Dimensionality reduction by UMAP to visualize physical and genetic interactions

Abstract

Dimensionality reduction is often used to visualize complex expression profiling data. Here, we use the Uniform Manifold Approximation and Projection (UMAP) method on published transcript profiles of 1484 single gene deletions of Saccharomyces cerevisiae. Proximity in low-dimensional UMAP space identifies groups of genes that correspond to protein complexes and pathways, and finds novel protein interactions, even within well-characterized complexes. This approach is more sensitive than previous methods and should be broadly useful as additional transcriptome datasets become available for other organisms.

Brain mapping across 16 autism mouse models reveals a spectrum of functional connectivity subtypes

“…All statistical analyses and clustering were performed using Matlab (R2019a) and Python (Scikit-learn, UMAP) 69,70 . We first sought to reduce the complexity of the full connectome (165×165 ROIs resulting in 13530 edges) by considering only the strongest, positively-correlated edges.…”

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“…To visualize the relationship between unLUHMES and difLUHMES, dimensionality reduction was performed according to UMAP. [ 28 ] Missing values were imputed by extracting values randomly from a left‐shifted Gaussian distribution (shift of 1.8 and scale of 0.3) modeling the lower detection rate. [ 29 ] In the volcano plot, imputed data of all proteins detected in at least four out of five biological replicates in one group (unLUHMES and difLUHMES) were visualized.…”

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