New directions in single cell genomics method development

单细胞基因组学方法开发的新方向

基本信息

  • 批准号:
    10732646
  • 负责人:
  • 金额:
    $ 35.21万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
  • 财政年份:
    2017
  • 资助国家:
    美国
  • 起止时间:
    2017-08-01 至 2027-06-30
  • 项目状态:
    未结题

项目摘要

PROJECT SUMMARY Single cell technologies, in particular single cell transcriptomics, have been applied to numerous areas in biological and biomedical research and become a powerful tool for complex tissue characterization. Despite its ever-growing throughput and complexity, the development of analytical tools for single cell genomics has fallen behind the technological advances. The overarching goal of this proposal is to address some of the most pressing analytic challenges facing profiling and interpreting single cell genomics data, including: 1) lack of differential expression analysis methods that properly account for within-sample cellular heterogeneity; 2) lack of cis-regulatory inference methods that leverage multi-omics data; and 3) lack of proper methods to perform eQTL mapping in population-scale scRNA-seq studies. In the proposal, we will work on the following aims: Aim 1. Develop a differential expression analysis framework that better resolves sample heterogeneity and combats false discoveries for single cell data. Aim 2. Develop Bayesian model selection methods that infer cis- regulatory relationships from multi-omics data. Aim 3. Develop eQTL mapping methods that accommodate multiple cell types and experimental conditions in population-scale scRNA-seq studies. All methods will be implemented in user-friendly software and disseminated to the scientific community. Successful achievement of Aims 1 and 2 will dramatically increase the power of routine single cell genomics analysis, facilitating the application of these cutting-edge technologies to translational and clinical studies. Successful achievement of Aim 3 will provide new ways to comprehensively characterize the genetic architecture underlying gene expression that is specific to both cell-type and experimental-condition, ultimately facilitating the understanding of common diseases and disease-related complex traits.
项目总结

项目成果

期刊论文数量(5)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Quantification of extracellular proteins, protein complexes and mRNAs in single cells by proximity sequencing
  • DOI:
    10.1038/s41592-022-01684-z
  • 发表时间:
    2022-12-01
  • 期刊:
  • 影响因子:
    48
  • 作者:
    Vistain,Luke;Phan,Hoang Van;Tay,Savas
  • 通讯作者:
    Tay,Savas
Alignment of single-cell RNA-seq samples without overcorrection using kernel density matching.
  • DOI:
    10.1101/gr.261115.120
  • 发表时间:
    2021-04
  • 期刊:
  • 影响因子:
    7
  • 作者:
    Chen M;Zhan Q;Mu Z;Wang L;Zheng Z;Miao J;Zhu P;Li YI
  • 通讯作者:
    Li YI
Controlling for Confounding Effects in Single Cell RNA Sequencing Studies Using both Control and Target Genes.
  • DOI:
    10.1038/s41598-017-13665-w
  • 发表时间:
    2017-10-19
  • 期刊:
  • 影响因子:
    4.6
  • 作者:
    Chen M;Zhou X
  • 通讯作者:
    Zhou X
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Mengjie Chen其他文献

Mengjie Chen的其他文献

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{{ truncateString('Mengjie Chen', 18)}}的其他基金

Develop new bioinformatics infrastructures and computational tools for epitranscriptomics data
为表观转录组数据开发新的生物信息学基础设施和计算工具
  • 批准号:
    10633591
  • 财政年份:
    2023
  • 资助金额:
    $ 35.21万
  • 项目类别:
Developing new computational tools for spatial transcriptomics data
开发空间转录组数据的新计算工具
  • 批准号:
    10278763
  • 财政年份:
    2021
  • 资助金额:
    $ 35.21万
  • 项目类别:
Developing new computational tools for spatial transcriptomics data
开发空间转录组数据的新计算工具
  • 批准号:
    10654027
  • 财政年份:
    2021
  • 资助金额:
    $ 35.21万
  • 项目类别:
Collaborative Research: Advanced statistical methods for single cell RNA sequencing studies
合作研究:单细胞 RNA 测序研究的先进统计方法
  • 批准号:
    10155503
  • 财政年份:
    2017
  • 资助金额:
    $ 35.21万
  • 项目类别:

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