Multi Omics Pipeline

Multi-Omics Pipeline — Pipeline Skill

Overview

Provide deterministic, copy-paste-ready code for multi-omics data processing tasks: ID mapping, batch correction, integration via MOFA2/mofapy2, enrichment analysis, and consensus clustering.

Usage

• Map gene identifiers across HGNC, Ensembl, and UniProt systems
• Run batch correction (ComBat/ComBat-seq) and multi-omic factor analysis (MOFA2/mofapy2)
• Perform GSEA enrichment and consensus clustering on integrated data

Core Concepts

Response Format

• Lead with the command or code the user needs — explain after
• Structure as: confirm inputs → working code → key parameters explained → gotchas
• One complete working example per task; do not show every alternative
• Keep code comments minimal and functional (what, not why-it-exists)
• Target: 50-100 lines of code with brief surrounding explanation

1. ID Mapping

1a. Python — HGNC Symbol ↔ Ensembl ↔ UniProt

import pandas as pd
import mygene

mg = mygene.MyGeneInfo()

# Input: list of HGNC symbols
genes = ["TP53", "BRCA1", "EGFR", "KRAS"]

# Query multiple ID types at once
result = mg.querymany(
    genes,
    scopes="symbol",
    fields="ensembl.gene,uniprot.Swiss-Prot",
    species="human",
    returnall=True,
)

df = pd.DataFrame(result["out"])
# Handle many-to-many: explode nested Ensembl IDs
df_ensembl = df.explode("ensembl")
df_ensembl["ensembl_id"] = df_ensembl["ensembl"].apply(
    lambda x: x["gene"] if isinstance(x, dict) else None
)
print(df_ensembl[["query", "ensembl_id"]].dropna())

1b. R — biomaRt

library(biomaRt)

ensembl <- useEnsembl(biomart = "genes", dataset = "hsapiens_gene_ensembl")

# Map HGNC symbols to Ensembl gene IDs and UniProt accessions
mapping <- getBM(
  attributes = c("hgnc_symbol", "ensembl_gene_id", "uniprotswissprot"),
  filters = "hgnc_symbol",
  values = c("TP53", "BRCA1", "EGFR", "KRAS"),
  mart = ensembl
)

# Remove rows with empty UniProt
mapping <- mapping[mapping$uniprotswissprot != "", ]
print(mapping)

ID Mapping Parameter Table

2. Batch Correction

2a. Python — ComBat (pycombat)

from combat.pycombat import pycombat
import pandas as pd
import numpy as np

# expression_df: genes (rows) x samples (columns), log-transformed
# batch: list of batch labels, same order as columns
# covariates: DataFrame with biological covariates to protect

expression_df = pd.read_csv("expression_matrix.csv", index_col=0)
batch = [1, 1, 1, 2, 2, 2, 3, 3, 3]  # batch labels per sample

# Protect biological covariate (e.g., disease status)
covar_df = pd.DataFrame({"disease": [0, 0, 1, 0, 1, 1, 0, 1, 1]})

corrected = pycombat(
    expression_df,
    batch,
    mod=covar_df,       # biological covariates to protect
    par_prior=True,     # parametric adjustment (default)
)

2b. R — ComBat / ComBat-seq

library(sva)

# For continuous data (log-transformed expression, protein intensity)
# expr_mat: genes x samples matrix
# batch: vector of batch labels
# mod: model matrix with biological covariates to protect

mod <- model.matrix(~ disease_status, data = sample_info)

corrected <- ComBat(
  dat = expr_mat,
  batch = sample_info$batch,
  mod = mod,
  par.prior = TRUE  # parametric
)

# For count data (RNA-seq raw counts) — use ComBat-seq
corrected_counts <- ComBat_seq(
  counts = count_mat,
  batch = sample_info$batch,
  group = sample_info$disease_status  # biological variable to protect
)

Batch Correction Parameter Table

3. MOFA2 / mofapy2 Integration

3a. R — MOFA2

library(MOFA2)

# Prepare data: list of matrices, one per omic layer
# Each matrix: features x samples
data_list <- list(
  transcriptomics = expr_mat,   # genes x samples
  proteomics = prot_mat,        # proteins x samples
  methylation = meth_mat        # CpG sites x samples
)

# Create MOFA object
mofa <- create_mofa(data_list)

# Set data options
data_opts <- get_default_data_options(mofa)

# Set model options
model_opts <- get_default_model_options(mofa)
model_opts$num_factors <- 20  # start with 20; inactive factors pruned automatically

# Set training options
train_opts <- get_default_training_options(mofa)
train_opts$convergence_mode <- "slow"  # more iterations for better convergence
train_opts$seed <- 42

# Prepare and run
mofa <- prepare_mofa(
  mofa,
  data_options = data_opts,
  model_options = model_opts,
  training_options = train_opts
)

mofa <- run_mofa(mofa, outfile = "mofa_model.hdf5")

# Inspect variance explained per factor per view
plot_variance_explained(mofa)

# Extract top features for factor 1
weights <- get_weights(mofa, views = "transcriptomics", factors = 1, as.data.frame = TRUE)
top_genes <- weights[order(abs(weights$value), decreasing = TRUE), ][1:50, ]

3b. Python — mofapy2

from mofapy2.run.entry_point import entry_point
import numpy as np

# Prepare data: list of numpy arrays [views][groups]
# Each array: samples x features
data = [
    [expr_array],   # view 0: transcriptomics, group 0
    [prot_array],   # view 1: proteomics, group 0
    [meth_array],   # view 2: methylation, group 0
]

ent = entry_point()
ent.set_data_options(scale_groups=False, scale_views=True)
ent.set_data_matrix(data, views_names=["transcriptomics", "proteomics", "methylation"])

ent.set_model_options(factors=20, spikeslab_weights=True, ard_factors=True)
ent.set_train_options(iter=1000, convergence_mode="slow", seed=42)

ent.build()
ent.run()
ent.save("mofa_model.hdf5")

MOFA Parameter Table

4. GSEA Enrichment

4a. Python — gseapy

import gseapy as gp
import pandas as pd

# ranked_genes: DataFrame with columns ["gene", "rank_metric"]
# rank_metric can be: log2FC, -log10(p) * sign(FC), or MOFA weight
ranked_genes = pd.read_csv("ranked_genes.csv")

# Pre-ranked GSEA against MSigDB Hallmark gene sets
result = gp.prerank(
    rnk=ranked_genes,
    gene_sets="MSigDB_Hallmark_2020",
    min_size=15,
    max_size=500,
    permutation_num=1000,
    seed=42,
    outdir="gsea_results",
)

# Filter significant results
sig = result.res2d[result.res2d["FDR q-val"] < 0.05]
print(sig[["Term", "NES", "FDR q-val"]].sort_values("NES", ascending=False))

4b. R — fgsea with clusterProfiler

library(fgsea)
library(msigdbr)

# Get Hallmark gene sets for human
hallmark <- msigdbr(species = "Homo sapiens", category = "H")
pathways <- split(hallmark$gene_symbol, hallmark$gs_name)

# ranked_genes: named numeric vector (names = gene symbols, values = rank metric)
ranked_genes <- setNames(de_results$log2FoldChange, de_results$gene_symbol)
ranked_genes <- sort(ranked_genes, decreasing = TRUE)

# Run fgsea
fgsea_res <- fgsea(
  pathways = pathways,
  stats = ranked_genes,
  minSize = 15,
  maxSize = 500,
  nPermSimple = 10000
)

# Filter and sort
sig_pathways <- fgsea_res[fgsea_res$padj < 0.05, ]
sig_pathways <- sig_pathways[order(sig_pathways$NES, decreasing = TRUE), ]
print(sig_pathways[, c("pathway", "NES", "padj")])

Enrichment Parameter Table

5. Consensus Clustering

Python — scikit-learn based consensus

import numpy as np
from sklearn.cluster import KMeans
from sklearn.metrics import silhouette_score

def consensus_clustering(data, k_range=range(2, 9), n_iterations=100, seed=42):
    """Run consensus clustering over a range of k values.
    data: samples x features array. Returns dict mapping k to results.
    """
    rng = np.random.default_rng(seed)
    n_samples = data.shape[0]
    results = {}

    for k in k_range:
        co_occurrence = np.zeros((n_samples, n_samples))
        co_sampled = np.zeros((n_samples, n_samples))

        for _ in range(n_iterations):
            # Subsample 80% of samples
            idx = rng.choice(n_samples, size=int(0.8 * n_samples), replace=False)
            km = KMeans(n_clusters=k, n_init=10, random_state=rng.integers(1e6))
            labels = km.fit_predict(data[idx])

            for i in range(len(idx)):
                for j in range(i + 1, len(idx)):
                    co_sampled[idx[i], idx[j]] += 1
                    co_sampled[idx[j], idx[i]] += 1
                    if labels[i] == labels[j]:
                        co_occurrence[idx[i], idx[j]] += 1
                        co_occurrence[idx[j], idx[i]] += 1

        # Consensus matrix
        with np.errstate(divide="ignore", invalid="ignore"):
            consensus = np.where(co_sampled > 0, co_occurrence / co_sampled, 0)

        # Final clustering on consensus matrix
        km_final = KMeans(n_clusters=k, n_init=10, random_state=seed)
        final_labels = km_final.fit_predict(1 - consensus)  # distance = 1 - consensus
        sil = silhouette_score(1 - consensus, final_labels, metric="precomputed")

        results[k] = {"consensus_matrix": consensus, "silhouette": sil, "labels": final_labels}
        print(f"k={k}: silhouette={sil:.3f}")

    return results

# Usage with MOFA factors
# factors: samples x factors array from MOFA
results = consensus_clustering(factors, k_range=range(2, 9))

# Select optimal k by maximum silhouette score
optimal_k = max(results, key=lambda k: results[k]["silhouette"])
print(f"Optimal k: {optimal_k}")

R — ConsensusClusterPlus

library(ConsensusClusterPlus)
# input_mat: features x samples
results <- ConsensusClusterPlus(
  d = input_mat,
  maxK = 8,
  reps = 1000,
  pItem = 0.8,        # proportion of samples to subsample
  pFeature = 1,       # use all features
  clusterAlg = "km",  # k-means
  distance = "euclidean",
  seed = 42,
  plot = "pdf",
  title = "consensus_clustering"
)

# Inspect consensus CDF and delta area plots in output PDF
# Select k where CDF curve is flattest (highest consensus)

Consensus Clustering Parameter Table

Common Mistakes

• Wrong: Mapping gene IDs using symbol-based joins without handling duplicates or deprecated symbols Right: Use HGNC ID or Ensembl stable ID as the primary key; resolve ambiguous symbols via the HGNC multi-symbol checker Why: Gene symbols are not unique (e.g., "MARCH1" was renamed to "MARCHF1") — symbol joins silently drop or duplicate rows

• Wrong: Running ComBat without including biological covariates in the model matrix Right: Always pass mod = model.matrix(~ disease_status + sex, data=meta) to protect biological signal Why: Without protected covariates, ComBat removes biological variation that correlates with batch

• Wrong: Using mofapy2 with raw counts instead of normalized, variance-stabilized data Right: Apply log2(CPM+1) or VST to RNA-seq counts before passing to MOFA; use log2 intensity for proteomics Why: MOFA assumes Gaussian-distributed continuous data — raw counts violate this and produce meaningless factors

• Wrong: Running gseapy/fgsea with a gene list ranked by fold change alone (no significance weighting) Right: Rank by -log10(p) × sign(log2FC) or use the Wald statistic from DESeq2/limma Why: Fold-change-only ranking treats noisy large-FC genes the same as significant ones, inflating false enrichments

• Wrong: Using default min_size=1 in fgsea, allowing tiny gene sets to appear significant Right: Set minSize=15, maxSize=500 to filter gene sets to biologically interpretable sizes Why: Very small gene sets produce unstable enrichment scores; very large sets are too generic to be informative