Transcriptomic Profiling Methods

28th November 2025
Posted by: Breige McBride
Categories: Bioinformatics, Gene Expression Analysis

As transcriptomic profiling methods advance they are transforming how researchers investigate cellular states, tissue architecture, and the molecular mechanisms driving disease. Today, there are more options than ever for measuring gene expression but navigating them can be challenging. To help make it easier to choose the transcriptomic profiling method best suited to your research, we have created a comparison chart to help you quickly determine which technique best fits your experimental goals.

Transcriptomic Profiling Methods
Comparison Chart

How to Choose the Right Transcriptomic Profiling Method

Selecting the most appropriate transcriptomic approach ultimately depends on your biological question, the type of sample you’re working with, and the budget you have available for your project. For example, if your goal is to capture broad gene expression trends across an entire tissue or cell population, bulk RNA-seq is usually the most efficient and cost-effective choice. It provides a comprehensive overview of transcriptional activity without the complexity of single-cell resolution.

However, when your research requires a deeper understanding of cellular diversity, such as characterising heterogeneity within a tissue or detecting rare cell populations, single-cell RNA-seq (scRNA-seq) provides the level of resolution needed. This approach makes it possible to uncover subtle differences between individual cells that bulk sequencing would otherwise mask.

Alternatively, if spatial organisation is central to your question, spatial transcriptomics would be the most informative option. It keeps the spatial layout of gene expression intact, enabling you to see where specific transcripts and cell types are located within intact tissue architecture.

In cases where both cellular resolution and spatial information are important, pairing scRNA-seq with spatial transcriptomics helps you see both cell details and their locations. This combined approach delivers high-resolution insights into cell identity while simultaneously revealing how those cells are arranged and interact within their native environment.

We Analyse Bulk, Single-Cell, and Spatial Transcriptomics Data

Choosing which type of transcriptomic data to generate is only half the challenge; the other half is making sense of it. Each method comes with its own analytical complexities, from normalisation and QC to cell clustering and spatial integration.

Our team provides end-to-end analysis support for Bulk RNA-seq data, single-cell data and spatial data.

Bulk RNA-seq Data Analysis

We deliver reliable and flexible analysis services for bulk RNA-sequencing data, helping you uncover insights into:

Differential gene expression
Pathway and functional enrichment
& more

Our FGEz pipeline combines automated processing with careful manual validation, ensuring high-quality results. We handle data from any source and use trusted open-source tools, including EdgeR and Limma, to provide a comprehensive workflow capable of analysing both simple and complex datasets.

Single-Cell Data Analysis

We deliver cost-efficient and rapid turnaround analysis services for single-cell gene expression data helping you uncover insights into:

Cellular heterogeneity
Rare populations
Developmental trajectories
Cell-type-specific responses
& more

We work with single-cell gene expression data from any provider, including 10X Genomics, Parse, BioClavis and ScaleBio.

Spatial Data Analysis

At Fios Genomics we routinely analyse spatial transcriptomics data to help our clients uncover meaningful biological insights. Our analyses enable a deeper understanding of:

Spatial gene expression patterns
Tissue architecture
Tumour microenvironments
In situ cellular interactions
& more

What’s more, we are platform agnostic, enabling us to analyse spatial data from all major technologies, including Slide-seq, 10x Genomics Visium and Xenium, NanoString GeoMx and CosMx and Vizgen MERSCOPE.

Whether you need help analysing one dataset or developing a full multi-omics strategy, we can support your research from raw data to publication-ready insights. If you have a transcriptomics project you’d like to discuss, simply fill out the form below, and we’ll get in touch to chat about your goals and how we can support your analysis.