---
title: "Data Analysis"
lang: ko
url: https://www.gereallife.com/ko/services/resources-data-analysis.html
canonical: https://www.gereallife.com/ko/services/resources-data-analysis.html
entity: Shanghai Gereallife Biotech Co., Ltd
updated: 2026-11-02
---
KEY POINTS 
 
- DESeq2 (negative-binomial model)
- edgeR (exact test / GLM)
- limma-voom (mean–variance modeling) 
 
 
 

## 01Analysis Pipeline Overview

 
- 01 Data QC
- 02 Preprocessing
- 03 Differential Analysis
- 04 Functional Enrichment
- 05 Multi-Omics Integration
- 06 Visualization 
 
 
 

## 02Preprocessing

 

High-quality preprocessing is the foundation of reliable analysis results

### Data Quality Control (QC)

Raw-data quality assessment—FASTQ QC (FastQC), mapping-rate statistics and duplication rate—ensuring data reliability and downstream accuracy.

### Normalization

Removes batch effects and technical bias with mainstream methods (TMM, RLE, Quantile, ComBat), making samples comparable.

### Missing-Value Handling

Multiple imputation methods (KNN, MICE) or removal of high-missingness features, ensuring data integrity and robust analysis.

### Data Transformation

log2 transformation, Pareto scaling and z-score standardization to meet the distribution requirements of different analysis models.

 
 
 

## 03Differential-Analysis Methods

 

Professional differential-analysis strategies tailored to each omics data type

### Transcriptomics

 
- DESeq2 (negative-binomial model)
- edgeR (exact test / GLM)
- limma-voom (mean–variance modeling) 

### Proteomics

 
- limma (empirical-Bayes modeling)
- MSstats (MS-data statistics)
- DEP R package (DIA/DDA data) 

### Metabolomics

 
- t-test / ANOVA parametric tests
- PLS-DA multivariate discriminant analysis
- VIP > 1 screening of key metabolites 

### Multivariate Statistics

 
- PCA unsupervised dimensionality reduction
- OPLS-DA supervised classification
- permutation-test validation 
 
 
 

## 04Functional Enrichment Analysis

 

In-depth interpretation of the biological functions and pathway significance of differential molecules

 

| Method | Database / tools | Use case | Output |
| --- | --- | --- | --- |
| ORA (hypergeometric test) | GO、KEGG、Reactome | differential gene/protein/metabolite lists | significantly enriched pathways (p-value) |
| GSEA (gene-set enrichment analysis) | MSigDB, custom gene sets | genome-wide ranked data | NES、FDR、Leading Edge |
| Pathway-topology analysis | KEGG、MetaboAnalyst | metabolomics data | pathway impact score |
| Network Analysis | STRING、Cytoscape | protein interaction, gene co-expression | network-topology parameters, hub nodes |

 
 
 
 

## 05Multi-Omics Integration

 

Joint cross-omics analysis to uncover deeper biological mechanisms

### Correlation Integration

Pearson/Spearman cross-omics correlation analysis builds gene–protein–metabolite association networks, revealing multi-layered regulatory relationships.

### Multi-Omics Dimensionality Reduction

MOFA (Multi-Omics Factor Analysis), mixOmics DIABLO and sPLS-DA integrate multi-omics data for dimensionality reduction and classification.

### Machine-Learning Integration

Random-forest and XGBoost feature selection builds multi-omics predictive models for biomarker discovery and phenotype prediction.

 
 
 

## Q&AFAQ

 Should I run DESeq2 or limma for differential expression? For transcriptomics the options listed are DESeq2 (negative-binomial model), edgeR (exact test / GLM) and limma-voom (mean–variance modeling), chosen to match the omics data type. 
 Can proteomics and metabolomics share the same differential analysis? No. Proteomics uses limma (empirical-Bayes modeling), MSstats or the DEP R package; metabolomics uses t-test / ANOVA and PLS-DA, with VIP > 1 to screen key metabolites. 
 What metrics should I read in the enrichment results? ORA reports significantly enriched pathways by p-value, GSEA reports NES, FDR and Leading Edge, and pathway-topology analysis reports a pathway impact score. 
 My samples were processed in different batches — can normalization fix that? Normalization removes batch effects and technical bias with methods such as TMM, RLE, Quantile and ComBat, making samples comparable. 
 How do I combine transcriptome, proteome and metabolome data? Pearson/Spearman correlation builds gene–protein–metabolite association networks; MOFA, mixOmics DIABLO and sPLS-DA cover dimensionality reduction and classification; random forest and XGBoost build predictive models. 
 
 
 
 
 
 Need study design? 
 

Free study design and sample assessment, response within 1 hour.

 Contact the team → 
 
 
 
 

 
 Call us： 400-660-8826 
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