Data Analysis With R and Python

RNAseq_metadata_airway
│
├── Inspect metadata
│ ├── Sample names
│ ├── Experimental groups
│ ├── Replicates
│ └── Missing/incorrect values
│
RNAseq_count_matrix_airway
│
├── Inspect count matrix
│
├── Match count-matrix columns with metadata rows
│
├── Reorder samples if necessary
│
├── Filter low-count genes
│
▼
Create DESeq2 object
│
├── Experimental design
│ └── ~ dex
│
▼
DESeq2 normalization
│
├── Raw counts
├── Size factors
├── Normalized counts
└── VST / rlog transformed counts
│
▼
Quality Control & Sample Exploration
│
├── Library size
├── Count distribution
├── Sample-to-sample correlation
├── Sample distance
├── Hierarchical clustering
├── PCA
└── Sample heatmap
│
▼
Differential Expression Analysis
│
├── DESeq2 model fitting
├── Treated (trt) vs Untreated (untrt)
├── BaseMean
├── log2 Fold Change
├── Standard error
├── P-value
└── Adjusted P-value (FDR)
│
├── MA plot
├── Volcano plot
└── DEG heatmap
│
▼
Significant DEGs
│
├── Upregulated genes
├── Downregulated genes
├── DEG summary
└── Top DEG table
│
▼
Gene ID Annotation
│
├── Ensembl ID
├── Entrez ID
└── Gene symbol
│
├─────────────────────────────┐
▼ ▼
GO Enrichment KEGG Enrichment
│ │
├── BP ├── KEGG pathway table
├── CC ├── Dot plot
└── MF └── Bar plot
│
├── Tables
├── Dot plots
└── Bar plots
│
└──────────────┬──────────────┘
▼
Biological Interpretation
│
├── Key genes
├── Key pathways
├── Treatment response
└── Publication-ready results

Why is R essential for advanced biological data analysis?

Modern biological research often involves complex experiments with multiple treatments, genotypes, environments, traits, and repeated measurements. R allows researchers to go beyond basic ANOVA and perform mixed-effects models, GLMs, multivariate analysis (PCA, PCoA, NMDS), PERMANOVA, clustering, dose–response modeling, survival analysis, correlation networks, SEM, machine learning, and omics analysis.

It also provides post-hoc comparisons, compact letter displays, publication-quality visualization, and fully reproducible statistical workflows—making R especially valuable for turning complex biological experiments into defensible, publication-ready results.

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