We developed the package scMetric to apply ITML algorithm on gene expression data. The package let users to assign a few pairs of cells that are similar with each other and assign a few pairs of dissimilar cells. With this weak training information, scMetric learns the metric A to best preserve the similarity and dissimilarity reflected in the training pairs. It then employs t-SNE to visualize the data using this metric. The package also outputs the t-SNE map using the conventional Euclidean distance metric. The learned metric A as well as the key genes that compose most weights in the metric can also be output to other analysis methods that need a distance or similarity metric.
|Maintainer||Wenchang Chen <firstname.lastname@example.org>|
|Package repository||View on GitHub|
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