Single-cell RNA-seq data analysis and interpretation

November 12, 2021
Single-cell RNA-seq (scRNA-seq) is commonly used among researchers to characterize transcriptional activity across thousands of cells to discover complex and rare cell populations and gain further insight into the dynamic nature of the transcriptome. In this training, attendees will learn how to analyze and interpret their own scRNA-seq data using QIAGEN CLC Genomics Workbench and QIAGEN Ingenuity Pathway Analysis (IPA).

In this 90-minute training, attendees will learn how to:
• Start with fastq, cell matrix file and/or differential expression file for scRNA-seq data
• Per user preference, either automate or customize their analysis pipeline/workflow
• 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 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 highquality images or tabular format

Speaker: Araceli Cuellar, Field Application Specialist, QIAGEN Digital Insights

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