Matthew N Bernstein1, Zijian Ni2, Michael Collins1, Mark E Burkard3,4, Christina Kendziorski5, Ron Stewart6. 1. Morgridge Institute for Research, Madison, WI, 53715, USA. 2. Department of Statistics, University of Wisconsin - Madison, Madison, WI, 53706, USA. 3. Department of Medicine, Hematology/Oncology, University of Wisconsin - Madison, Madison, WI, 53705, USA. 4. University of Wisconsin Carbone Cancer Center, Madison, WI, 53705, USA. 5. Department of Biostatistics and Medical Informatics, University of Wisconsin - Madison, Madison, WI, 53792, USA. kendzior@biostat.wisc.edu. 6. Morgridge Institute for Research, Madison, WI, 53715, USA. rstewart@morgridge.org.
Abstract
BACKGROUND: Single-cell RNA-seq (scRNA-seq) enables the profiling of genome-wide gene expression at the single-cell level and in so doing facilitates insight into and information about cellular heterogeneity within a tissue. This is especially important in cancer, where tumor and tumor microenvironment heterogeneity directly impact development, maintenance, and progression of disease. While publicly available scRNA-seq cancer data sets offer unprecedented opportunity to better understand the mechanisms underlying tumor progression, metastasis, drug resistance, and immune evasion, much of the available information has been underutilized, in part, due to the lack of tools available for aggregating and analysing these data. RESULTS: We present CHARacterizing Tumor Subpopulations (CHARTS), a web application for exploring publicly available scRNA-seq cancer data sets in the NCBI's Gene Expression Omnibus. More specifically, CHARTS enables the exploration of individual gene expression, cell type, malignancy-status, differentially expressed genes, and gene set enrichment results in subpopulations of cells across tumors and data sets. Along with the web application, we also make available the backend computational pipeline that was used to produce the analyses that are available for exploration in the web application. CONCLUSION: CHARTS is an easy to use, comprehensive platform for exploring single-cell subpopulations within tumors across the ever-growing collection of public scRNA-seq cancer data sets. CHARTS is freely available at charts.morgridge.org.
BACKGROUND: Single-cell RNA-seq (scRNA-seq) enables the profiling of genome-wide gene expression at the single-cell level and in so doing facilitates insight into and information about cellular heterogeneity within a tissue. This is especially important in cancer, where tumor and tumor microenvironment heterogeneity directly impact development, maintenance, and progression of disease. While publicly available scRNA-seq cancer data sets offer unprecedented opportunity to better understand the mechanisms underlying tumor progression, metastasis, drug resistance, and immune evasion, much of the available information has been underutilized, in part, due to the lack of tools available for aggregating and analysing these data. RESULTS: We present CHARacterizing Tumor Subpopulations (CHARTS), a web application for exploring publicly available scRNA-seq cancer data sets in the NCBI's Gene Expression Omnibus. More specifically, CHARTS enables the exploration of individual gene expression, cell type, malignancy-status, differentially expressed genes, and gene set enrichment results in subpopulations of cells across tumors and data sets. Along with the web application, we also make available the backend computational pipeline that was used to produce the analyses that are available for exploration in the web application. CONCLUSION: CHARTS is an easy to use, comprehensive platform for exploring single-cell subpopulations within tumors across the ever-growing collection of public scRNA-seq cancer data sets. CHARTS is freely available at charts.morgridge.org.
Authors: Ashley M Laughney; Jing Hu; Nathaniel R Campbell; Samuel F Bakhoum; Manu Setty; Vincent-Philippe Lavallée; Yubin Xie; Ignas Masilionis; Ambrose J Carr; Sanjay Kottapalli; Viola Allaj; Marissa Mattar; Natasha Rekhtman; Joao B Xavier; Linas Mazutis; John T Poirier; Charles M Rudin; Dana Pe'er; Joan Massagué Journal: Nat Med Date: 2020-02-10 Impact factor: 53.440
Authors: Kevin R Moon; David van Dijk; Zheng Wang; Scott Gigante; Daniel B Burkhardt; William S Chen; Kristina Yim; Antonia van den Elzen; Matthew J Hirn; Ronald R Coifman; Natalia B Ivanova; Guy Wolf; Smita Krishnaswamy Journal: Nat Biotechnol Date: 2019-12-03 Impact factor: 54.908