Explore how to analyze and interpret your own single-cell RNA-seq (scRNA-seq) data using QIAGEN CLC Genomics Workbench and QIAGEN Ingenuity Pathway Analysis (IPA).

In this 90-minute training, you’ll learn how to:
• Start with FASTQ, cell matrix file and/or differential expression file for scRNA-seq data
• Either automate or customize your analysis pipeline/workflow, depending on your needs
• Easily generate visualizations such as t-SNE, UMAP, heatmap, differential expression table, dot plots and more
• Upload differential expression data to QIAGEN IPA (either from QIAGEN CLC or from another source)
• Perform pathway analysis on scRNA-seq data and compare different clusters to discover novel biological mechanisms, cell type-specific biomarkers and key regulators/targets
• Export results in the form of high-quality images or tabular format

Slides from a previous similar training: https://qiagen.showpad.com/share/WAXz1vrHBsvArdeceWpcX

Explore an easy way to extend your in-house resources to include integrated single-cell data and analysis

Single-cell sequencing is a powerful technology that offers a focused approach to biomarker and target discovery. If you're working in oncology research, you most likely use it to develop new diagnostic biomarkers or anti-tumor treatments. If you study the immune system, you may use it to detect individual immune cells or to distinguish among different immune cell groups to propose new targets for disease treatment.

Yet, if you work with single-cell sequencing data, you know that beyond the many possibilities lie many challenges. You probably obtain single-cell RNA-seq (scRNA-seq) data from multiple programs and disease indications, and from both publicly and internally generated data. These data are difficult to integrate and align. What's more, the sheer volume of data and noise within each dataset make it extremely challenging to draw meaningful conclusions.

QIAGEN Discovery Bioinformatics Services eases the challenges of working with single-cell sequencing data to quickly and efficiently help answer questions relevant to your research goals. Our team supplements your workforce with our bioinformatics experts and performs analyses tailored to your interests. We offer a range of support, such as building custom pipelines and server solutions, as well as provide hypercare support and training. We take on everything from secondary analysis services to in-depth analysis of biological data.

Need support with content curation? Leave it up to us, whether it's literature, datasets, pathways or a customized integrated 'omics data collection based on your internal data ('internal Land'). We'll perform deep meta-analysis on data collections and take on the bulk data processing. By working with us, you'll save time organizing and visualizing internal pipelines and results so you can focus on validating your hypothesis to more quickly make your next big discovery.

Easily query thousands of single-cell datasets to facilitate hypothesis generation

In addition to our basic services, our custom-built single-cell analysis pipelines incorporate cutting-edge public and/or proprietary bioinformatics tools to identify rare cell types, track cell lineage, infer developmental trajectories and determine inter-cellular interactions of single cells. We do this using data from many single-cell sequencing methods, such as cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq), assay for transposase-accessible chromatin by sequencing (ATAC-seq), single-cell T cell receptor sequencing (scTCR-seq) and single nucleus RNA sequencing (snRNA-seq), among others. Our experts query the OmicSoft Single Cell Land database to answer your specific biological questions, such as:

• How is a particular target gene expressed across cell types?
• How is this target gene expressed across multiple single-cell projects?
• What is the expression signature for a specific disease state?
• What are the different cell types and their abundance across datasets?
• What is the differential gene expression of treatment vs. control samples in a particular cell type (e.g. monocytes)?

Figure 1. Example output showing the abundance of different cell types across datasets.

Let us help you manage the unique challenges of analyzing scRNA-seq data

General challenges of analyzing scRNA-seq data

We design our pipelines to handle your most common scRNA-seq analysis challenges. These challenges include the relatively small number of sequencing reads, the sparsity of data, limited processing power and cell population heterogeneity. Gene expression profiling by scRNA-seq is also inherently noisier than bulk RNA-seq, making effective data analysis more complicated. To address these caveats of working with scRNA-seq data, we:

• Use a customized unified pipeline script to process scRNA-seq datasets and store the data into internal data lakes called 'Lands'
• Perform stringent quality control checks to eliminate data from low-quality cells
• Make batch corrections before performing downstream analysis to avoid incorrect data integration and interpretation due to batch effect, which is another major challenge in scRNA-seq data analysis
• Include normalization steps to correctly interpret the results from scRNA-seq data for cell subpopulation identification and differential expression calling
• Use imputation as a strategy to replace the missing data (dropouts) with substituted values to recover the accurate expression level of all genes
• Employ strategies such as dimensionality reduction or feature selection to deal with the high dimensional nature of scRNA-seq data, which involves thousands of genes and a large number of cells
• Mitigate statistical issues arising from cellular heterogeneity using a set of known markers or use algorithms for unsupervised clustering. We incorporate public and proprietary algorithms in the pipeline, such as Seurat, which can facilitate the identification of cell subpopulations
• Identify distinct subpopulations or groups of cells using differential expression analysis
• Provide a framework to run the samples in parallel using Amazon WorkSpace to store the data

Figure 2. CellMap employing dimension reduction to explore cell types across samples.

Metadata curation challenges

Another challenge of scRNA-seq data is metadata curation. Due to a lack of standards for the deposition of cell-level metadata, you could end up spending the majority of your time processing data and curating metadata. Why not let our team of curators handle the metadata? Our team of experts can manually curate single-cell projects and precisely curate cell clusters to help you more quickly and easily answer your biological questions. To do this, we:

• Extract/curate project, technical, sample and clinical metadata from data submissions and publications
• Apply controlled vocabularies and curation standards
• Curate expected results from papers, such as clustering experiments, cell types and key markers/differentially expressed genes
• Add statistical comparisons using the curated metadata

Differential expression analysis challenges

After identifying the cell type identities of the scRNA-seq clusters, you'd typically perform differential expression analysis between conditions within particular cell types. While commercial algorithms perform differential expression analysis, the p-values from these analyses are often inflated because a cell is treated as a sample. Since single cells within a sample have variation, we can compare gene expression across individual cells. This is known as pseudo-bulk RNA-seq data. Our team implements this approach to create a custom pseudo-bulk integrated 'omics data collection ('Land') so you can easily explore single-cell data to make accurate comparisons.

Figure 3. Explore pre-computed differential expression of marker genes in different cell types, to understand how a target gene differentially expressed in different cell clusters. In this example output, you can see the differential expression of CD34 across different cell types in normal vs. hematologic cancer samples.

Deliverables that get you quick insights

On all our projects, we work with you to determine the output and deliverables that best fit your needs. Here are some examples of our deliverables that'll support your work using single-cell technologies:

• A centralized database that biologists use for disease-focused queries from a customized integrated 'omics data collection based on your internal data ('internal Land')
• Custom scripts enabling the analysis of both human and non-human data
• Support for hypothesis validation by combining information from multiple studies to create a unified picture of biomarkers and disease signatures
• Differential gene expression analysis that assesses changes of a candidate target gene related to a disease
• Signature-based analyses to gain insights into which diseases are relevant for a target gene
• Assessment of relative cell type (cell state) abundance at sample-level resolution for multiple tissue types
• Reports of both differentially expressed genes and gene signatures at cell-type resolution to help you quickly prioritize drug targets, biomarkers and compounds
• Detection of novel cell subpopulations that are responsible for a response to drug treatments (responders vs. resistant cells)
• Identification of subpopulations of cells with variations in gene expression that can provide insight into developmental trajectories (for example, T-helper cell development, B-cell differentiation)

Figure 4. Single-cell project workflow.

Get in touch

By working with QIAGEN Discovery Bioinformatics Services on projects for scRNA-seq processing, you'll save time and increase accuracy to more quickly gain a robust and insightful understanding of complex disease. We'll take on the aspects of your bioinformatics that are most challenging and limiting for you. Our customized support empowers you to more readily gain a robust and insightful understanding of the biological mechanisms in your data, to accelerate and drive your next big discovery.

Would you like to reduce the burden of working with scRNA-seq data to more quickly reach your next biomarker or target discovery? Learn more and request a consultation about our range of bioinformatics services that'll extend and scale your in-house resources. Contact us today at QDIservices@qiagen.com to get your custom single-cell project started. Together, let's unravel the biological discoveries hidden in single cells.

Discover our new platform for exploring highly-curated single-cell RNA-seq data 

Are you looking for highly curated single-cell (scRNA-seq) data to validate your latest results? Have you ever spent hours or days processing data from a publication only to find your gene of interest isn’t detected?  Maybe you’ve tried a single-cell portal but find it lacks critical metadata? 

Discover our new solution that makes single-cell data analysis easy while delivering deep and novel insights. We’ve extended our valuable QIAGEN OmicSoft DiseaseLand and OncoLand frameworks to enable rapid exploration of single-cell data, creating the new QIAGEN OmicSoft Single Cell Land. Our team of certified curators manually curated hundreds of single-cell projects generating thousands of precisely curated cell clusters. You can access millions of cells that are searchable using any one of our more than 70 metadata attributes. Compare across projects and easily find the data you are looking for. Then, export your findings graphically, tabularly or to an open data standard.  

The new QIAGEN OmicSoft Single Cell Land lets you: 

• Find data that are relevant to your project with manually curated datasets, including metadata on the project, sample and cell type  

• Search, explore and compare scRNA-seq data across projects in a consistent, reproducible manner, using gene, tissue, disease, cell-type or any of the other more than 70 curated metadata attributes 
• Visualize the data to extract key insights: You can discover dimension reduction results via tSNE or UMAP plots, expression distribution plots or violin plots, cell cluster plots, heatmaps and more 
• Enhance discoveries from thousands of bulk cell experiments 

Learn more about how QIAGEN OmicSoft Single Cell Land can support your research. 

Request a trial to explore how this database of scRNA-seq datasets can dramatically facilitate and accelerate your single-cell genomics research. 

Check out the new features of QIAGEN CLC Genomics

Are you struggling to find a bioinformatics analysis tool that meets your specific research needs? One that is easy-to-use, yet powerful, scalable and flexible? We are excited to announce the launch of QIAGEN CLC Genomics 21.0, packed with new features to help you take your data analysis to the next level. QIAGEN CLC Genomics has solutions for all your sequencing, NGS and 'omics data analysis needs. Get the features that meet your research goals with our new licensing models developed for this v21 release. Our favorite new features and functions now available in v21 include:

• Import reads from Illumina BaseSpace or Amazon S3, using the Cloud Plugin
• Build end-to-end Sanger sequencing workflows, from trace data to consensus alignments: Sanger assemblies can now also be visualized with read wrapping
• Name workflow outputs automatically based on metadata or batch identifiers

Illumina BaseSpace integration Data stored in Illumina BaseSpace can now be seamlessly imported into the Workbench. To get started, just install the Cloud Plugin. Illumina BaseSpace will then be available to select as an import location.

Sanger workflows Draw end-to-end workflows for the analysis of Sanger reads, starting with on-the-fly import of trace files. If you run the trimming and assembly of forward-reverse Sanger reads in batch mode, the outputs will be named after the batch unit – or you can use advanced custom output naming patterns in workflows to include even more information in the file names. Extract consensus sequences and create alignments within the same workflow. You can now also visualize Sanger assemblies in the wrapped view.

New in the v21 release, QIAGEN CLC Genomics now has three key offerings, with packages ranging from basic (QIAGEN CLC Main Workbench), advanced (QIAGEN CLC Genomics Workbench) and premium (QIAGEN CLC Genomics Workbench Premium), to meet your specific sequence and ‘omics data analysis needs.

QIAGEN CLC Main Workbench: For basic sequencing analysis

• Primer design
• Multiple sequence alignment tools
• Phylogenic analysis tools
• Sanger sequencing analysis: Workflow enabled with v21
• Molecular cloning
• Gene expression analysis
• 3D molecular modeling
• Support most sequence formats, including Vector NTI
• Workflow editor
• Whole genome alignment: The v21 release includes several improvements to visualizations and functionality to help you more easily gain insights into your microbial genome research. Read more here.

QIAGEN CLC Genomics Workbench: For advanced sequencing analysis

Includes all the features of the QIAGEN CLC Main Workbench, plus:

• Supports de novo assembly of NGS reads
• Supports all organisms
• Resequencing analysis and variant calling
• Long read analysis (PacBio, Oxford Nanopore): For the v21 release, the Long Read Support plugin now offers full functionality and a range of tools for working with long, error-prone reads, such as the long reads typically produced by PacBio or Oxford Nanopore sequencing technologies.
• RNA-seq (including miRNA and lncRNA), ChIPseq, DNA methylation
• Biomedical genomics analyses
• Haplotype calling: (Expected release: June, 2021) Allows direct import, export and validation of variants and supports phasing information and delivers variant locus, allele variants, haplotype alleles and haplotypes.
• QIAseq panel analysis workflows
• Download data stored in your BaseSpace or AWS S3 account using the Cloud Plugin.

QIAGEN CLC Genomics Workbench Premium: Our full-featured solution

Includes all the features of the QIAGEN CLC Genomics Workbench, plus:

• QIAGEN CLC Microbial Genomics Module, for:
  • Microbial typing
  • Antimicrobial resistance
  • Metagenomics characterization
  • Outbreak and strain typing analysis
• QIAGEN CLC Genome Finishing Module for assembling and finishing of genomes
• The new QIAGEN CLC Single Cell Analysis Module released in this v21 launch enables analysis from raw FASTQ files or imported count matrices to clusters of cells with annotated cell types and differentially expressed genes. Visualize data from over a million cells at once.

QIAGEN CLC Genomics Server: All CLC functionality is also available as enterprise software, which operates on any hardware server. The Genomics Analysis Portal allows sample- and workflow centric views of analyses run on the server.

QIAGEN CLC Genomics Cloud Engine: Run CLC workflows in the cloud on data stored in your BaseSpace or AWS S3 account. Launch workflows from the CLC Genomics Workbench or Server in the cloud using the Cloud Plugin.

Learn more about the applications supported by our portfolio of QIAGEN CLC Genomics solutions, and request a consultation with one of our experts to help you find the right QIAGEN CLC toolset for your research goals.

Not too long ago, we returned from Oxford Global’s 8^th Annual NGS Congress. While the majestic host city of London was a compelling draw, it was difficult to tear ourselves away from the all action inside the Novotel West hotel.

We hope you had a chance to stop by our booth during the event, where we offered three separate presentations and met countless new customers and colleagues. Our speakers covered topics ranging from overcoming challenges in single-cell isolation, to taking an integrative approach to biomarker discovery, and we had a special guest speaker: Dr. Dominic Graham Rothwell from the University of Manchester. Dr. Rothwell discussed how NGS can help realize the growing clinical potential of using liquid biopsies for cancer.

While liquid biopsy was a significant focus for us, other topics covered at the event included CRSPR gene editing, the 100,000 genomes project, developments in NGS technologies and platforms, and NGS data management, to name but a few. We were thrilled to be part of the action at this year’s event, and will definitely be back again next year. In the meantime, please contact us if you have questions.