Robust prediction of clinical outcomes using cytometry data.
Robust prediction of clinical outcomes using cytometry data.
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DOI：
10.1093/bioinformatics/bty768
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发表时间：
2019-04-01
期刊：
影响因子：
--
通讯作者：
Butte AJ
中科院分区：
文献类型：
--
作者：
Hu Z;Glicksberg BS;Butte AJ
流式细胞术和质谱细胞术被广泛用于诊断疾病和预测临床结果。当将临床特征与细胞计数数据相关联时，传统的分析方法需要细胞门控作为中间步骤，导致信息丢失和对批次效应的敏感性。在这里，我们希望探索一种替代方法，从细胞计数数据预测临床特征，而无需细胞门控步骤。我们还希望测试这种无门控方法是否提高了预测的准确性和鲁棒性。我们提出了一种新的策略（CytoDx），使用细胞计数数据预测临床结果，而无需细胞门控。将CytoDx应用于真实世界的数据集，使我们能够预测多种类型的临床特征。特别是，CytoDx能够使用高度异质的数据集预测对流感疫苗的反应，这表明它不仅准确，而且对批次效应和细胞仪平台具有鲁棒性。CytoDx作为R包在Bioconductor上提供（bioconductor. org/packages/CytoDx）。用于再现结果的数据和脚本可在www.example.com上获得。 补充数据可在Bioinformatics在线获得。
Flow cytometry and mass cytometry are widely used to diagnose diseases and to predict clinical outcomes. When associating clinical features with cytometry data, traditional analysis methods require cell gating as an intermediate step, leading to information loss and susceptibility to batch effects. Here, we wish to explore an alternative approach that predicts clinical features from cytometry data without the cell-gating step. We also wish to test if such a gating-free approach increases the accuracy and robustness of the prediction. We propose a novel strategy (CytoDx) to predict clinical outcomes using cytometry data without cell gating. Applying CytoDx on real-world datasets allow us to predict multiple types of clinical features. In particular, CytoDx is able to predict the response to influenza vaccine using highly heterogeneous datasets, demonstrating that it is not only accurate but also robust to batch effects and cytometry platforms. CytoDx is available as an R package on Bioconductor (bioconductor.org/packages/CytoDx). Data and scripts for reproducing the results are available on bitbucket.org/zichenghu_ucsf/cytodx_study_code/downloads. Supplementary data are available at Bioinformatics online.
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DOI：
10.1002/cyto.a.22732
发表时间：
2016-01
期刊：
Cytometry. Part A : the journal of the International Society for Analytical Cytology
影响因子：
--
作者：
Aghaeepour N;Chattopadhyay P;Chikina M;Dhaene T;Van Gassen S;Kursa M;Lambrecht BN;Malek M;McLachlan GJ;Qian Y;Qiu P;Saeys Y;Stanton R;Tong D;Vens C;Walkowiak S;Wang K;Finak G;Gottardo R;Mosmann T;Nolan GP;Scheuermann RH;Brinkman RR
通讯作者：
Brinkman RR
影响因子：
64.5
作者：
Tsang JS;Schwartzberg PL;Kotliarov Y;Biancotto A;Xie Z;Germain RN;Wang E;Olnes MJ;Narayanan M;Golding H;Moir S;Dickler HB;Perl S;Cheung F;Baylor HIPC Center;CHI Consortium
通讯作者：
CHI Consortium
影响因子：
9.8
作者：
Bhattacharya S;Dunn P;Thomas CG;Smith B;Schaefer H;Chen J;Hu Z;Zalocusky KA;Shankar RD;Shen-Orr SS;Thomson E;Wiser J;Butte AJ
通讯作者：
Butte AJ
影响因子：
64.5
作者：
Brodin P;Jojic V;Gao T;Bhattacharya S;Angel CJ;Furman D;Shen-Orr S;Dekker CL;Swan GE;Butte AJ;Maecker HT;Davis MM
通讯作者：
Davis MM
DOI：
10.1158/1078-0432.ccr-15-2412
发表时间：
2016-06-15
期刊：
Clinical cancer research : an official journal of the American Association for Cancer Research
影响因子：
--
作者：
Martens A;Wistuba-Hamprecht K;Geukes Foppen M;Yuan J;Postow MA;Wong P;Romano E;Khammari A;Dreno B;Capone M;Ascierto PA;Di Giacomo AM;Maio M;Schilling B;Sucker A;Schadendorf D;Hassel JC;Eigentler TK;Martus P;Wolchok JD;Blank C;Pawelec G;Garbe C;Weide B
通讯作者：
Weide B