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Introduction to METAFLUX

Zaoqu Liu

2026-01-23

Source: vignettes/introduction.Rmd
introduction.Rmd

Overview

METAFLUX is a high-performance R package for genome-scale metabolic flux analysis (MFA) from transcriptomic data. This package implements the METAFlux algorithm (Huang et al., Nature Communications, 2023) with significant computational optimizations.

Key Features

  • Genome-scale modeling: Utilizes Human-GEM with 13,082 metabolic reactions
  • Dual-mode analysis: Supports both bulk and single-cell RNA-seq data
  • High performance: Optimized algorithms with parallel computing and C++ acceleration
  • Community modeling: Captures metabolic interactions in tumor microenvironment

Author

Zaoqu Liu (Maintainer) - Email: - ORCID: 0000-0002-0452-742X - GitHub: Zaoqu-Liu

Installation 

install.packages("METAFLUX", repos = "https://zaoqu-liu.r-universe.dev")

From GitHub 

# install.packages("remotes")
remotes::install_github("Zaoqu-Liu/METAFlux")

Quick Start

Load Package

Bulk RNA-seq Analysis 

# Load example data
data("bulk_test_example")
data("human_blood")

# Step 1: Calculate Metabolic Reaction Activity Scores (MRAS)
mras <- calculate_reaction_score(bulk_test_example)

# Step 2: Compute metabolic fluxes
flux <- compute_flux(mras = mras, medium = human_blood)

# View results
dim(flux)  # 13082 reactions × n samples

Single-Cell Analysis 

# Load data
data("sc_test_example")
data("human_blood")

# Step 1: Bootstrap aggregation
avg_expr <- calculate_avg_exp(
  myseurat = sc_test_example,
  myident = "Cell_type",
  n_bootstrap = 100,
  seed = 42
)

# Step 2: Calculate MRAS
mras <- calculate_reaction_score(avg_expr)

# Step 3: Community flux calculation
fractions <- as.numeric(table(sc_test_example$Cell_type) / ncol(sc_test_example))
flux <- compute_sc_flux(
  num_cell = length(unique(sc_test_example$Cell_type)),
  fraction = fractions,
  fluxscore = mras,
  medium = human_blood
)

Workflow Overview 

┌─────────────────────────────────────────────────────────────────┐
│                      METAFLUX Workflow                         │
├─────────────────────────────────────────────────────────────────┤
│                                                                 │
│   Gene Expression Data                                          │
│         │                                                       │
│         ▼                                                       │
│   ┌─────────────────────────────┐                              │
│   │ calculate_reaction_score()  │  → MRAS (13,082 reactions)   │
│   └─────────────────────────────┘                              │
│         │                                                       │
│         ▼                                                       │
│   ┌─────────────────────────────┐                              │
│   │      compute_flux()         │  → Bulk RNA-seq              │
│   │    compute_sc_flux()        │  → Single-cell               │
│   └─────────────────────────────┘                              │
│         │                                                       │
│         ▼                                                       │
│   Metabolic Flux Matrix                                         │
│                                                                 │
└─────────────────────────────────────────────────────────────────┘

Data Requirements

Data Type Format Requirements
Bulk RNA-seq Matrix Rows: HGNC symbols, Columns: samples
Single-cell Seurat object RNA assay with cell type annotations
Medium Data frame Columns: metabolite, reaction_name

Citation

If you use METAFLUX in your research, please cite:

Huang Y, et al. Characterizing cancer metabolism from bulk and single-cell RNA-seq data using METAFlux. Nature Communications 14, 4883 (2023). https://doi.org/10.1038/s41467-023-40457-w

Session Info