Characterizing phenotype-associated subpopulations from single-cell sequencing data
从单细胞测序数据表征表型相关的亚群
基本信息
- 批准号:10658611
- 负责人:
- 金额:$ 30.8万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2023
- 资助国家:美国
- 起止时间:2023-06-01 至 2027-05-31
- 项目状态:未结题
- 来源:
- 关键词:AddressAutomobile DrivingBiologicalBiological ProcessBiotechnologyCell CountCell TherapyCellsCellular AssayClinicalClinical ResearchComplexDataData AnalysesData SetDetectionDevelopmentDiseaseDisease ProgressionGenesGenetic TranscriptionGraphIndividualLearningLinkMeasurementMethodologyMethodsModelingMolecularMutationNail plateNatureNeoplasm MetastasisPerformancePharmacotherapyPhenotypePopulationPopulation HeterogeneityPrognostic MarkerRationalizationSample SizeSamplingSeriesSignal TransductionSpeedThe Cancer Genome AtlasTimeTissue SampleTissuesWatercell typecellular targetingclinical phenotypeclinically significantdesignfeature selectionflexibilityhuman diseaseimprovedinnovationlearning strategyloss of functionmachine learning methodnovelnovel strategiespublic health relevancesimulationsingle cell analysissingle cell sequencingsingle cell technologysingle-cell RNA sequencingsupervised learningsurvival outcometheoriestherapy resistanttooltranscriptome sequencingtreatment response
项目摘要
Project Summary
Single-cell sequencing (scSeq) allows us to achieve new discoveries by distinguishing cell types, states, and
lineages from heterogeneous tissue microenvironments. However, it remains challenging to interpret complex
single-cell data from highly heterogeneous populations of cells. Currently, most existing single-cell data analyses
focus on cell type clusters defined by unsupervised clustering methods that cannot directly link cell clusters with
specific biological and clinical phenotypes. Additionally, the ever-increasing capability of scSeq in profiling
thousands to millions of cells brings more challenges of pinpointing which cell cluster for further analysis. Given
so many cells, the rationale of our "phenotype-centric" analysis is based on a Buddhist theory that "Each
individual can only drink one bottle of water from the entire river." Therefore, focusing on specific cell
subpopulations related to essential phenotypes is more important than evenly analyzing all cell clusters.
Furthermore, clinical phenotype information, such as treatment resistance, survival outcomes, cancer metastasis,
and disease stages, is primarily collected on bulk tissue samples. As a result, there is an unmet need to leverage
widely available clinical phenotype information to aid subpopulation identification from single-cell data.
Meanwhile, single-cell samples generated under different conditions require tools to identify the phenotype-
enriched subpopulations for each condition. Taken together, there is a great need for further methodological
progress for "phenotype-centric" scSeq data analysis. To this end, we propose to develop a suite of supervised-
learning-based novel methods to accurately identify the most highly phenotype-associated cell subpopulations
from scSeq data. We will (1) develop a platform with broad utilities for bulk phenotype-guided subpopulation
identification from scSeq; (2) build a novel strategy to learn high-confidence phenotype-enriched subpopulations
from scSeq data; (3) and establish a new platform for supervised phenotypic trajectory learning of subpopulations
from scSeq data. The proposed methods will be evaluated by rigorous simulations and real data analyses. This
proposal is conceptually innovative in the following aspects: (1) Our bulk phenotype-guided scSeq analysis
enables hypothesis-free identification of clinically and biologically relevant cell subpopulations from scSeq data;
(2) Our supervised learning frameworks can simultaneously select genes and identify phenotype-associated
subpopulations from scSeq data; (3) Our method to learn cell subpopulations associated with continuous
phenotypes has a unique feature to recover the hidden phenotypic stages. In summary, we expect this proposal
to deliver a suite of novel machine learning methods for "phenotype-centric" single-cell data analysis, thus
allowing us to precisely pinpoint disease-relevant subpopulations from single-cell data for cellular target
discovery.
项目总结
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Zheng Xia其他文献
Zheng Xia的其他文献
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{{ truncateString('Zheng Xia', 18)}}的其他基金
In Silico Screening of Alternative Polyadenylation Regulators in Cancers
癌症中替代多聚腺苷酸化调节剂的计算机筛选
- 批准号:
9924645 - 财政年份:2018
- 资助金额:
$ 30.8万 - 项目类别:
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