This information is over 2 years old. Information was current at time of publication.{"id":13571,"date":"2021-03-23T11:09:26","date_gmt":"2021-03-23T15:09:26","guid":{"rendered":"https:\/\/info.hsls.pitt.edu\/updatereport\/?p=13571"},"modified":"2021-04-05T16:25:03","modified_gmt":"2021-04-05T20:25:03","slug":"introducing-a-new-tool-for-single-cell-gene-expression-analysis","status":"publish","type":"post","link":"https:\/\/info.hsls.pitt.edu\/updatereport\/introducing-a-new-tool-for-single-cell-gene-expression-analysis\/","title":{"rendered":"Introducing a New Tool for Single-Cell Gene Expression Analysis"},"content":{"rendered":"

Are you ever in need of easy-to-use, robust tools and algorithms to help analyze, annotate, and identify cell types in your single-cell NGS data? Do you ever want to discover key biomarkers for cell types, tissues, and diseases without the tedious work of curating and analyzing each dataset? Do you ever look for the biological context of specific pathways underlying your single-cell data?<\/p>\n

HSLS Molecular Biology Information Service<\/a> is pleased to announce that we now license three concurrent seats for a new single-cell gene expression analysis module within the long-licensed CLC Genomics Workbench<\/a> from QIAGEN Digital Insights. It is point-and-click, GUI-based software that does not require programming experience to use. Uncover new and unexpected biological discoveries from individual cells via these key applications:<\/p>\n

scRNA-seq for biomarker discovery\u2014perform single-cell data analysis from raw FASTQ files to clusters of cells with annotated cell types and differentially expressed genes<\/li>\n

Visualization of scRNA-seq data\u2014create interactive 2D and 3D visualizations of UMAP and tSNE plots for single-cell expression data overlaid with cluster information, cell annotations, and gene expressions<\/li>\n

Cross comparison of scRNA-seq data\u2014discover subtle differences in gene expression among individual cells by importing and processing single-cell experiments, starting with raw FASTQ or quantification data using an scRNA-seq pipeline<\/li>\n

Annotation of cell types\u2014annotate individual cells using a cell classifier and differential gene expression for GO analysis to guide additional manual cluster annotation<\/li>\n

Single-cell pathway analysis\u2014upload differential gene expression results into HSLS-licensed Ingenuity Pathway Analysis<\/a> to gain deeper insight into the biological mechanism driving expression differences<\/li>\n<\/ul>\n
Researchers with a Pitt or UPMC email address may register for access to CLC Genomics Workbench<\/a> software, which is required to install a plugin for the CLC single-cell gene expression analysis module. Numerous resources are available to get you started using the tool:<\/p>\n