name: genomics description: Genomics and transcriptomics analysis strategies category: domain metadata: version: "1.0.0"

Genomics and Transcriptomics Analysis

When to Use This Skill

• When data contains gene expression measurements (RNA-seq, microarray)
• When analyzing differential gene expression
• When performing pathway or gene set enrichment analysis
• When interpreting genetic variants or mutations

Core Concepts

Gene Expression Data Types

RNA-seq counts:

• Raw read counts per gene
• Requires normalization (TPM, RPKM, DESeq2)
• Suitable for differential expression analysis

Microarray intensities:

• Probe fluorescence intensities
• Log-transformed, background-corrected
• Legacy platform, less common now

Single-cell RNA-seq:

• Expression per cell (not bulk tissue)
• High sparsity (many zeros)
• Specialized analysis methods
• ⚠️ For differential expression across conditions, aggregate to the sample level

(pseudobulk) — cells are not independent replicates (see below)

Gene Nomenclature

Human genes:

• Official symbols: HUGO Gene Nomenclature Committee (HGNC)
• Example: TP53 (tumor protein p53)
• Italicized in publications

Mouse genes:

• Similar to human but capitalization differs
• Example: Tp53 (first letter capital, rest lowercase)

Protein names:

• Not italicized
• Example: p53 protein

Always verify gene symbols - aliases and outdated names are common.

Differential Expression Analysis

Workflow

import pandas as pd
import numpy as np
from scipy.stats import ttest_ind
from statsmodels.stats.multitest import multipletests
# Load expression data (genes × samples)
# Rows = genes, Columns = samples
expr_data = pd.read_csv("expression_data.csv", index_col=0)
# Define groups
group1_samples = ["Sample1", "Sample2", "Sample3"]
group2_samples = ["Sample4", "Sample5", "Sample6"]
results = []
for gene in expr_data.index:
    group1_expr = expr_data.loc[gene, group1_samples]
    group2_expr = expr_data.loc[gene, group2_samples]
    # T-test
    t_stat, p_value = ttest_ind(group1_expr, group2_expr)
    # Fold change
    mean1 = group1_expr.mean()
    mean2 = group2_expr.mean()
    log2fc = np.log2(mean1 / mean2) if mean2 > 0 else np.nan
    results.append({
        "gene": gene,
        "log2FC": log2fc,
        "p_value": p_value,
        "mean_group1": mean1,
        "mean_group2": mean2
    })
results_df = pd.DataFrame(results)
# Multiple testing correction
results_df["p_adj"] = multipletests(results_df["p_value"], method="fdr_bh")[1]
# Define significant genes
significant = results_df[
    (results_df["p_adj"] < 0.05) &
    (abs(results_df["log2FC"]) > 1)  # 2-fold change
]
print(f"Significant genes: {len(significant)}")
print(f"Upregulated: {sum(significant['log2FC'] > 0)}")
print(f"Downregulated: {sum(significant['log2FC'] < 0)}")

Volcano Plot

Visualize differential expression:

import matplotlib.pyplot as plt
plt.figure(figsize=(8, 6))
plt.scatter(
    results_df["log2FC"],
    -np.log10(results_df["p_adj"]),
    alpha=0.5, s=10, c="gray"
)
# Highlight significant genes
sig_mask = (results_df["p_adj"] < 0.05) & (abs(results_df["log2FC"]) > 1)
plt.scatter(
    results_df.loc[sig_mask, "log2FC"],
    -np.log10(results_df.loc[sig_mask, "p_adj"]),
    alpha=0.7, s=20, c="red", label="Significant"
)
plt.xlabel("log2 Fold Change")
plt.ylabel("-log10(adjusted p-value)")
plt.axhline(-np.log10(0.05), linestyle="--", color="black", linewidth=0.5)
plt.axvline(-1, linestyle="--", color="black", linewidth=0.5)
plt.axvline(1, linestyle="--", color="black", linewidth=0.5)
plt.title("Volcano Plot")
plt.legend()
plt.savefig("volcano_plot.png", dpi=300)

Single-Cell Differential Expression: Beware Pseudoreplication

⚠️ The most common and most consequential error in single-cell DE. A typical single-cell experiment has many cells (tens of thousands) but few biological samples (a handful of patients or animals per condition). Cells from the same donor are not independent — they share genetic background, batch, dissociation and technical effects. The unit of replication is the sample/donor, not the cell.

Running a per-cell test (t-test, Wilcoxon, naive rank_genes_groups) and treating each cell as a replicate silently inflates the effective n from ~6 samples to ~60,000 cells. This collapses the variance estimate and produces enormous false-positive rates — naive cell-level tests call hundreds of "significant" genes even when comparing two random splits of the same population, with highly expressed genes flagged most often (Squair et al. 2021; Zimmerman et al. 2021).

Fix 1 — Pseudobulk (recommended default)

When: Comparing a given cell type across conditions in a multi-sample design. This is the field-standard default and the top performer for FDR control in benchmarks (Squair et al. 2021; Murphy & Skene 2022; Junttila et al. 2022).

Sum raw counts across all cells of one cell type within each sample, then run a bulk DE tool (DESeq2/edgeR/limma-voom) on the resulting sample-level matrix. The statistics then reduce to the bulk RNA-seq workflow above, with an honest n.

import anndata as ad
import pandas as pd
# adata: cells × genes; obs has "sample_id", "cell_type", "condition"
adata = ad.read_h5ad("single_cell.h5ad")
# Aggregate raw counts -> one pseudobulk profile per sample, for one cell type
def pseudobulk(adata, cell_type):
    sub = adata[adata.obs["cell_type"] == cell_type]
    X = sub.X.toarray() if hasattr(sub.X, "toarray") else sub.X
    counts = pd.DataFrame(X, index=sub.obs["sample_id"].values, columns=sub.var_names)
    # SUM counts within each sample (not mean — DESeq2 expects counts)
    return counts.groupby(level=0).sum()  # samples × genes
pb = pseudobulk(adata, cell_type="Microglia")
# One metadata row per sample, aligned to the pseudobulk matrix
sample_meta = (
    adata.obs[["sample_id", "condition"]]
    .drop_duplicates()
    .set_index("sample_id")
    .loc[pb.index]
)
# DESeq2 on the sample-level matrix (n = number of samples, not cells)
from pydeseq2.dds import DeseqDataSet
from pydeseq2.ds import DeseqStats
dds = DeseqDataSet(counts=pb, metadata=sample_meta, design_factors="condition")
dds.deseq2()
res = DeseqStats(dds, contrast=["condition", "disease", "control"])
res.summary()
print(res.results_df.sort_values("padj").head())

muscat (R/Bioconductor; Crowell et al. 2020) implements this aggregation-based workflow across all cell types at once if you prefer a turnkey tool.

Fix 2 — Mixed-effects model (random intercept per donor)

When: You want to retain cell-level resolution, have a continuous/complex design, or aggregation would discard needed structure. A (generalized) linear mixed model with a random intercept per sample models the within-donor correlation directly (Zimmerman et al. 2021).

import statsmodels.formula.api as smf
# Long dataframe: one row per cell, columns "expression", "condition", "sample_id"
# The random intercept per sample absorbs donor-level correlation.
model = smf.mixedlm("expression ~ condition", data=cell_df, groups=cell_df["sample_id"])
result = model.fit()
print(result.summary())  # the fixed effect of `condition` is the test of interest

Tradeoffs: heavier compute, convergence/fitting fragility, and sensitivity to the assumed count distribution. In benchmarks weighing both sensitivity and specificity, pseudobulk generally matches or beats mixed models (Murphy & Skene 2022) — so prefer pseudobulk unless you have a specific reason.

A note on metacells

Metacells (MetaCell, SEACells; Baran et al. 2019; Persad et al. 2023) pool transcriptionally similar cells within a sample to denoise sparse data and resolve fine-grained states. They are a within-sample representation tool and do not create independent biological replicates. Condition-level DE run on metacells still pseudoreplicates — you must still aggregate to the sample level (pseudobulk) or use donor random effects.

Replication

Power for condition-level DE scales with the number of independent samples, not the number of cells per sample. Aim for ≥3 samples per condition (more is better); adding cells per donor does not buy replication (Zimmerman et al. 2021; Heumos et al. 2023).

Gene Set Enrichment

Simple Pathway Enrichment

When: You have a list of significant genes and want to know which pathways are affected

# Define gene sets (pathways)
gene_sets = {
    "Cell Cycle": ["TP53", "CDK1", "CCNB1", "CDC20", ...],
    "Apoptosis": ["TP53", "BAX", "BCL2", "CASP3", ...],
    "DNA Repair": ["TP53", "BRCA1", "BRCA2", "ATM", ...],
    # ... more pathways
}
# Fisher's exact test for enrichment
from scipy.stats import fisher_exact
all_genes = set(expr_data.index)
sig_genes = set(significant["gene"])
enrichment_results = []
for pathway, pathway_genes in gene_sets.items():
    pathway_genes = set(pathway_genes) & all_genes  # Only genes in dataset
    # 2x2 contingency table
    a = len(sig_genes & pathway_genes)  # Sig & in pathway
    b = len(sig_genes - pathway_genes)  # Sig & not in pathway
    c = len(pathway_genes - sig_genes)  # Not sig & in pathway
    d = len(all_genes - sig_genes - pathway_genes)  # Not sig & not in pathway
    oddsratio, p_value = fisher_exact([[a, b], [c, d]], alternative='greater')
    enrichment_results.append({
        "pathway": pathway,
        "overlap": a,
        "pathway_size": len(pathway_genes),
        "odds_ratio": oddsratio,
        "p_value": p_value
    })
enrich_df = pd.DataFrame(enrichment_results)
enrich_df["p_adj"] = multipletests(enrich_df["p_value"], method="fdr_bh")[1]
enrich_df = enrich_df.sort_values("p_adj")
print(enrich_df.head(10))

Gene Ontology (GO) Terms

Common GO categories:

• Biological Process (BP): What the gene does (e.g., "cell cycle", "apoptosis")
• Molecular Function (MF): Biochemical activity (e.g., "kinase activity")
• Cellular Component (CC): Where it acts (e.g., "nucleus", "mitochondrion")

Resources:

• Gene Ontology: http://geneontology.org/
• Enrichr: https://maayanlab.cloud/Enrichr/ (web-based enrichment)
• DAVID: https://david.ncifcrf.gov/

KEGG Pathway Enrichment

KEGG = Kyoto Encyclopedia of Genes and Genomes

Provides curated pathway maps for:

• Metabolic pathways
• Signaling pathways
• Disease pathways

Example pathways:

• hsa04110: Cell cycle
• hsa04210: Apoptosis
• hsa04151: PI3K-Akt signaling

Common Analysis Patterns

Pattern 1: Transcription Factor Activity

Observation: Many genes upregulated

Hypothesis: Shared transcription factor (TF)

Test:

# Check if significant genes share TF binding motifs
tf_targets = {
    "TP53": ["BAX", "CDKN1A", "MDM2", "GADD45A", ...],
    "MYC": ["CDK4", "CCND1", "E2F1", ...],
    # ... more TFs
}
# Test for enrichment (same as pathway enrichment)

Interpretation: Enrichment suggests TF is active/inactive in condition

Pattern 2: Pathway Coordination

Observation: Genes in same pathway all up/down together

Interpretation: Pathway-level regulation (not individual genes)

Example:

All glycolysis genes ↑↑ → Increased glycolysis
All oxidative phosphorylation genes ↓↓ → Metabolic shift

Pattern 3: Compensatory Response

Observation: Opposite regulation of related pathways

Example:

De novo biosynthesis genes ↓
Salvage pathway genes ↑
→ Metabolic switch to energy-efficient salvage

Correlation Analysis

Co-expression Networks

When: Identify genes that change together

from scipy.stats import pearsonr
# Compute pairwise correlations
genes = significant["gene"].tolist()[:50]  # Top 50 for tractability
corr_matrix = expr_data.loc[genes].T.corr()
# Filter high correlations
high_corr = []
for i in range(len(genes)):
    for j in range(i+1, len(genes)):
        if abs(corr_matrix.iloc[i, j]) > 0.8:
            high_corr.append({
                "gene1": genes[i],
                "gene2": genes[j],
                "correlation": corr_matrix.iloc[i, j]
            })
print(f"High correlations (|r| > 0.8): {len(high_corr)}")

Interpretation:

• Positive correlation → co-regulated (same pathway, shared TF)
• Negative correlation → antagonistic regulation

Network Visualization

import networkx as nx
# Build network
G = nx.Graph()
for item in high_corr:
    G.add_edge(item["gene1"], item["gene2"], weight=abs(item["correlation"]))
# Find communities (clusters of co-expressed genes)
from networkx.algorithms import community
communities = community.greedy_modularity_communities(G)
for i, comm in enumerate(communities):
    print(f"Community {i}: {list(comm)}")

Literature Search Strategies

Effective Queries

For gene function:

"[GENE] function"
"[GENE] role in [PROCESS]"
"[GENE] knockout phenotype"

For pathway context:

"[GENE] pathway"
"[GENE] interacting proteins"
"[GENE] regulation"

For disease relevance:

"[GENE] [DISEASE]"
"[GENE] mutation [DISEASE]"

Key Databases

1. NCBI Gene: Gene summaries and references
2. UniProt: Protein function and domains
3. STRING: Protein-protein interactions
4. GeneCards: Comprehensive gene info
5. PubMed: Literature search

Genomics-Specific Hypotheses

Template Hypotheses

H1: Transcriptional Regulation

"Condition X activates transcription factor [TF], upregulating
target genes [G1, G2, G3] in pathway [P]"

H2: Pathway Activation

"Condition X activates [pathway], evidenced by coordinated
upregulation of pathway genes and increased activity signature"

H3: Epigenetic Regulation

"Condition X alters chromatin state at [locus], changing
expression of genes [G1, G2]"

H4: Post-transcriptional Regulation

"MicroRNA [miR] is upregulated, suppressing target genes [G1, G2],
explaining decreased protein levels despite unchanged mRNA"

Quality Control

Before interpreting results:

• [ ] Check for batch effects (PCA colored by batch)
• [ ] Verify sample labels are correct
• [ ] Check for outlier samples (hierarchical clustering)
• [ ] Confirm expression distribution (should be roughly normal after log transform)
• [ ] Verify normalization (samples should have similar distributions)

Common Pitfalls

❌ Ignoring log transformation

• Expression data should be log-transformed for most analyses
• Fold changes are linear differences in log space

❌ Using nominal p-values for many genes

• Always correct for multiple testing (FDR)
• Use adjusted p-values for significance

❌ Overinterpreting small fold changes

• log2FC < 0.5 (1.4-fold) may not be biologically meaningful
• Use stricter thresholds for noisy data

❌ Confusing gene expression with protein activity

• mRNA ≠ protein levels
• Protein activity may require post-translational modifications

❌ Cherry-picking genes

• Don't select genes to fit a story
• Use unbiased pathway enrichment

❌ Treating single cells as biological replicates (pseudoreplication)

• Cells from one donor are not independent; the replicate unit is the sample/donor
• A naive per-cell t-test/Wilcoxon inflates n by orders of magnitude → false positives
• Use pseudobulk (default) or a donor random effect — see "Single-Cell Differential

Expression: Beware Pseudoreplication" above

Integration with Other Data Types

Transcriptomics + Metabolomics

Strategy:

1. Identify differentially expressed metabolic enzymes
2. Map to KEGG pathways
3. Check if corresponding metabolites are changed
4. Build integrated metabolic model

Example:

Gene: PHGDH (phosphoglycerate dehydrogenase) ↑
Metabolite: Serine ↑
→ Integrated finding: Increased serine biosynthesis

Transcriptomics + Proteomics

Compare mRNA vs protein changes:

• Concordant (both up/down) → transcriptional regulation
• Discordant (mRNA ≠ protein) → post-transcriptional regulation

Key References

Single-cell pseudoreplication and differential expression:

1. Squair et al. (2021) Confronting false discoveries in single-cell differential expression. Nat Commun. https://doi.org/10.1038/s41467-021-25960-2
2. Zimmerman et al. (2021) A practical solution to pseudoreplication bias in single-cell studies. Nat Commun. https://doi.org/10.1038/s41467-021-21038-1
3. Crowell et al. (2020) muscat detects subpopulation-specific state transitions from multi-sample multi-condition single-cell transcriptomics data. Nat Commun. https://doi.org/10.1038/s41467-020-19894-4
4. Murphy & Skene (2022) A balanced measure shows superior performance of pseudobulk methods in single-cell RNA-sequencing analysis. Nat Commun. https://doi.org/10.1038/s41467-022-35519-4
5. Junttila et al. (2022) Benchmarking methods for detecting differential states between conditions from multi-subject single-cell RNA-seq data. Brief Bioinform. https://doi.org/10.1093/bib/bbac286
6. Baran et al. (2019) MetaCell: analysis of single-cell RNA-seq data using K-nn graph partitions. Genome Biol. https://doi.org/10.1186/s13059-019-1812-2
7. Persad et al. (2023) SEACells infers transcriptional and epigenomic cellular states from single-cell genomics data. Nat Biotechnol. https://doi.org/10.1038/s41587-023-01716-9
8. Hurlbert (1984) Pseudoreplication and the design of ecological field experiments. Ecol Monogr. https://doi.org/10.2307/1942661
9. Heumos et al. (2023) Best practices for single-cell analysis across modalities. Nat Rev Genet. https://doi.org/10.1038/s41576-023-00586-w — practical DE walkthrough: https://www.sc-best-practices.org/conditions/differential_gene_expression.html

Key Principle

Gene expression is the messenger, not the message.

mRNA changes indicate potential for protein changes. Always consider:

• Post-transcriptional regulation (miRNA, RNA stability)
• Translational control
• Protein stability and degradation
• Post-translational modifications

Connect expression changes to phenotype through pathways and functional validation.