Analytical Workflow Pipeline
We operate on absolute transparency. Below is our standard scRNA-seq computational deployment blueprint, charting every quality boundary and statistical control.
Pipeline Checkpoints
Raw Data Ingestion & Alignment
Your dataset is ingested via encrypted transfer portals. If raw FASTQ sequencing files are provided, they undergo quality profiling using FastQC. Then, alignment is conducted using STAR or Salmon pipelines to map reads against biological index references.
QC, Doublet Detection & Filtration
Single-cell sequencing often captures empty droplets or cells containing double structures. We use DoubletFinder to calculate and delete double cells. Next, we filter cells using strict threshold cutoffs mapping low library sizes and high mitochondrial percentages.
Log Normalization & Scaling
To balance library sizes and prevent highly expressed genes from dominating downstream clustering, we execute library size log-normalization. We leverage standard log-transformation or SCTransform (Seurat v5.0) which models sequencing depth parameters mathematically.
PCA ➔ UMAP Modality reduction
Using highly variable genes, we conduct Principal Component Analysis (PCA) to shrink the dimensional footprint. Then, coordinates are registered using UMAP (Uniform Manifold Approximation and Projection) to plot cells onto intuitive scatter dimensions.
Clustering & Cell Type Annotation
Cells are grouped using nearest-neighbor graphs and modularity clustering (Leiden / Louvain). Cell types are annotated by reference database mapping (Azimuth/SingleR) or manually validated by tracking canonical cell biomarkers.
Differential Expression Analysis
We execute rigorous statistical testing (using Wilcoxon, DESeq2-pseudo-bulk, or MAST methods) to locate differentially expressed genes across clusters or patient treatment cohorts. Results pinpoint specific cellular regulators.
Trajectory & RNA Velocity
To analyze cell maturation profiles, we construct pseudotime coordinates using Monocle3 or Slingshot. RNA velocity is plotted using scVelo to model transcription dynamics, tracing arrows that detail future cell states.
Deliverables Assembly
At checkpoint conclusion, all files are bundled securely. You receive fully commented R/Python scripts, vectorized high-DPI figures, CSV gene matrices, annotated `.h5ad`/`.rds` object variables, and a detailed PDF methodology report.