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

项目摘要/摘要 癌症是一种在个体内部和跨个体之间都极其不同的疾病，导致需要 个体化治疗方案和早期发现和预防的新战略。每一种癌细胞 具有已获得的体细胞基因组改变和基因表达改变的独特特征 随着时间的推移。此外，每个肿瘤的微环境都含有免疫细胞的复杂混合物， 未改变的上皮细胞和间质细胞。这些不同类型的细胞之间的相互作用创造了一个生态系统 最终可以促进或抑制肿瘤的生长。单细胞rna测序(scrna-seq)是一种新的测序方法。 基因组学方法，能够研究单个细胞的转录本。有了这种技术， 研究人员可以评估切除组织中癌细胞转录途径的变异性，并可以 以公正的方式描述存在于肿瘤微环境中的细胞群体。然而， ScRNA-seq数据很复杂，需要先进的分析技术。我们希望在此之前 开发了计算工具，以开发、应用和验证处理和 分析scRNA-seq数据。具体地说，我们的工具包将由一个无缝工作流组成，该工作流包含 用于(A)质量控制和批次校正、(B)样本大小和测序深度估计、(C)单元- 级别分类和识别、细胞分类和降维，(D)差异表达和 不同的细胞丰度，和(E)功能途径图谱。我们的软件将通过以下方式满足重要需求 为scRNA-seq提供一个全面的、用户友好的、可接近的基于R的分析框架 由具有或不具有强大计算背景的研究人员进行。最终，这个工具包将加速研究 试图了解转录和细胞异质性如何在肿瘤的发生和发展中发挥作用 治疗。

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.