An interactive analysis toolkit for single cell RNA-seq in cancer research

用于癌症研究中单细胞 RNA-seq 的交互式分析工具包

• 批准号: 9389818
• 负责人: William Evan Johnson
• 金额: $ 39.8万
• 依托单位: BOSTON UNIVERSITY MEDICAL CAMPUS
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-08-01 至 2020-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9389818
• 关键词: Address; Automobile Driving; Benchmarking; Bioconductor; Biological; Breast; Cancerous; Cell Separation; Cells; Classification; Complex; Complex Mixtures; Computer Simulation; Computer software; Data; Data Analyses; Data Quality; Data Set; Data Storage and Retrieval; Development; Dimensions; Disease; Early Diagnosis; Ecosystem; Effectiveness; Epithelial Cells; Evolution; Gene Expression; Genes; Genetic Transcription; Genome; Genomic approach; Heterogeneity; Imagery; Immune; Immune system; Immunotherapy; Individual; Lead; Lesion; Lung; Lung Neoplasms; Malignant Neoplasms; Mammary Neoplasms; Metastatic breast cancer; Methods; Morphologic artifacts; Mutation; Oncogenic; Outcome; Pathway interactions; Pattern; Phenotype; Play; Population; Precision therapeutics; Premalignant; Prevention; Quality Control; Refractory; Research Design; Research Personnel; Resected; Role; Sample Size; Sampling; Site; Smoker; Software Framework; Source; Standardization; Stromal Cells; Structure of thyroid parafollicular cell; Techniques; Technology; Therapeutic; Time; Tissues; Treatment Protocols; anticancer research; base; cancer cell; cell type; computerized data processing; computerized tools; differential expression; genomic data; graphical user interface; individualized medicine; insight; interactive tool; killings; method development; neoplastic cell; non-smoker; novel; novel strategies; response; single cell analysis; standard of care; tool; transcriptome; transcriptome sequencing; treatment response; tumor; tumor growth; tumor heterogeneity; tumor microenvironment; user-friendly

Project Summary/Abstract Cancer is an extremely heterogeneous disease both within and across individuals, leading to the need for individualized treatment regimens and novel strategies for early detection and prevention. Each cancer cell harbors a unique profile of somatic genome alterations and gene expression changes that have been acquired over time. In addition, the microenvironment of each tumor contains a complex mixture of immune cells, unaltered epithelial cells, and stromal cells. Interactions between these various cell types create an ecosystem that can ultimately promote or inhibit tumor growth. Single cell RNA-sequencing (scRNA-seq) is a new genomic approach that enables the study of the transcriptomes of individual cells. With this technology, researchers can assess the variability in transcriptional pathways in cancer cells from resected tissue and can characterize cellular populations present in the tumor microenvironment in an unbiased fashion. However, scRNA-seq data are complex and require advanced analytical techniques. We hope to leverage previously developed computational tools to develop, apply and validate a coordinated framework for processing and analyzing scRNA-seq data. Specifically, our toolkit will consist of a seamless workflow that incorporates modules for (A) quality control and batch correction, (B) sample size and sequencing depth estimation, (C) cell- level classification and identification, cell sorting, and dimension reduction, (D) differential expression and differential cell abundance, and (E) functional pathway profiling. Our software will fill a significant need by providing a comprehensive and user-friendly R-based analysis framework for scRNA-seq that is approachable by researchers with or without strong computational backgrounds. Ultimately, this toolkit will accelerate studies seeking to understand how transcriptional and cellular heterogeneity plays a role in tumor development and treatment.

项目总结/文摘