Core 2: Computational and Data Integration

核心2：计算和数据集成

• 批准号: 10414448
• 负责人: Kelly Valentine Ruggles
• 金额: $ 22.21万
• 依托单位: NEW YORK UNIVERSITY SCHOOL OF MEDICINE
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-15 至 2027-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10414448
• 关键词: Artificial Intelligence; Automobile Driving; Biological Models; Cancer cell line; Cells; Code; Communities; Complex; Computer Analysis; Computing Methodologies; Data; Data Analyses; Data Set; Development; Disease; Disseminated Malignant Neoplasm; Encyclopedias; Ensure; Fibroblasts; Fostering; Foundations; Future; Genes; Genetic Engineering; Genetic Transcription; Goals; Graph; Health; Heterogeneity; Histology; Individual; Interdisciplinary Communication; Internet; Intuition; Malignant Neoplasms; Melanoma Cell; Methods; Microscopic; Microscopy; Modeling; Molecular; Neoplasm Metastasis; New York; Online Systems; Pathologic; Pathway Analysis; Patients; Process; Records; Reporting; Reproducibility; Research; Research Project Grants; Resolution; Resources; Scientist; Signal Pathway; Skin; Specimen; Standardization; Supervision; System; Technology; Testing; The Cancer Genome Atlas; Time; Tissues; Translations; Tumor Tissue; Universities; analysis pipeline; base; cancer genomics; cell type; clinical application; clinically relevant; computational pipelines; computerized data processing; computing resources; dashboard; data access; data analysis pipeline; data exploration; data integration; data sharing; data standards; data visualization; digital; experience; genetic signature; innovation; insight; interdisciplinary approach; interdisciplinary collaboration; lymph nodes; melanoma; melanomagenesis; metastatic process; mouse model; multiplexed imaging; nano-string; neoplasm resource; neoplastic cell; novel; predictive modeling; programs; single-cell RNA sequencing; skills; success; tool; transcriptomics; tumor; tumor heterogeneity; tumor progression

COMPUTATIONAL AND DATA INTEGRATION CORE SUMMARY The primary objective of Core C is to strengthen the New York University Langone Health (NYULH) Metastasis Research Network Center’s research goals by providing standardized, robust, and harmonized workflows to acquire, analyze, integrate, visualize and share data from single cell RNA sequencing, spatial transcriptomics, digital spatial profiling and highly multiplexed microscopy platforms that are utilized by all three research projects and Core B. Core C will build on multi-dimensional molecular and tissue histology data that are generated with specimens obtained from both genetically engineered melanoma mouse models and patient specimens to: 1) develop and use a computational analysis pipeline that is robust, transparent, reproducible and scalable; 2) integrate metastasis driving molecular features of melanoma cells, stromal fibroblasts and lymph-nodes in space and time with data from complementary technology platforms; and 3) build a web based dashboard that will allow basic scientists and clinicians to analyze, explore and visualize this complex molecular and spatial information in an intuitive and meaningful way. We envision that our robust data analysis pipelines and interactive data visualization platforms will foster inter- disciplinary collaborations within the larger metastasis field and spark new ideas to strengthen our understanding of early tumor cell dissemination, cross talk between tumor cells and their cellular and molecular microenvironments and determinants of melanoma metastasis and metastatic cancer progression in general.

预防和数据集成核心摘要 核心C的主要目标是加强纽约大学朗格尼健康（NYULH）转移 研究网络中心的研究目标，通过提供标准化的，强大的，协调的工作流程， 获取、分析、整合、可视化和共享来自单细胞RNA测序、空间转录组学 所有三个研究项目都使用了数字空间分析和高度复用的显微镜平台 和核心B。 核心C将建立在标本产生的多维分子和组织组织学数据上 从遗传工程化的黑素瘤小鼠模型和患者样本中获得，以：1）开发和 使用稳健、透明、可再现和可扩展的计算分析管道; 2）集成 黑色素瘤细胞、基质成纤维细胞和淋巴结在空间和时间上的转移驱动分子特征 利用来自互补技术平台的数据;以及3）建立一个基于网络的仪表板， 科学家和临床医生分析，探索和可视化这种复杂的分子和空间信息， 直观和有意义的方式。 我们设想，我们强大的数据分析管道和交互式数据可视化平台将促进互联网， 在更大的转移领域的学科合作，并激发新的想法，以加强我们的理解 早期肿瘤细胞播散，肿瘤细胞与其细胞和分子之间的相互作用 微环境和决定因素的黑色素瘤转移和转移性癌症进展的一般。