Single-Cell RNA-seq Data Analysis - Turn Complex Data into Coherent Biological Stories - with Tools That Match Your Science

 

Your clusters look beautiful. But do they mean anything?

We help researchers turn complex single-cell datasets into publication-ready insight - without overclustering, overcorrecting, or overpromising.

⚠️ Common Pitfalls in scRNA-seq Analysis:

  • ❌ Clusters shift with every parameter tweak, and you don’t know which one is “correct”
  • ❌ Batch correction (e.g., Harmony, MNN, LIGER) removes more than it preserves - and reviewers push back
  • ❌ Ambient RNA, doublets, and QC steps are inconsistently handled across samples
  • ❌ Marker-based annotation is vague - or misleadingly inferred from UMAP layout
  • ❌ RNA velocity and pseudotime tools are applied without validating biological plausibility

🧩 Why These Problems Keep Happening

scRNA-seq data is powerful but unforgiving. Subtle changes in normalization (log vs. SCTransform), dimensionality reduction (PCA, ICA, diffusion maps), or batch integration (Harmony, scVI, BBKNN) can amplify noise or bury signal. Automated pipelines (like those from Cell Ranger, Seurat, or Scanpy) offer convenience - but without context or interpretation, they often mislead.

🧠 Our Approach: Experience Meets Scientific Judgment

We go beyond default settings. Every project begins with a conversation about your biological system - not just your FASTQ files. We tailor the workflow to your platform (10X, SMART-seq, Drop-seq), experimental design, and downstream needs.

- We evaluate multiple QC, clustering, and batch correction options - and explain the tradeoffs
- We combine reference-based tools (SingleR, Azimuth, CellTypist) with unsupervised annotation strategies
- We assess and visualize uncertainty (e.g., clustering robustness, ambiguous assignments)
- We can perform advanced modules: RNA velocity (scVelo), pseudotime inference (Monocle, Slingshot), and pathway analysis (GSEA, EnrichR)
- We’ve supported dozens of scRNA-seq projects across tissue types, species, and experimental designs

📈 From Biology to Insight - Not Just Output

Whether you're mapping brain development, profiling tumor heterogeneity, or dissecting immune responses, we tailor analysis to your real-world goals: clear cell-type labels, interpretable differential expression, and robust pathway enrichment.

📬 Ready to make sense of your single-cell data?
Contact us today for a free initial consultation or cost estimate tailored to your scRNA-seq project.

📝 Want to go deeper into single-cell data analysis?

We’ve written two articles that may help, depending on your needs:

🧬 Why Clients Trust Us

This careful, biology-first mindset - backed by the right tools and rigorous interpretation - is why clients return to us again and again.

Explore the 10 Reasons Why Clients Choose Us →

Reason #1: 150 years of combined experience in bioinformatics
Reason #2: Deep understanding of both the biology and the computer
Reason #3: Top-level expertise possessed by our bioinformaticians
Reason #4: Wide range of bioinformatics and data modeling solutions that we cover
Reason #5: Researchers from 80 organizations can't all be wrong!
Reason #6: We are cost-effective
Reason #7: We guarantee to do it "right"!
Reason #8: We will assist you in paper reviews
Reason #9: We will assist you in grant writing
Reason #10: See how researchers have evaluated us

Find out more about our company, or check out our FAQ page!

Send us an inquiry, chat with us online (during our business hours 9–5 Mon–Fri U.S. Central Time), or reach us in other ways!