Interactive dual projections for gene expression analysis

  1. Ignacio Díaz 1
  2. José M. Enguita 1
  3. Diego García 1
  4. Ana González 1
  5. Abel A. Cuadrado 1
  6. María D. Chiara 2
  7. Nuria Valdés 3
  1. 1 Universidad de Oviedo
    info

    Universidad de Oviedo

    Oviedo, España

    ROR https://ror.org/006gksa02

  2. 2 Institute of Sanitary Research of the Principado de Asturias, Hospital Universitario Central de Asturias
  3. 3 Department of Internal Medicine, Section of Endocrinology and Nutrition, Hospital Universitario de Cabueñes
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Actas:
ESANN 2022 proceedings. European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning

Año de publicación: 2022

Congreso: European Symposium on Artificial Neural Networks, Computational Intelligence andMachine Learning (ESANN) (2022. Bruges, Belgium)

Tipo: Aportación congreso

DOI: 10.14428/ESANN/2022.ES2022-22 GOOGLE SCHOLAR

Resumen

We present an application of interactive dimensionality reduction(DR) for exploratory analysis of gene expression data that producestwo lively updated projections, a sample map and a gene map, by renderingintermediate results of a t-SNE. The user can condition the projections“on the fly” by subsets of genes or samples, so updated views reveal coexpressionpatterns for different cancer types or gene groups.

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Información de financiación

This work is part of Grant PID2020-115401GB-I00 funded by MCIN/AEI/ 10.13039/501100011033. The results shown here are based upon data generated by the TCGA Research Network: https://www.cancer.gov/tcga.

Financiadores

  • Ministerio de Ciencia e Innovación Spain
    • PID2020-115401GB-I00