12.3 RNA-Seq Data Analysis and Differential Expression

RNA-seq analysis is a powerful tool for understanding gene expression. It allows us to see which genes are active in different cells or conditions, helping us uncover the molecular basis of biological processes and diseases.

This topic dives into the nitty-gritty of RNA-seq data processing and differential expression analysis. We'll learn how to turn raw sequencing data into meaningful insights about gene activity, uncovering which genes are turned on or off in different scenarios.

RNA Sequencing Basics and Applications

RNA-seq Technology and Workflow

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• RNA sequencing (RNA-seq) quantifies and analyzes the transcriptome, providing a snapshot of RNA expression in biological samples
• RNA-seq workflow involves RNA extraction, library preparation, sequencing, and data analysis, each requiring specific protocols and quality control measures
• Detects both known and novel transcripts, enabling discovery of new genes, splice variants, and non-coding RNAs
• Offers advantages over microarrays including higher sensitivity, broader dynamic range, and ability to detect novel transcripts without prior gene sequence knowledge

RNA-seq Applications and Specialized Techniques

• Gene expression profiling measures the activity of genes in a sample
• Differential expression analysis compares gene expression levels between conditions (healthy vs. diseased tissue)
• Identification of alternative splicing events reveals different mRNA isoforms produced from the same gene
• Detection of gene fusions uncovers abnormal joining of two previously separate genes (common in cancer)
• Allele-specific expression analysis examines expression differences between maternal and paternal alleles
• Single-cell RNA-seq (scRNA-seq) analyzes gene expression at individual cell level, providing insights into cellular heterogeneity and rare cell populations
• Long-read RNA sequencing (PacBio, Oxford Nanopore) enables sequencing of full-length transcripts, facilitating study of complex splicing patterns and isoform diversity

RNA-Seq Data Processing and Analysis

Quality Control and Read Alignment

• Quality control of raw sequencing data assesses sequence quality scores, GC content, and presence of adapter sequences or contaminants
• Read alignment to reference genome or transcriptome uses specialized algorithms (STAR, HISAT2, Kallisto) accounting for splicing events and RNA-seq specific genomic features
• Evaluation of RNA-seq specific quality metrics includes percentage of mapped reads, gene body coverage, and strand specificity

Transcript Quantification and Normalization

• Transcript abundance estimation methods (RSEM, Salmon) use probabilistic models to quantify gene expression levels, accounting for read mapping uncertainty and transcript length
• Normalization techniques adjust for differences in sequencing depth and gene length, enabling comparisons across samples and genes
  • TPM (Transcripts Per Million)
  • FPKM (Fragments Per Kilobase Million)
  • ###deseq2's_size_factors_0###
• Batch effect correction methods (ComBat, RUVSeq) remove unwanted technical variation that may confound biological signals in multi-sample experiments

Data Exploration and Visualization

• Principal component analysis (PCA) reduces dimensionality of data to visualize sample relationships and identify major sources of variation
• Hierarchical clustering groups samples or genes based on expression similarity, revealing patterns and potential subgroups
• Heatmaps display expression levels of multiple genes across samples, allowing for visual identification of expression patterns
• Gene body coverage plots assess the uniformity of read distribution along transcripts, helping identify potential biases in library preparation or sequencing

Differential Gene Expression Analysis

Statistical Frameworks and Methods

• Differential expression analysis frameworks (DESeq2, edgeR, limma-voom) model count data using negative binomial distributions
• Employ empirical Bayes methods to improve variance estimates, particularly beneficial for experiments with few replicates
• False discovery rate (FDR) controls for Type I errors in multiple hypothesis testing using methods like Benjamini-Hochberg procedure
• Fold change and p-value thresholds determine significantly differentially expressed genes
  • Common thresholds: |log2(fold change)| > 1 and adjusted p-value < 0.05
  • Choice of cutoffs depends on experimental design and research questions

Specialized Analytical Approaches

• Time-course RNA-seq experiments use specialized tools (maSigPro, ImpulseDE2) to identify genes with significant temporal expression patterns
• Differential splicing analysis tools (rMATS, DEXSeq) detect changes in alternative splicing events between conditions
• Power analysis and sample size estimation determine number of biological replicates needed to detect differentially expressed genes with desired statistical power
  • Factors considered: effect size, desired false discovery rate, sequencing depth

Visualization and Interpretation Tools

• Volcano plots display both statistical significance (-log10(p-value)) and magnitude of change (log2(fold change)) for all genes
• MA plots show relationship between mean expression level and log2(fold change) for each gene
• Heatmaps of differentially expressed genes visualize expression patterns across samples and conditions
• Interactive visualization tools (e.g., Glimma) allow for exploration of differential expression results and associated statistics

Interpreting Differentially Expressed Genes

Functional Enrichment Analysis

• Gene Ontology (GO) enrichment analysis identifies overrepresented biological processes, molecular functions, or cellular components among differentially expressed genes
• Pathway analysis tools (KEGG, Reactome) contextualize differentially expressed genes within known biological pathways and signaling cascades
• Gene set enrichment analysis (GSEA) detects coordinated changes in predefined gene sets, even when individual genes may not meet significance thresholds
  • Useful for identifying subtle but consistent changes in biological processes

Network and Systems-level Analysis

• Network analysis techniques reveal functional relationships between differentially expressed genes
  • Protein-protein interaction networks show physical associations between gene products
  • Gene regulatory networks illustrate transcriptional control mechanisms
• Integration of RNA-seq data with other omics data types provides comprehensive understanding of gene regulation and cellular processes
  • ChIP-seq data can link changes in gene expression to alterations in transcription factor binding
  • Proteomics data can reveal post-transcriptional regulation affecting protein levels

Validation and Contextual Interpretation

• Comparison of differential expression results with publicly available datasets (GEO, ArrayExpress) helps validate findings and place them in broader biological context
• Literature-derived gene signatures aid in interpreting expression changes in light of known biological phenomena (cell cycle, inflammation)
• Experimental validation of key differentially expressed genes crucial for confirming RNA-seq results
  • qPCR verifies expression changes for individual genes
  • Western blotting confirms changes at protein level
  • Functional assays (e.g., gene knockdown, overexpression) establish biological relevance of identified genes