Developing new computational tools for spatial transcriptomics data

开发空间转录组数据的新计算工具

基本信息

批准号：
10654027
负责人：
Mengjie Chen
金额：
$ 38.13万
依托单位：
UNIVERSITY OF CHICAGO
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-09-16 至 2025-06-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10654027
关键词：
Achievement Address Architecture Awareness Biological Breast Cancer Patient Case/Control Studies Cell Culture Techniques Clinical Research Collection Communities Computer software Data Data Analyses Data Set Data Sources Detection Development Dimensions Disease Event Foundations Gene Expression Genes Genomics Goals Health Heterogeneity Human Image Immune response Investigation Joints Link Location Malignant Neoplasms Maps Measurement Methodology Methods Neurodegenerative Disorders Pattern Performance Phenotype Property Research Resolution Sample Size Sampling Slide Software Tools Structure Technology Tissue Sample Tissues Training Treatment outcome Validation Variant Work bioinformatics infrastructure cohort computerized data processing computerized tools deep learning deep neural network digital falls frontier genomic data histological image innovation learning strategy multiple data sources new technology novel open source phase II trial rapid detection simulation single-cell RNA sequencing technology platform tissue culture tool transcriptomics transfer learning translational study user friendly software user-friendly

项目摘要

Project Summary Spatial transcriptomics is a groundbreaking new technology that allows measurement of gene ac- tivity in a tissue sample while mapping where the activity is occurring. It holds the promise to facilitate our understanding of spatial heterogeneity underlying essential phenotypes and diseases, such as neurodegenerative diseases and cancer. However, the development of bioinformatics infrastructures and computational tools has fallen seriously behind the technological advances. The lack of proper computational approaches presents current data analysis barriers that signiﬁcantly hinder biological investigations. The overarching goal of this proposal is to address some of the most pressing ana- lytic challenges facing proﬁling and interpreting spatial transcriptomics data, including 1) lack of robust identiﬁcation of genes with spatial expression patterns across a variety of technical platforms, 2) lack of tools to identify structures, microenvironments as well as developmental trajectory on the tissue, and 3) lack of tools that can jointly analyze spatial transcriptomic data across multiple samples and multiple data sources. In the proposal, we will work on the following aims: Aim 1. Develop nonpara- metric tools for identifying genes with spatial expression patterns. Aim 2. Develop spatially aware dimension reduction tools for detecting structures and developmental trajectories on the tissue. Aim 3. Develop integrative association tools for spatial transcriptomic analysis across multiple samples and datasets. All the methods will be implemented in user-friendly software and disseminated to the sci- entiﬁc community. Successful achievement of all aims will dramatically increase the power of spatial transcriptomics analysis, and facilitate the application of these cutting-edge technologies to transla- tional and clinical studies.

项目摘要空间转录组学是一项开创性的新技术，可以测量基因在映射活动的地方时，组织样品中的潮流。它有促进的承诺我们对基本基本表型和疾病的基本空间异质性的理解，例如神经退行性疾病和癌症。但是，生物信息学基础设施的发展计算工具严重落后于技术进步。缺乏适当的计算方法目前的数据分析障碍显着阻碍生物学调查。该提案的总体目标是解决一些最紧迫的ana- 裂解和解释空间转录组学数据面临的裂解挑战，包括1）缺乏强大的在各种技术平台上识别具有空间表达模式的基因，2）缺乏识别结构，微环境以及组织上的发展轨迹的工具 3）缺乏可以共同分析多个样本中的空间转录组数据的工具多个数据源。在提案中，我们将致力于以下目的：目标1。用于识别具有空间表达模式的基因的公制工具。目标2。发展空间意识减少尺寸的工具，用于检测组织上的结构和发育轨迹。目标3。开发跨多个样本的空间转录组分析的综合关联工具，数据集。所有方法都将在用户友好的软件中实现，并将其传播到SCI- 界社区。成功实现所有目标将大大增加空间的力量转录组学分析，并促进这些尖端技术在翻译中的应用领域和临床研究。