Omics Data Analysis Guide: Three-Tiered Approach
Metadata
Short Description: Comprehensive guide for analyzing omics data (transcriptomics, proteomics) using validated pipelines, standard workflows, or custom analysis methods.
Authors: HITS
Version: 1.0
Last Updated: December 2025
License: CC BY 4.0
Commercial Use: Allowed
Citations and Acknowledgments
If you use validated pipelines or tools (Option 1):
- Citation: Always cite the original publication associated with each tool or pipeline
- Acknowledgment: Cite the specific tools and methods used in your analysis
If you use standard workflows (Option 2):
- Acknowledgment Statement: "Analysis performed using standard omics data analysis workflows and best practices"
- Citation for RNA-seq analysis: Dobin A, et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics. 2013;29(1):15-21. PMID: 23104886
- Citation for proteomics: Cox J, Mann M. MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification. Nat Biotechnol. 2008;26(12):1367-72. PMID: 19029910
Overview
This guide provides a three-tiered approach to omics data analysis, prioritizing validated pipelines and standard workflows before moving to custom analysis. Always start with Option 1 and proceed to subsequent options only if needed.
The guide covers:
- Transcriptomics: Bulk RNA-seq
- Proteomics: Pre-quantified protein abundance data (similar to bulk RNA-seq analysis)
Note: This guide focuses on analysis of already-quantified data. For raw data processing (alignment, quantification), refer to specialized tools and pipelines.
Key Concepts
Validated Pipeline vs. Standard Workflow vs. Custom Analysis
A validated pipeline is a specific tool with peer-reviewed benchmarking data demonstrating performance on data like yours (e.g., DESeq2 for RNA-seq counts, MaxQuant for label-free proteomics). A standard workflow is the canonical sequence of QC → normalization → statistical test → multiple-testing correction assembled from accepted community practice but tuned to your specific dataset. Custom analysis is bespoke statistical or computational modeling required when neither prior tier covers the data type or research question. The progression Option 1 → Option 2 → Option 3 trades reproducibility for flexibility — always exhaust earlier tiers first.
Missing Value Mechanisms (MCAR / MAR / MNAR)
Missing data in omics arises from three distinct mechanisms with different correct treatments. MCAR (Missing Completely At Random) means missingness is independent of any value — safe to impute with mean, median, or KNN. MAR (Missing At Random) means missingness depends on observed variables but not the unobserved value — KNN or model-based imputation is appropriate. MNAR (Missing Not At Random) means missingness depends on the missing value itself, typical in proteomics where low-abundance proteins drop below detection — requires left-censored imputation (minprob/QRILC) below the detection limit. Choosing the wrong mechanism systematically biases downstream statistics.
Test Assumptions and Test Selection
Parametric tests (Student's t-test, Welch's t-test) assume approximate normality and (for Student's) equal variances; they have higher power than non-parametric tests when assumptions hold. Non-parametric tests (Mann-Whitney U, permutation) make weaker assumptions and are correct under skewed distributions or small n, at the cost of statistical power. The choice depends on sample size (n < 10 favors non-parametric), normality (Shapiro-Wilk / Anderson-Darling at the feature level), variance homogeneity (Levene's test), and outlier prevalence.
Multiple Testing Correction
Omics analyses test thousands of features simultaneously. Without correction, expected false positives at α=0.05 across 20,000 genes is 1,000. Family-wise error rate (FWER) corrections like Bonferroni control the probability of any false positive but are conservative. False discovery rate (FDR) corrections like Benjamini-Hochberg control the expected proportion of false positives among reported significant features and are the standard for omics. Always report adjusted p-values, never raw p-values, when calling significance.
Decision Framework
Use this tree to choose the right analysis tier for your data:
Have you searched for a validated
pipeline matching your data type?
│
┌─────────────┴─────────────┐
│ │
NO YES
│ │
▼ ▼
Run Method 1 Did you find a validated
(literature) AND pipeline with benchmarks
Method 2 (consortia matching your data type
workflows) FIRST and biological question?
│
┌───────┴───────┐
│ │
YES NO
│ │
▼ ▼
OPTION 1: Is your data a
Use validated common type
pipeline (RNA-seq counts,
(e.g., DESeq2, pre-quantified
edgeR, MaxQuant) proteomics)?
│
┌───────┴───────┐
│ │
YES NO
│ │
▼ ▼
OPTION 2: OPTION 3:
Standard Custom analysis
workflow (consult
(QC → norm → statistician;
test → FDR) document
thoroughly)Decision Table
| Data type | Sample size | Has validated pipeline? | Recommended tier | Specific tool / approach | |-----------|-------------|--------------------------|------------------|--------------------------| | Bulk RNA-seq counts | n ≥ 3/group | Yes (DESeq2, edgeR) | Option 1 | DESeq2 (negative binomial, default FDR < 0.05) | | Pre-quantified proteomics, normal-distributed | n ≥ 5/group | Sometimes | Option 1 if pipeline matches; else Option 2 | limma or t-test + BH-FDR | | Pre-quantified proteomics, MNAR-heavy | n ≥ 5/group | No (mechanism-specific) | Option 2 | minprob imputation → t-test or Mann-Whitney → BH-FDR | | Small-cohort omics (n < 5) | n < 5 | Rarely | Option 2 with caution | Permutation test, report effect sizes; flag results as preliminary | | Multi-omics integration | Variable | Limited | Option 3 | MOFA, DIABLO, or custom Bayesian model | | Novel data type (e.g., spatial multi-omics) | Variable | No | Option 3 | Build from first principles; cross-validate | | Time-series omics | n per timepoint | Sometimes (maSigPro, ImpulseDE2) | Option 1 if available; else Option 3 | maSigPro for transcriptomics; custom for proteomics |
Option 1: Search for Validated Analysis Methods (Recommended First)
1.1 Search for Validated Analysis Pipelines
IMPORTANT: You MUST complete BOTH Method 1 AND Method 2 before proceeding to Option 2. Do not skip Method 2 even if Method 1 finds no results.
Method 1: Literature Search for Best Practices
Search for validated analysis methods using web search tools or literature databases (PubMed, Google Scholar).