Statistical classification of multivariate flow cytometry data analyzed by manual gating: stem, progenitor, and epithelial marker expression in nonsmall cell lung cancer and normal lung.
Statistical classification of multivariate flow cytometry data analyzed by manual gating: stem, progenitor, and epithelial marker expression in nonsmall cell lung cancer and normal lung.
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DOI：
10.1002/cyto.a.22240
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发表时间：
2013-01
期刊：
影响因子：
3.7
通讯作者：
Donnenberg, Albert D.
中科院分区：
文献类型：
--
作者：
Normolle, Daniel P.;Donnenberg, Vera S.;Donnenberg, Albert D.
关键词：
在多维流式细胞术日益复杂的推动下，使用监督分类从初级流式细胞术数据中提取标记是一个新兴的领域，已经取得了重大进展。无论标记是在没有监督的情况下提取的，还是通过传统的门和区域方法提取的，确定的候选变量的数量通常大于样本的数量(p<；n)，并且许多变量高度相互关联。因此，跨组或不同处理之间的比较以确定哪些标记物是重要的是具有挑战性的。在这里，我们利用一个数据集，其中86个变量是通过对单个列表模式数据文件的常规手动分析创建的，并比较了五种多变量分类方法的应用，以区分35例非小细胞肺癌和邻近正常肺标本的干/祖细胞含量的细微差异。比较的方法包括弹性网法、套索法、随机森林法、对角线性判别分析法和最佳单变量法(BEST-1)。我们描述了一种广泛适用的方法，包括：(1)变量转换和标准化；(2)变量之间相关性的可视化和评估；(3)重要变量的选择和建模；以及(4)模型的质量和稳定性的表征。分析得到了两个验证结果(肿瘤是非整倍体，具有比正常肺更高的光散射特性)，以及需要跟进的线索：细胞角蛋白+CD133+祖细胞在正常肺中存在，但在肺癌中减少；二倍体(或假二倍体)CD117+CD44+细胞在肿瘤中更常见。我们预计，这里描述的方法将广泛适用于各种多维细胞学问题。
The use of supervised classification to extract markers from primary flow cytometry data is an emerging field that has made significant progress, spurred by the growing complexity of multidimensional flow cytometry. Whether the markers are extracted without supervision or by conventional gate and region methods, the number of candidate variables identified is typically larger than the number of specimens (p < n) and many variables are highly intercorrelated. Thus, comparison across groups or treatments to determine which markers are significant is challenging. Here, we utilized a data set in which 86 variables were created by conventional manual analysis of individual listmode data files, and compared the application of five multivariate classification methods to discern subtle differences between the stem/progenitor content of 35 non-small cell lung cancer and adjacent normal lung specimens. The methods compared include elastic-net, lasso, random forest, diagonal linear discriminant analysis, and best single variable (best-1). We described a broadly applicable methodology consisting of: (1) variable transformation and standardization; (2) visualization and assessment of correlation between variables; (3) selection of significant variables and modeling; and (4) characterization of the quality and stability of the model. The analysis yielded both validating results (tumors are aneuploid and have higher light scatter properties than normal lung), as well as leads that require followup: Cytokeratin+ CD133+ progenitors are present in normal lung but reduced in lung cancer; diploid (or pseudo-diploid) CD117+CD44+ cells are more prevalent in tumor. We anticipate that the methods described here will be broadly applicable to a variety of multidimensional cytometry problems.
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影响因子：
4.5
作者：
EFRON, B
通讯作者：
EFRON, B
DOI：
10.1056/nejmoa1101324
发表时间：
2011-05-12
期刊：
The New England journal of medicine
影响因子：
--
作者：
Kajstura J;Rota M;Hall SR;Hosoda T;D'Amario D;Sanada F;Zheng H;Ogórek B;Rondon-Clavo C;Ferreira-Martins J;Matsuda A;Arranto C;Goichberg P;Giordano G;Haley KJ;Bardelli S;Rayatzadeh H;Liu X;Quaini F;Liao R;Leri A;Perrella MA;Loscalzo J;Anversa P
通讯作者：
Anversa P
上皮组织中的茎/祖细胞含量的流式细胞术测定：非肺癌和正常肺的一个例子。
影响因子：
3.7
作者：
Donnenberg, Vera S.;Landreneau, Rodney J.;Pfeifer, Melanie E.;Donnenberg, Albert D.
通讯作者：
Donnenberg, Albert D.
DOI：
10.1073/pnas.0530291100
发表时间：
2003-04-01
影响因子：
11.1
作者：
Al-Hajj, M;Wicha, MS;Clarke, MF
通讯作者：
Clarke, MF
影响因子：
46.9
作者：
通讯作者：
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