Evaluation and Development of Statistical Methods for Data Harmonization in Molecular Prognostication
Evaluation and Development of Statistical Methods for Data Harmonization in Molecular Prognostication
批准号:
10303963
负责人:
Li-Xuan Qin
金额:
$49.63万
依托单位国家:
美国
项目类别:
财政年份:
2021
资助国家:
美国
项目状态:
已结题
起止时间:
2021-09-03 至 2024-08-31
关键词:
AddressAdoptedAlgorithmsAwarenessBenchmarkingBiographyBiologicalCharacteristicsClassificationClinicalCommunitiesDataData AnalysesData SetDerivation procedureDetectionDevelopmentDiseaseEvaluationFoundationsFundingGene ExpressionGenesGoalsHumanHybridsKnowledgeLettersMalignant Female Reproductive System NeoplasmMalignant neoplasm of ovaryMethodsMicroRNAsModelingMolecularMorphologic artifactsOutcomeOvarianPatientsPerformancePhysiciansPlayPrincipal InvestigatorPrognosisPropertyPublishingRNAReproducibilityResearchResearch DesignRoleSamplingScientistSecureSignal TransductionSmall RNAStatistical MethodsStratificationSurvival AnalysisThe Cancer Genome AtlasTranslationsUnited States National Institutes of HealthUntranslated RNAanticancer researchbasecBioPortalcancer genomicscomputerized toolsdata harmonizationdesigndifferential expressionexperiencegenomic datahigh dimensionalityinnovationinsightmultidimensional datanoveloff-label useopen sourceoptimismoutcome predictionprognosticsimulationsurvival predictiontooltranscriptomicstreatment responsevirtual
中文摘要
项目总结
生存分析在生物医学转录学研究中发挥着基础性作用,以开发可靠的
患者预后和治疗反应的预测因子。虽然生存分析方法可以用来
针对高维和信号稀疏的问题,目前对数据问题的研究还比较缺乏
与不同的实验处理相关的人工制品,这是转录组数据的关键特征。
已发表的研究通常通过借用为区分而开发的方法来处理人工制品
表达式分析,其中最流行的是微阵列数据的分位数归一化和缩放
对测序数据进行标准化。尽管人们对这种“标签外”的使用持毫无根据的乐观态度,但我们发现
归一化可能会扭曲标记在样本中的排序,并随后危及对
结果相关标记物和结果预测的准确性。因此,迫切需要重新--
评估处理这些数据构件的现有方法,并专门为派生定制新的方法
分子预报器,以便它可以准确和重复性地进行。在这份提案中,我们将首先填充
MicroRNAs(一类对基因起重要调节作用的小RNA)的知识缺口
人类的表达)使用真实分布和稳健基准的数据。到时候我们会的
利用生存回归框架,开发管理处理构件的新方法。我们会
使用模拟工具与现有方法进行比较,评估新方法的性能
通过对癌症基因组图谱中卵巢癌数据的应用,展示它们的用途。我们的项目
预计将促进优化microRNA数据中的数据协调所需的知识,从而
加速它们的可重复性转化为临床有用的预测指标,并为进一步研究铺平道路
RNA数据及其翻译中的这些问题。
英文摘要
PROJECT SUMMARY
Survival analysis plays a foundational role in biomedical transcriptomics studies for developing reliable
predictors of patient prognosis and treatment response. While survival analysis methods are available to
address the issues of high dimensionality and signal sparsity, research is still lacking on the issue of data
artifacts associated with disparate experimental handling, which is a pivotal feature of transcriptomics data.
Published studies often deal with handling artifacts by borrowing methods that were developed for differential
expression analysis, the most popular of which is quantile normalization for microarray data and scaling
normalization for sequencing data. Despite the unfounded optimism for such ‘off-label’ uses, we found that
normalization may distort a marker’s ordering across samples and subsequently compromise the detection of
outcome-associated markers and the accuracy of outcome prediction. Thus, there is a pressing need to re-
evaluate existing methods for dealing with these data artifacts and tailor new ones specifically for the derivation
of molecular prognosticators so that it can be done accurately and reproducibly. In this proposal, we will first fill
the knowledge gap for microRNAs (a class of small RNAs that play an important regulatory role of gene
expression in humans) using data that are realistically distributed and robustly benchmarked. We will then
develop new methods for managing handling artifacts, leveraging the survival regression framework. We will
assess the performance of the new methods in comparison with existing methods using simulation tools and
demonstrate their use with an application to ovarian cancer data from The Cancer Genome Atlas. Our project
is expected to advance the knowledge needed for optimizing data harmonization in microRNA data and thus
accelerating their reproducible translations to clinically useful predictors and for paving the way to press on
these issues in RNA data and their translations.
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DOI:
10.1093/nar/gkac064
发表时间:
2022-06-10
期刊:
NUCLEIC ACIDS RESEARCH
影响因子:
14.9
作者:
[Dueren, Yannick, Lederer, Johannes, Qin, Li-Xuan]
通讯作者:
Qin, Li-Xuan
Making External Validation Valid for Molecular Classifier Development.
使外部验证对分子分类器的开发有效。
DOI:
10.1200/po.21.00103
发表时间:
2021
期刊:
JCO precision oncology
影响因子:
4.6
作者:
[Wu,Yilin, Huang,Huei-Chung, Qin,Li-Xuan]
通讯作者:
Qin,Li-Xuan
DOI:
10.1093/bib/bbab257
发表时间:
2021-11-05
期刊:
Briefings in bioinformatics
影响因子:
9.5
作者:
[Ni A, Qin LX]
通讯作者:
Qin LX
DOI:
10.3389/fgene.2022.838679
发表时间:
2022
期刊:
Frontiers in genetics
影响因子:
3.7
作者:
[]
通讯作者:
CF 2: Biostatistics and Bioinformatics Core
-
批准号:10932619
-
项目类别:
-
资助金额:$8.61万
-
财政年份:2023
-
负责人:Li-Xuan Qin
-
依托单位:
CF 2: Biostatistics and Bioinformatics Core
-
批准号:10247693
-
项目类别:
-
资助金额:$15.52万
-
财政年份:2018
-
负责人:Li-Xuan Qin
-
依托单位:
CF 2: Biostatistics and Bioinformatics Core
-
批准号:10016093
-
项目类别:
-
资助金额:$15.95万
-
财政年份:2018
-
负责人:Li-Xuan Qin
-
依托单位:
CF 2: Biostatistics and Bioinformatics Core
-
批准号:10468960
-
项目类别:
-
资助金额:$15.52万
-
财政年份:2018
-
负责人:Li-Xuan Qin
-
依托单位:
Statistical Methods for Normalizing Microarrays in Cancer Biomarker Studies
-
批准号:8231280
-
项目类别:
-
资助金额:$40.54万
-
财政年份:2011
-
负责人:Li-Xuan Qin
-
依托单位:
Statistical Methods for Normalizing Microarrays in Cancer Biomarker Studies
-
批准号:8453253
-
项目类别:
-
资助金额:$28.77万
-
财政年份:2011
-
负责人:Li-Xuan Qin
-
依托单位:
Statistical Methods for Normalizing Microarrays in Cancer Biomarker Studies
-
批准号:8052541
-
项目类别:
-
资助金额:$72.38万
-
财政年份:2011
-
负责人:Li-Xuan Qin
-
依托单位:
Biostatistics/Bioinformatics Core
-
批准号:7976120
-
项目类别:
-
资助金额:$9.38万
-
财政年份:2010
-
负责人:Li-Xuan Qin
-
依托单位:
Biostatistics/Bioinformatics Core
-
批准号:8379509
-
项目类别:
-
资助金额:$9.61万
-
财政年份:--
-
负责人:Li-Xuan Qin
-
依托单位:
Biostatistics/Bioinformatics Core
-
批准号:8712175
-
项目类别:
-
资助金额:$9.09万
-
财政年份:--
-
负责人:Li-Xuan Qin
-
依托单位:
CF 2: Biostatistics and Bioinformatics Core
-
批准号:9767087
-
项目类别:
-
资助金额:$15.92万
-
财政年份:--
-
负责人:Li-Xuan Qin
-
依托单位:
Biostatistics/Bioinformatics Core
-
批准号:8314127
-
项目类别:
-
资助金额:$9.11万
-
财政年份:--
-
负责人:Li-Xuan Qin
-
依托单位:
Biostatistics/Bioinformatics Core
-
批准号:8515349
-
项目类别:
-
资助金额:$9.01万
-
财政年份:--
-
负责人:Li-Xuan Qin
-
依托单位:
海外基金