Spatial Transcriptomics


Spatial transcriptomics allows scientists to measure and map the location of gene activity in a tissue sample. It expands the gene expression context provided by RNA‑Seq and scRNA‑Seq techniques alone into holistic, three-dimensional cellular behavior.

Partek® Flow® software provides an easy‑to‑use interface, advanced statistical algorithms, and rich interactive visualizations for start‑to‑finish spatial transcriptomics analysis. It also allows you to analyze and integrate single cell RNA-Seq, CITE-Seq, antibody capture sequencing, cell hashing data, and more.

Covering the Complete Spatial Analysis Process

Supporting10x Genomics® Visium® and Xenium®, NanoString® CosMx™, Akoya Biosciences®, and more in all standard formats.

Spatial Analysis Pipeline

Integrating tissue and transcriptomics data with side-by-side visualizations.

Integrated tissue and transcriptomics data with side-by-side visualizations in the Partek Flow Data Viewer.
Left: adult mouse brain slices (up: rostral section, down: caudal section). Result of graph-based clustering of gene expression data overlaid on tissue spots.
Center: adult mouse brain slices (up: rostral section, down: caudal section). Tissue spots are colored by the expression levels of Plp1 gene.
Right: UMAPs based on gene expression data of the rostral (top) and caudal (bottom) section. The colors represent graph-based clusters.

What You Can Do

  • Analyze multiple samples together or independently
  • Annotate cells from metadata
  • Perform QA/QC
  • Filter cell spots and features
  • Perform data normalization
  • Remove batch effects with multiple options (general linear model, Harmony, and Seurat 3 integration)
  • Spatially visualize and analyze gene expression
  • Perform dimension reduction, graph-based clustering, and interactively explore clusters in their spatial context
  • Discover biomarkers that define a tissue or cluster
  • Find differentially expressed genes in any region of interest
  • Perform gene ontology and pathway analysis

10x Genomics Ductal Carcinoma In Situ (DCIS) sample using 10x Genomics Xenium technology

Human Ductal Carcinoma In Situ (DCIS) sample in Partek Flow using 10x Genomics Xenium technology. The cells are colored by cell type.

Cells are colored by gene or protein expression, and augmented with the boundaries of the cell and nucleus.

Data from 10x Genomics Xenium – Cells are colored by gene or protein expression, and augmented with the boundaries of the cell and nucleus.

tissue samples from human lung profiled in Nanostring CosMx technology

Four tissue samples from human lungs profiled using the Nanostring CosMx technology. Cells are colored by cell type.

Powerful Tools & Features in an Easy-to-Use Application:

Taxonomic classification Cell Type Classification
Manually classify your cells by traditional methods or perform automatic cell classification using regression-based methods for human and mouse.
QA/QC icon Comprehensive QA/QC Tools
Evaluate the status of your analysis and optimize downstream viability using a variety of quality assurance/control tools and reports.
QA/QC icon Visualize and integrate tissue with transcriptomic data
Characterize gene and protein expression in their native spatial context for a detailed map of tissue architecture.
QA/QC icon Answer biological questions using spatial morphology
Overlay gene expression data to visualize spatial relationships in a biological context.
Differential analysis Explore Multiple Samples Simultaneously
Analyze and visualize multiple samples together or independently and discover biological variations with combined and split-by-sample analysis.
Differential analysis Detect Differential Expression Patterns 
Powerful statistical analysis tools help you determine patterns and markers in datasets, including genes, proteins, and pathways.
pathway analysis Investigate Gene Set and Pathway Enrichment
Learn about the biology underlying gene expression changes using gene ontology (GO) or pathway enrichment analysis.
scale icon Define and compare cell populations through biomarker discovery
Take advantage of extensive statistical models to make make meaningful comparisons using biomarkers to define a cell population, condition, or another parameter within your study.

Spatial Webinars


Ask a question or request a software demonstration/trial

Kathi GoscheSpatial Transcriptomics