Single-Cell Transcriptomics

Single-Cell RNA-seq: Cell Type Identification,
Clustering, and Trajectory Analysis

BioMate runs the full scRNA-seq pipeline — Cell Ranger preprocessing, dimensionality reduction, clustering, automated cell type annotation, and trajectory inference — using Seurat or Scanpy on AWS Batch. No coding required. Results include annotated UMAP plots, marker gene tables, and a downloadable methods report.

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Pipeline overview

From raw reads to annotated cell atlas

BioMate routes scRNA-seq requests through a fully containerized pipeline covering preprocessing, quality filtering, dimensionality reduction, clustering, annotation, and trajectory analysis — all running on AWS Batch with reproducible container environments.

Pipeline stage Tools Output
Preprocessing Cell Ranger, STARsolo Filtered feature-barcode matrix, alignment QC
Quality control Seurat, Scanpy, DoubletFinder Filtered cells by nFeature, nCount, %MT; doublet removal
Normalization & feature selection Seurat SCTransform, Scanpy normalize_total Normalized count matrix, highly variable genes
Dimensionality reduction PCA, UMAP, t-SNE Embedding plots, elbow plots, neighbor graphs
Clustering Leiden, Louvain, Seurat graph-based Cluster assignments, resolution sweep results
Cell type annotation SingleR, CellTypist, marker-gene scoring Annotated UMAP, per-cluster marker tables
Trajectory analysis Monocle3, scVelo Pseudotime plot, RNA velocity arrows, lineage tree
Use cases

Common scRNA-seq research applications

Describe your biological question in plain English and BioMate configures the correct pipeline for your system.

Tumor Microenvironment

Deconvolve immune cell infiltrates, cancer cell states, and stromal populations from tumor biopsies. Identify exhaustion signatures, cytotoxic T cell subsets, and myeloid polarization states.

Developmental Biology

Map lineage commitment and cell fate decisions during organ development. Monocle3 pseudotime and RNA velocity reveal branching trajectories from progenitors to differentiated cell types.

Immunology

Profile peripheral blood mononuclear cells (PBMCs), lymph node biopsies, or bone marrow aspirates. Identify rare immune populations, activation states, and clonal expansion dynamics.

Neuroscience

Classify neuronal and glial subtypes from brain or spinal cord tissue. Map regional heterogeneity, identify disease-associated microglia states, and characterize spatial gene expression patterns.

How it works

From FASTQ to cell atlas in plain English

  1. Describe your experiment Tell BioMate your tissue, species, cell capture platform (10x Chromium, Drop-seq, etc.), and research question. No pipeline configuration needed.
  2. BioMate configures the pipeline The AI selects Cell Ranger or STARsolo for preprocessing, Seurat or Scanpy for analysis, and appropriate annotation databases for your tissue type.
  3. Real-time progress on AWS Batch Each step — alignment, QC, clustering, annotation — streams live updates to your dashboard. GPU-accelerated UMAP for large datasets.
  4. Annotated results and methods report Receive UMAP plots with cell type labels, marker gene heatmaps, cluster composition tables, and a downloadable methods section for your publication.
Example QC metrics
Cells per sample GOLD  8,412
Median genes / cell GOLD  2,847
Doublet rate GOLD  1.8%
Mitochondrial % SILVER  14.2%
Seurat v5 · 14 clusters · 22 cell types annotated · Methods report ready
FAQ

Common questions about single-cell RNA-seq in BioMate

What tools does BioMate use for single-cell RNA-seq?

BioMate supports Seurat (R) and Scanpy (Python) for dimensionality reduction, clustering, and visualization, with Cell Ranger or STARsolo for preprocessing raw FASTQ files.

What cell types can BioMate annotate?

BioMate uses marker-gene databases (CellMarker, PanglaoDB) and automated annotation tools (SingleR, CellTypist) to assign cell type labels to clusters.

Does BioMate support trajectory analysis?

Yes. BioMate runs monocle3 and RNA velocity (scVelo) for pseudotime trajectory inference and cell fate prediction.

What input formats are accepted?

10x Genomics Cell Ranger output (filtered_feature_bc_matrix), FASTQ files, or count matrices in H5AD/RDS format.

Get started

Run your first single-cell RNA-seq analysis today

No command line, no R environment to configure. Upload your Cell Ranger output or FASTQ files and describe your biological question.

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All genomics services → Bulk RNA-seq analysis → Bioconductor methods in BioMate →