CytoGLMM: conditional differential analysis for flow and mass cytometry experiments.
CytoGLMM: conditional differential analysis for flow and mass cytometry experiments.
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DOI：
10.1186/s12859-021-04067-x
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发表时间：
2021-03-22
影响因子：
3
通讯作者：
Holmes S
中科院分区：
文献类型：
--
作者：
Seiler C;Ferreira AM;Kronstad LM;Simpson LJ;Le Gars M;Vendrame E;Blish CA;Holmes S
流式细胞仪和质谱仪是测量单个细胞上多种蛋白质表达水平的重要现代免疫学工具。目的是通过研究蛋白质的差异表达，更好地了解单细胞反应的机制。大多数当前的数据分析工具比较了许多计算发现的细胞类型的表达。我们的目标是专注于一种细胞类型。我们更窄的应用领域使我们能够定义更具体的统计模型，更容易控制统计保证。由于标记物相关性和受试者间异质性，标记物表达的差异分析可能是困难的，特别是对于人类免疫学的研究。我们用两种多元回归策略来应对这些挑战：自举广义线性模型和广义线性混合模型。在模拟数据集上，我们比较了两种策略对标记相关性和异质性的鲁棒性。对于配对实验，我们发现，这两种策略保持目标的错误发现率在中等相关性和正确的模型规范下，混合模型在统计上更强大。对于未配对的实验，我们的结果表明，需要更大的患者样本量来检测差异。我们在妊娠数据集上说明了CytoGLMM R包和两种策略的工作流程。我们在流式细胞术和质谱数据中发现差异蛋白的方法减少了标记相关性产生的偏倚，并防止了患者异质性引起的错误发现。
Flow and mass cytometry are important modern immunology tools for measuring expression levels of multiple proteins on single cells. The goal is to better understand the mechanisms of responses on a single cell basis by studying differential expression of proteins. Most current data analysis tools compare expressions across many computationally discovered cell types. Our goal is to focus on just one cell type. Our narrower field of application allows us to define a more specific statistical model with easier to control statistical guarantees. Differential analysis of marker expressions can be difficult due to marker correlations and inter-subject heterogeneity, particularly for studies of human immunology. We address these challenges with two multiple regression strategies: a bootstrapped generalized linear model and a generalized linear mixed model. On simulated datasets, we compare the robustness towards marker correlations and heterogeneity of both strategies. For paired experiments, we find that both strategies maintain the target false discovery rate under medium correlations and that mixed models are statistically more powerful under the correct model specification. For unpaired experiments, our results indicate that much larger patient sample sizes are required to detect differences. We illustrate the CytoGLMM R package and workflow for both strategies on a pregnancy dataset. Our approach to finding differential proteins in flow and mass cytometry data reduces biases arising from marker correlations and safeguards against false discoveries induced by patient heterogeneity.
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影响因子：
4.3
作者：
Finak G;Frelinger J;Jiang W;Newell EW;Ramey J;Davis MM;Kalams SA;De Rosa SC;Gottardo R
通讯作者：
Gottardo R
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：
10.1126/science.1198704
发表时间：
2011-05-06
期刊：
Science (New York, N.Y.)
影响因子：
--
作者：
Bendall SC;Simonds EF;Qiu P;Amir el-AD;Krutzik PO;Finck R;Bruggner RV;Melamed R;Trejo A;Ornatsky OI;Balderas RS;Plevritis SK;Sachs K;Pe'er D;Tanner SD;Nolan GP
通讯作者：
Nolan GP
影响因子：
3.7
作者：
Finck, Rachel;Simonds, Erin F.;Jager, Astraea;Krishnaswamy, Smita;Sachs, Karen;Fantl, Wendy;Pe'er, Dana;Nolan, Garry P.;Bendall, Sean C.
通讯作者：
Bendall, Sean C.
DOI：
10.1111/j.2517-6161.1995.tb02031.x
发表时间：
1995-01-01
影响因子：
5.8
作者：
BENJAMINI, Y;HOCHBERG, Y
通讯作者：
HOCHBERG, Y