ArticlePLoS computational biology2022
Improved visualization of high-dimensional data using the distance-of-distance transformation.
Article in PLoS computational biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Bioinformatic characterization and automated detection of metabolically activated monocyte subpopulations in dyslipidemia.Scientific reports · 2026Article
- Representations of facial features and surface quality in monkey area TE compared to neural network models.Frontiers in neural circuits · 2026Article
- A cell and transcriptome atlas of human arterial vasculature.Cell genomics · 2025Article
- Explainable semi-supervised model for predicting invasion depth of esophageal squamous cell carcinoma based on the IPCL and AVA patterns.Scientific reports · 2025Article
- Development of a prognostic model for osteosarcoma based on macrophage polarization-related genes using machine learning: implications for personalized therapy.Clinical and experimental medicine · 2025Article
- A Semi-Supervised Learning Framework for Classifying Colorectal Neoplasia Based on the NICE Classification.Journal of imaging informatics in medicine · 2024Article
- Prognositic value of anoikis and tumor immune microenvironment-related gene in the treatment of osteosarcoma.Zhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences · 2024Article
- Detection of PatIent-Level distances from single cell genomics and pathomics data with Optimal Transport (PILOT).Molecular systems biology · 2024Article
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Authors and funding
2 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Dimensionality reduction tools like t-SNE and UMAP are widely used for high-dimensional data analysis. For instance, these tools are applied in biology to describe spiking patterns of neuronal populations or the genetic profiles of different cell types. Here, we show that when data include noise points that are randomly scattered within a high-dimensional space, a "scattering noise problem" occurs in the low-dimensional embedding where noise points overlap with the cluster points. We show that a simple transformation of the original distance matrix by computing a distance between neighbor distances alleviates this problem and identifies the noise points as a separate cluster. We apply this technique to high-dimensional neuronal spike sequences, as well as the representations of natural images by convolutional neural network units, and find an improvement in the constructed low-dimensional embedding. Thus, we present an improved dimensionality reduction technique for high-dimensional data containing noise points.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.