Different sample similariy metrics and methods are applied to the dimension reduced expression module data and metadata, respectively.
Application of downstream analyses on aggregated data instead of single gene data was shown to increase representativeness and reduce noisiness.
Correlation network represents module (p.1) and metagene (p.2) data as graph with cells as nodes connected if their mutual correlation exceeds a given threshold (epsilon-neighborhood).
Correlation Spanning Tree
Correlation spanning tree represents module (p.1) and metagene (p.2) data as graph with cells as nodes connected to a spanning tree of maximal mutual correlation between connected nodes.
Supervised and clustered heatmaps
Heatmaps of module (p.1-2) and metagene (p.3-4) expression data with supervised and hierarchically clustered sample ordering.
Independent Component Analysis
Independent component analysis (ICA) distributes cells along axes of most variability similar to principal component analysis. However, restriction to othogonal axes is omitted in ICA.
ICA is applied to module (p.1-2) and metagene (p.3-4) expression data, where the first three components are shown in 3d and pairwise 2d scatterplots.
t-distributed stochastic neighbor embedding (t-SNE) is a nonlinear dimensionality reduction technique projecting cells into a two-dimensional coordinate system. It is applied to module (p.1) and metagene (p.2) data.
Transcriptome Analytics Browser - Tour (part 3 of 5)
Task II: Sample diversity analysis
Diversity analysis of the samples is performed using multiple complementary approaches such as correlation networks, hierarchical clustering heatmaps and component analyses. They are applied to detect molecularly distinct subgroups of the samples, to detect outlier samples and to visualize transcriptional diversity of the data set.
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