Single-Cell Sequencing Services

scRNA-seq Analysis Pipelines

End-to-end dry-lab processing from raw FASTQ datasets or cell count matrices to publication-ready figures. Built on standard Seurat & Scanpy protocols with rigorous batch controls.

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Rigorous Pipeline Engineering

Analyzing single-cell datasets requires highly customized pathways to remove computational noise and preserve true biological signals. We adhere to the highest peer-review standards, utilizing cell filtration thresholds customized exactly to your tissue profile.

We leverage multi-sample batch integration algorithms to preserve underlying cell-states while neutralizing sample-source variance.

Core Pipeline Checklist

QC & Cell Filtering: DoubletFinder removal, cell-level mitochondrial cutoffs, cell-cycle regression.
Normalization & Scaling: LogNormalise or SCTransform (Seurat v5) optimized scaling.
Multi-Sample Batch Integration: Harmony, BBKNN, or scVI sample alignment.
Clustering & Annotation: Leiden/Louvain modularity clustering. Manual marker validation or reference mapping (Azimuth).
Pseudotime & Trajectories: Temporal ordering of cell states (Monocle3, Slingshot).
Cell Communication: Network receptor-ligand modeling using CellChat.

Computational Path

The scRNA-seq Pipeline Steps

Scroll to visualize the sequential checkpoints inside our active computational clusters.

01

Raw Data & Ingestion

FASTQ / MTX

We ingest raw FASTQ reads or count matrix data. FASTQ lines are aligned utilizing optimized STAR or Salmon mapping blocks against target reference genomes.

02

Quality Control (QC) & Filtering

DoubletFinder / Scran

Filter empty droplets, remove cell doublets, and execute precise cell-level mitochondrial gene thresholds to eliminate dead or compromised cells.

03

Modality Integration & Alignment

Harmony / scVI

Align cell coordinates across patient sets or sequencing batches. Harmony integration isolates biological signals from technical batch variances.

04

Clustering & Annotation

Leiden Modularity

Isolate distinct cell groups using nearest-neighbor graphs. Annotate clusters using database maps or validated gene markers (CD3E, MS4A1, CD14, etc.).

05

Dynamic Lineage Trajectories

Monocle3 / Pseudotime

Trace temporal changes inside development cascades. Order cell groups along pseudotime graphs to analyze cell-state shifts during differentiations.

Pricing System

Analytical Pricing Matrix

Transparent service levels designed for academic projects, research laboratories, and CRO partnerships.

Starter

$600 / sample

Basic QC & Annotation

Perfect for preliminary data vetting or validating a pilot sequencing sample.

✓ Cell-level Quality Filtering
✓ Leiden/Louvain Clustering
✓ Basic Cell Type Annotation
✓ Web-ready UMAP Vector Plots
✓ RDS / H5AD File Export

Select Starter

Standard

$1,800 / cohort (2-4 samples)

Full Cohort Integration

Our most popular package. Complete integration, batch correction, and differential expression pathways.

✓ Everything in Starter
✓ Harmony Batch Integration
✓ Differential Gene Expression (DE)
✓ GSEA & Pathway Enrichments
✓ Monocle3 Trajectory Plots
✓ Fully Commented Jupyter Notebooks

Select Standard

Comprehensive

Custom / CRO partnership

Custom Discovery Pipelines

Tailored engagements for large cohorts, multi-modal spatial transcriptomics, and custom AI annotation nets.

✓ Everything in Standard
✓ Multi-Modal Integration (ADT)
✓ CellChat Communication Networks
✓ Custom Genome Reference Indexes
✓ Dedicated Bioinformatics Liaison
✓ NDA-Backed CRO Service Level

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scRNA FAQ

Analytical Specifics

How many cells do I need to capture for a good single-cell RNA-seq profile?

For standard tissue profiling, a capture rate of 5,000 to 10,000 cells per sample is recommended. This provides sufficient statistical power to identify rare cell populations (representing <1% of the total cells) and generate reliable clustering profiles. If you are conducting pilot sequencing, 2,000 to 3,000 cells can still resolve major cell populations.

What does the final analysis report deliverable look like?

We deliver a comprehensive HTML/PDF report detailing the entire computational pipeline, QC metrics, violin plots of mitochondrial expression, clustering UMAPs, differentially expressed gene tables (Excel/CSV), pathway enrichment plots, and dynamic trajectory lines. In addition, you receive fully commented R Markdown or Jupyter notebooks and the raw `.h5ad` or `.rds` files containing cell coordinates and annotations.