Dimensionality reduction by UMAP to visualize physical and genetic interactions

Abstract

Dimensionality reduction is often used to visualize complex expression profilingdata. 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 clusters 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.A central goal of biological studies is the identification and characterization of proteins that act in a common cellular pathway. Efforts towards this goal have been greatly aided by large-scale perturbation analyses coupled with whole-transcriptome profiling, in which each gene's transcriptional response to a perturbation is measured. If a sufficient database of expression profiles exists, then a pathway affected by an uncharacterized

Biased instantaneous regional muscle activation maps: Embedded fuzzy topology and image feature analysis

“…On the other hand, an increased entropy shows a most irregular geometry (heterogeneity) due to increased local connectivity (McInnes et al, 2020;McInnes, 2018;Sánchez-Rico and Alvarado, 2019), which occurred with a small volume suggesting a lower distance between nodes (more similar RMS of MUAPs). Central parameters with higher entropy and lower volume were found near 50% overlap, while for window length, higher entropy and lower volume were found between 100 ms and 500 ms. A case of the total loss of connectivity was found for 1,000 ms, and 90% of overlap in coherence with findings of gene studies using UMAP (please, see Figure 2) (Dorrity et al, 2020).…”

How does the age of control individuals hinder the identification of target genes for Huntington’s disease?

“…For a comprehensive comparative assessment of the entire transcriptome between HD gene-positive individuals and all neurologically normal subjects, we employed the Uniform Manifold Approximation and Projection (UMAP) dimensionality reduction technique (McInnes et al, 2018), which proved itself to produce insightful reduced dimensions to represent genomic data (Dorrity et al, 2020). Utilizing three distinct components from UMAP, we subsequently applied the Density-based Spatial Clustering of Applications with Noise (DBSCAN) algorithm to unveil primary grouping patterns (Kriegel et al, 2011); the advantage of this approach is the fact that no prior knowledge on the ideal number of clusters is required; also, DBSCAN generates clusters based on density of data in a particular region and thus automatically recognizes anomalous or outlying observations.…”

Genotype likelihoods incorporated in non-linear dimensionality reduction techniques infer fine-scale population genetic structure

“…One such method is t-distributed stochastic neighbor embedding (t-SNE) (van der Maaten & Hinton, 2008), which has gained popularity in recent years due to its ability to capture local structures in genomics and transcriptomics data (Kiselev et al, 2019;Li et al, 2017;Platzer, 2013). Another algorithm is uniform manifold approximation and projection (UMAP) (McInnes, Healy, & Melville, 2018), which can also preserve non-linear structure in highdimensional data and has already been used to investigate patterns and relationships across different levels of dataset complexity and size (Becht et al, 2018;Diaz-Papkovich et al, 2019Dorrity et al, 2020). t-SNE analyzes the similarity of points in high-dimensional space using a Gaussian distance (van der Maaten & Hinton, 2008), whereas UMAP is based on generating a weighted graph where data points in close proximity to each other are given greater weights (McInnes, Healy, & Melville, 2018); thus, both algorithms preserve the local topology of the neighborhood.…”