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How to Analyze Single-Cell RNA-seq Data – Complete Beginner’s Guide Part 4: Cell Type Identification
Introduction: From Clusters to Biological Identities In Part 1, 2, 3 of this tutorial series, we’ve taken our scRNA-seq data from raw FASTQ files through quality control, integration, and clustering. We now have groups of cells that cluster together based on transcriptional similarity—but what are these cells? Cell type identification transforms abstract “Cluster 0, Cluster…
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How to Analyze Single-Cell RNA-seq Data – Complete Beginner’s Guide Part 3: Integration and Clustering
Introduction: Why Integration Matters in Multi-Sample scRNA-seq Analysis In Part 1 and Part 2 of this tutorial series, we processed PBMC samples from the GSE174609 dataset through the complete pipeline: from raw FASTQ files to quality-controlled count matrices. Now we face a critical question: How do we analyze multiple samples together to identify cell types…
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How to Analyze Single-Cell RNA-seq Data – Complete Beginner’s Guide Part 2: Quality Control and Cell Filtering
Introduction: Learning QC Through a Single-Sample Deep Dive Quality control in single-cell RNA sequencing is complex, with multiple layers of filtering and validation. Before tackling multi-sample experiments, it’s essential to understand the QC workflow thoroughly using a single sample. This focused approach allows you to: IMPORTANT: This QC workflow should be applied independently to each…
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How to Analyze Single-Cell RNA-seq Data – Complete Beginner’s Guide Part 1: From FASTQ to Count Matrix
A comprehensive step-by-step tutorial for analyzing 10x Genomics single-cell RNA sequencing data using Cell Ranger Introduction: Understanding Single-Cell RNA Sequencing The revolution in molecular biology has been marked by our ability to zoom in from cell populations to individual cells. This shift reveals hidden heterogeneity that bulk measurements mask. Single-cell RNA sequencing (scRNA-seq) is one…
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How to Build Gene Regulatory Networks from RNA-seq Data Using GENIE3 – Complete Step-by-Step Guide For Absolute Beginners
Introduction: Understanding Gene Regulatory Network Inference In the complex choreography of cellular function, transcription factors (TFs) act as master conductors, orchestrating when and where genes are expressed. Understanding which transcription factors regulate which target genes is fundamental to deciphering how cells respond to stimuli, how developmental programs unfold, and how diseases emerge from regulatory dysfunction.…
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How to Build Gene Co-expression Networks from RNA-seq Data Using WGCNA – Complete Step-by-Step Guide For Absolute Beginners
Introduction: Understanding Gene Co-expression Networks In the intricate machinery of living cells, genes rarely act in isolation. Instead, they work together in coordinated networks, with groups of genes being co-expressed to carry out specific biological functions. Understanding these relationships is fundamental to deciphering how cells respond to stimuli, how diseases develop, and how we might…
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How To Perform Genome-Wide Association Analysis (GWAS) For Absolute Beginners: From Raw Variants to Disease-Associated Loci Using PLINK
Introduction: Understanding Genome-Wide Association Studies After successfully calling variants from whole genome sequencing data (covered in Part 1 of our WGS series), you now have VCF files containing millions of genetic variants across multiple individuals. But which of these variants contribute to disease risk or influence quantitative traits? This is where Genome-Wide Association Studies (GWAS)…
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How to Cluster RNA-seq Data to Uncover Gene Expression Patterns: Hierarchical and K-means Methods for Absolute Beginners
Introduction: Understanding Clustering in RNA-seq Analysis In the vast landscape of gene expression data, patterns often hide in plain sight. Among thousands of genes measured simultaneously, groups of genes may share similar expression patterns across samples, suggesting coordinated biological functions or responses. Clustering analysis serves as a powerful computational microscope that brings these hidden patterns…
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How To Analyze Whole Exome Sequencing Data For Absolute Beginners: From Raw Reads to High-Quality Variants, Mutations, and CNVs
Introduction: Understanding Whole Exome Sequencing vs. Whole Genome Sequencing What is Whole Exome Sequencing (WES)? Whole Exome Sequencing (WES) is a targeted sequencing approach that focuses specifically on the protein-coding regions of the genome, known as exons. While the human genome contains approximately 3.2 billion base pairs, the exome represents only about 1-2% of this…
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How To Analyze Whole Genome Sequencing Data For Absolute Beginners Part 6-2: Identifying Tumor Copy Number Variants Using CNVkit
Introduction: Understanding Tumor Copy Number Variants Cancer is fundamentally a disease of genomic instability, where normal cells accumulate mutations that drive uncontrolled growth and metastasis. Among these mutations, somatic copy number alterations (SCNAs) – also known as tumor CNVs – play a pivotal role in cancer initiation, progression, and treatment resistance. This tutorial builds upon…
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Recent Posts
- How to Set Up a Bulk RNA-seq Pipeline on an HPC Cluster — A Complete Beginner’s Guide to Nextflow and nf-core/rnaseq
- How to Choose Your scRNA-seq QC Tools (Part2-2): SoupX vs DecontX and DoubletFinder vs scDblFinder
- How to Analyze Single-Cell RNA-seq Data — Complete Beginner’s Guide Part 17: Infer Signaling Pathway Activity with decoupleR and PROGENy
- How to Analyze Single-Cell RNA-seq Data — Complete Beginner’s Guide Part 16: Build Gene Regulatory Networks with decoupleR and CollecTRI
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Alternative Splicing Analysis ATAC-seq BAM ChIP-seq chromatin accessibility CNV DESeq2 Differential Expression edgeR FASTQ GATK Mutect2 gene expression heatmap HOMER HPC Isoform limma MACS2 MAF miRNA miRNA-seq MSigDB Normalization peak calling RNA-seq SLURM somatic mutations Transcript VCF whole genome sequencing