CHARTS: a web application for characterizing and comparing tumor subpopulations in publicly available single-cell RNA-seq data sets

Matthew N. Bernstein; Zijian Ni; Michael Collins; Mark E. Burkard; Christina Kendziorski; Ron Stewart

doi:10.1186/s12859-021-04021-x

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 Over the past three decades, the cancer research community has amassed large quantities of gene expression data from tumors. The premier example of such data was generated by The Cancer Genome Atlas [4], which generated bulk RNA-seq and microarray data from thousands of tumors across dozens of cancer types. These data have enabled a greater understanding into the molecular biology of cancer and have revealed great heterogeneity not only between cancer types, but also between tumors of the same cancer type [2]. Unfortunately, investigations utilizing this resource are limited by the fact that gene expression was profiled using bulk methods, which measure gene expression on average across thousands, or tens of thousands, of cells in a sample. With the advent of single-cell RNA-seq (scRNA-seq), investigators are now able to measure gene expression at the single-cell level thereby gaining access to the substantial heterogeneity of cells within a tumor and the tumor microenvironment [9]. Publicly available scRNA-seq cancer data sets offer unprecedented opportunity to better understand the mechanisms of tumor progression, metastasis, drug resistance, and immune evasion. However, analyzing these data in the aggregate is challenging, especially for those without strong computational skills. To this end, easy-to-use web-based tools are important for enabling the broader research community to perform integrative analyses and, in doing so, to increase their ability to leverage their knowledge and comprehensively examine scientific and/or clinically relevant hypotheses in multiple data sets. While a few web-based tools for analyzing scRNA-seq data are available, they are not designed specifically for cancer research or do not easily enable exploration of existing public data sets. For example, recent tools such as Alona [7] and Granatum [29] enable scRNA-seq analysis in the web browser; however, these tools are not cancer-specific and therefore do not enable important cancer-specific tasks such as classifying cells as being either transformed malignant cells or untransformed cells of the tumor microenvironment. Furthermore, these tools do not enable exploration of preprocessed, publicly available scRNA-seq data sets. Another tool, GREIN [16], enables exploration of public gene expression data, but it is neither single-cell specific nor cancer-specific and, consequently, does not implement features necessary for single-cell analysis such as cell type identification, clustering, or gene set enrichment, nor does it implement cancer-specific analyses such as malignancy classification. CancerSEA [27] enables exploration of gene set enrichment scores for gene sets pertaining to cancer-related processes, but does not enable visualization, differential expression, or cell type identification. In short, while web-based tools exist for exploring expression data, most do not allow for detailed analysis of scRNA-seq data across diverse tumors and data sets. To address this gap, we present CHARacterizing Tumor Subpopulations (CHARTS), a web application and associated computational pipeline for analyzing and characterizing publicly available cancer scRNA-seq data sets. As described in detail below, for each tumor in its database, CHARTS identifies clusters and enables exploration via interactive dimension-reduction methods. Derived clusters are annotated with cell types from the Cell Ontology [1] via CellO [3], with information provided on the probability of the specific cell type as well as its ancestors. For example, the data may provide substantial evidence to classify cells within a cluster as T cells, but less evidence may be available to classify cells into more specific functional groups (e.g. helper or memory T cells). In addition, for each cluster within each tumor, enrichment of genes involved in biological processes and pathways is provided. Genes that are differentially expressed between the cluster and others are also available. Finally, CHARTS can be used to distinguish malignant vs. non-malignant cells allowing for precise exploration into the interactions between cell subpopulations within the tumor microenvironment. CHARTS currently enables exploration of 198 tumors across 15 cancer types, and data is being continually added. CHARTS is freely available at charts.morgridge.org.

CHARTS: a web application for characterizing and comparing tumor subpopulations in publicly available single-cell RNA-seq data sets

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