Resolve biology with
single-cell sequencing

Single-cell RNA-seq (scRNA-seq) data analysis is the process of extracting biological insights from gene expression measurements taken at the individual cell level.

It involves multiple steps including QC filtering, normalization, dimensionality reduction, unsupervised clustering, cell type annotation, and differential expression analysis.  

Unlike bulk RNA-seq, which averages signals across thousands of cell types, scRNA-seq enables researchers to identify distinct cell populations and subtypes, detect differentially expressed genes between conditions, trace stem cell differentiation trajectories, and map cell-to-cell interactions in the tumor microenvironment.

Our analysis pipeline takes your data from raw FASTQ through publication-ready figures. 

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This depends on your sample type, research goals, and project scale. Use our Solution Selector tool above to get a recommendation or contact our team for a consultation.

Best practices include rigorous QC before analysis, validating cluster structure with multiple dimensionality reduction methods, annotating cell types using validated marker genes, accounting for batch effects across samples, and confirming key findings with orthogonal methods like immunofluorescence or flow cytometry.

Our team applies these standards across every project, ensuring the highest quality with 1:1 expert support and guaranteed responses within 24 hours.   

Admera Health’s end-to-end workflow covers everything from tissue dissociation and single-cell isolation through library preparation, sequencing, and data analysis. 

A comprehensive scRNA-seq pipeline typically includes raw data quality control (QC) and filtering, normalization, dimensionality reduction (PCA, UMAP/t-SNE), clustering, and cell type annotation; as well as downstream analysis such as differential expression, pathway enrichment and publication ready figures in approximately 3 weeks.

To ensure high-quality data, we assess samples based on cell viability for best results, concentration, volume, total cell count, and RNA quality metrics.

If you are working with fresh or cryopreserved samples, Single-cell RNA-seq (scRNA-seq) is the gold standard because it captures the entire transcriptomic picture from both the nucleus and the cytoplasm. This "whole-cell" approach usually yields the highest gene detection per cell, making it ideal for liquid samples like blood or easily dissociated fresh tissue. However, because it requires high cell viability, it can be sensitive to the stresses of tissue processing.

Single-nucleus RNA-seq (snRNA-seq) is a powerful tool for archived or difficult-to-dissociate samples, such as frozen brain or heart tissue. By focusing only on the isolated nuclei, this method bypasses the need for live cells and avoids the cellular damage that often occurs during harsh tissue digestion. While you are primarily sequencing nuclear RNA, the biological insights remain highly accurate.

Pricing is generally comparable.

Yes! For immune cell samples, we offer 10x Chromium 5' Gene Expression + VDJ profiling. This captures full-length V(D)J sequences for T cell receptors (TCR α/β) and B cell receptors (BCR heavy/light chains), paired with gene expression from the same cells. This must be planned at the library prep stage. Contact us before sample submission.

Seurat is one of the most widely used R packages for scRNA-seq analysis.

A standard Seurat workflow involves loading your count matrix, performing QC filtering, normalizing data, identifying highly variable features, scaling, running PCA, clustering with graph-based methods, and visualizing results with UMAP or t-SNE.

Both are data containers for single-cell analysis but belong to different ecosystems. Seurat objects are used within the Seurat R package and are widely adopted for their integrated workflows. 

SingleCellExperiment (SCE) is a Bioconductor-based object that serves as a common data structure across many Bioconductor packages, offering greater interoperability within that ecosystem.

The choice typically comes down to your preferred toolset and downstream analysis needs. 

Common visualization approaches in scRNA-seq include UMAP and t-SNE for low-dimensional embeddings, violin plots and feature plots for gene expression, dot plots for marker gene comparison across clusters, and trajectory plots for pseudotime analysis. 

Both Seurat and Scanpy offer built-in visualization functions, and tools like Cellxgene enable interactive exploration of large datasets.