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Graphia: A platform for the graph-based visualisation and analysis of high dimensional data

Fig 4

Analysis of cell and gene associations in scRNA-Seq data.

The structure of scRNA-Seq data is commonly represented using approaches such as (A) t-SNE and (B) UMAP as shown here for immune cells derived from the Tabula Muris dataset. However, the distance between data points and groups of data points is difficult to interpret. (C) Graphia enables the construction of cell-to-cell networks built on a similarity parameter. Here, the 48 most significant PCA values for each cell were first calculated and this PCA profile used to construct a correlation network. The plot bottom left of C, shows the PCA profiles of cells in the two largest cell clusters. To better show graph structure, a k-NN (k = 10) transformation was applied and outlier cells removed (r < 0.85 and node degree < 10, nodes coloured white). The graph comprises of 12,498 nodes (cells) and 143k edges. Cell clusters have been annotated as the cell types defined by the authors. (D) Shows a gene correlation network generated from these data by first calculating the average expression of genes within cell clusters and then calculating a correlation matrix from these values. (E) Plots show the average expression profile (y-axis) of a selection of gene-clusters across the aggregated cell-clusters (x-axis). The label gives the cluster number, e.g., C1, the number of genes within the cluster (966) and the association of the genes with a given biology or cell type.

Fig 4