School of Mathematics, Southeast University, Nanjing, 210096, People's Republic of China.
Department of Mathematics, Nanjing Forestry University, Nanjing, 210037, People's Republic of China.
BMC Bioinformatics. 2018 Dec 17;19(1):512. doi: 10.1186/s12859-018-2495-5.
For analyzing these gene expression data sets under different samples, clustering and visualizing samples and genes are important methods. However, it is difficult to integrate clustering and visualizing techniques when the similarities of samples and genes are defined by PCC(Person correlation coefficient) measure.
Here, for rare samples of gene expression data sets, we use MG-PCC (mini-groups that are defined by PCC) algorithm to divide them into mini-groups, and use t-SNE-SSP maps to display these mini-groups, where the idea of MG-PCC algorithm is that the nearest neighbors should be in the same mini-groups, t-SNE-SSP map is selected from a series of t-SNE(t-statistic Stochastic Neighbor Embedding) maps of standardized samples, and these t-SNE maps have different perplexity parameter. Moreover, for PCC clusters of mass genes, they are displayed by t-SNE-SGI map, where t-SNE-SGI map is selected from a series of t-SNE maps of standardized genes, and these t-SNE maps have different initialization dimensions. Here, t-SNE-SSP and t-SNE-SGI maps are selected by A-value, where A-value is modeled from areas of clustering projections, and t-SNE-SSP and t-SNE-SGI maps are such t-SNE map that has the smallest A-value.
From the analysis of cancer gene expression data sets, we demonstrate that MG-PCC algorithm is able to put tumor and normal samples into their respective mini-groups, and t-SNE-SSP(or t-SNE-SGI) maps are able to display the relationships between mini-groups(or PCC clusters) clearly. Furthermore, t-SNE-SS(m)(or t-SNE-SG(n)) maps are able to construct independent tree diagrams of the nearest sample(or gene) neighbors, where each tree diagram is corresponding to a mini-group of samples(or genes).
为了分析不同样本下的这些基因表达数据集，聚类和可视化样本和基因是重要的方法。然而，当使用 PCC（Person 相关系数）度量来定义样本和基因的相似性时，很难整合聚类和可视化技术。
对于基因表达数据集的稀有样本，我们使用 MG-PCC（通过 PCC 定义的小组）算法将它们划分为小组，并使用 t-SNE-SSP 图谱来显示这些小组，其中 MG-PCC 算法的思想是最近的邻居应该在同一个小组中，t-SNE-SSP 图谱是从一系列标准化样本的 t-SNE（t 统计随机邻居嵌入）图谱中选择的，并且这些 t-SNE 图谱具有不同的困惑度参数。此外，对于大量基因的 PCC 聚类，它们通过 t-SNE-SGI 图谱显示，t-SNE-SGI 图谱是从一系列标准化基因的 t-SNE 图谱中选择的，并且这些 t-SNE 图谱具有不同的初始化维度。在这里，t-SNE-SSP 和 t-SNE-SGI 图谱是通过 A 值选择的，A 值是从聚类投影区域建模的，t-SNE-SSP 和 t-SNE-SGI 图谱是具有最小 A 值的 t-SNE 图谱。
从癌症基因表达数据集的分析中，我们证明了 MG-PCC 算法能够将肿瘤和正常样本放入它们各自的小组中，t-SNE-SSP（或 t-SNE-SGI）图谱能够清晰地显示小组（或 PCC 聚类）之间的关系。此外，t-SNE-SS(m)（或 t-SNE-SG(n)）图谱能够构建样本（或基因）最近邻居的独立树图，每个树图对应于一个样本小组。
