Abstract
BackgroundFor 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.ResultsHere, 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.ConclusionsFrom 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).
Highlights
For analyzing these gene expression data sets under different samples, clustering and visualizing samples and genes are important methods
For MG-Person correlation coefficient (PCC) and MGEuclidean, Table 2 showed that they correctly judged all samples of data-1 and data-3, and only a few samples of data-2, data-4 and data-5 were misjudged
Only 2 normal and 2 tumor samples of data-5 were misjudged by MG-PCC algorithm, where data-5 contained 60 normal and 60 tumor samples
Summary
For analyzing these gene expression data sets under different samples, clustering and visualizing samples and genes are important methods. To display classification of genes(or samples) in a meaningful way for exploration, presentation, and comprehension in diseased states and normal differentiation, many dimension reduction techniques were used to embed high-dimensional data for visualization in 2D(two dimensional) spaces [14,15,16,17], and had been successful in complementing clusters of Euclidean distance [14], such as Hierarchical clustering dendrograms, PCA(principal component analysis), t-SNE, heat maps, and network graphs [14,15,16,17,18]. For PCC clusters of gene expression data, many projection techniques gave them poor visualizations usually [16]. For PCC clusters of the original gene expression points, PCAF and t-SNE-MCP-O gave them poor visualizations [19, 20]
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