible environments, version pinning, and fixes for common install errors.
Salmon quant.sf Explained: Mapping Rates and DESeq2 Import
Understand Salmon quant.sf columns, mapping rates, effective length, NumReads, TPM, and the correct handoff to tximport, tximeta, and DESeq2.
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.
STAR vs HISAT2 vs Salmon: Which Aligner Should You Use?
STAR aligns to the genome, HISAT2 uses less memory, Salmon skips alignment. What each approach means for your RNA-seq results and when each is the right call.
RNA-Seq Sample Preparation: RNA Extraction to FASTQ Files
RNA-seq sample preparation from RNA extraction and quality checks through library preparation, Illumina sequencing, base calling, and FASTQ files.
fastp vs Trimmomatic: RNA-Seq Adapter Trimming Tutorial
When adapter trimming helps, when it hurts, and how to run Trimmomatic and fastp on RNA-seq data with the parameter choices that actually matter.
How to Run FastQC and MultiQC on Multiple FASTQ Files
A hands-on guide to automating RNA-seq QC across dozens of samples using FastQC and MultiQC, with bash and Python scripts for parsing and flagging failures.
Bulk RNA-Seq Pipeline: FASTQ to Gene Counts Step by Step
Every computational step in bulk RNA-seq, explained: from FASTQ quality control through trimming, alignment, and quantification to your final count matrix.