Do you want to make unique biological discoveries with your single-cell data? Do you need to quickly identify cells of interest in your research or find similar experiments to make insightful comparisons? Want to visualize single-cell data in real-time to make quick yet well-informed hypotheses and decisions?
QIAGEN Digital Insight solutions for single-cell gene expression analysis help biologists and bioinformaticians reveal complex and rare cell populations, uncover regulatory relationships among genes and analyze and visualize gene expression differences among different cell types, or within a unique cell type. Interpret and explore the underlying biology and integrate ‘omics datasets from different platforms to gain insights into the biology and molecular drivers of specific cell populations.
Tools and techniques for single-cell gene expression analysis:
Browse our collection of case studies, blogs, articles and tutorials to learn how QIAEN Digital Insights solutions can accelerate your single-cell genomics discoveries.
Perform single-cell data analysis from raw FASTQ files to clusters of cells with annotated cell types and differentially expressed genes using QIAGEN CLC Single Cell Analysis Module. Additional functionality includes workflows for TCR- and BCR-repertoire analysis to help you study immune response, ATAC-seq support to study gene regulation via open chromatin analysis and RNA velocity analysis to study the development and differentiation of cell systems.
Large scRNA-seq datasets can be deployed locally using QIAGEN CLC Genomics Server, or deployed in the cloud, using QIAGEN CLC Genomics Cloud Engine.
Deepen your understanding of disease biology to cellular resolution using OmicSoft DiseaseLand/OncoLand together with Single Cell Lands – collections of highly curated single cell and bulk RNA-seq samples. Search for and compare your genes across scRNA-seq datasets from normal and disease tissue to discover exactly where your genes are expressed. Discover cell-specific biomarkers by exploring manually curated cell types.
Interactively analyze, visualize and annotate millions of cells at once, directly from your laptop.
Quickly visualize gene expression pattern within cell clusters. Explore where your gene is expressed in hundreds of disease and normal cell types across dozens of diseases, then expand your understanding by discovering disease-relevant datasets differentially expressing these genes. Search gene signatures to discover cell types with up- or down-regulation.
Interactive 2D and 3D visualizations of UMAP and tSNE plots for single-cell expression data can be overlaid with cluster information, cell annotations and gene expressions.
Visualize differential gene expression using summary and detailed plots (volcano plot, heat map, dot plot, etc.), interactive and on-the-fly rendering based on your selection and filter settings.
Discover subtle differences in gene expression among individual cells by importing and processing single-cell experiments, starting with raw FASTQ or quantification data using a scRNA-seq pipeline.
Incorporate your own bulk RNA-seq and scRNA-seq data into the OmicSoft Lands framework to enable rapid search and discovery of your own valuable datasets, compared side-by-side with curated OmicSoft data.
Reveal differential gene expression between pairs of selected clusters or between a cluster and the rest of the cells using the UMAP or tSNE plot editor.
Harness the power of QIAGEN’s curated content and algorithms to collaboratively analyze and annotate cell clusters.
With the QIAGEN CLC Single Cell Analysis module, annotate individual cells using our cell classifier. Use differential gene expression for GO analysis to guide additional manual cluster annotation. Perform manual cell type annotation with just a few clicks by using the Lasso tool.
Highly granular and manual curation of scRNA-seq datasets enables you to easily find, filter, parse and integrate data. Our detailed annotations enable you to quickly and easily find the right datasets to make insightful data comparisons.
Browse annotations to discover projects that include cell types of interest, and discover statistical comparisons including these cell types.
Characterize outlier cells within a population to help you understand how cells develop drug resistance, or how relapse occurs, during cancer treatment. Analyze the genome of circulating tumor cells to reveal drivers of tumorigenesis or biological pathways involved in metastasis and cancer cell propagation. Integrate single-cell ‘omics datasets from different platforms to reveal unique patterns in a specific cancer cell type or subset.
Explore where your gene is expressed across thousands of tumor and normal datasets across many cancer types with OncoLand. Deepen your understanding with Single Cell Land’s curated cell types. Search gene signatures to discover normal and cancer cell types with up- or down-regulation. Compare multiple genes together to explore correlation and validate cancer cell gene signatures. Visualize single-cell resolution of expression patterns in tumor vs. normal tissues. Subgroup samples by clinical metadata and expression patterns, to create impactful visualizations.
QIAGEN Ingenuity Pathway Analysis allows you to analyze expression profiles of specific pathways and interpret and explore the underlying biology.
Analyze expression profiles of specific pathways. Interpret and explore the underlying biology.
Find experimental validation for your candidate biomarkers, discover new co-expressed genes for indications of interest, and make surprising connections to studies where your gene is relevant, enabling new explorations.
Upload differential gene expression results into QIAGEN IPA to gain deeper insight into the biological mechanism driving the expression differences.
Request a consultation with one of our bioinformatics specialists to discuss your specific research requirements for single-cell gene expression analysis.
Analyze NGS and 'omics data from Sample to Insight using our highly visual and specialized tools for data normalization, quality control, read mapping, gene expression and more
Generate insights from complex 'omics data and deepen your understanding of the biological mechanisms hidden in your experiments
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