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Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

Tensor-Decomposition-Based Unsupervised Feature Extraction in Single-Cell Multiomics Data Analysis.

Y-H Taguchi¹, Turki Turki²

¹Department of Physics, Chuo University, Tokyo 112-8551, Japan.

|September 28, 2021

Summary

This summary is machine-generated.

We developed a tensor decomposition (TD) method for unsupervised feature extraction (FE) to integrate complex single-cell multiomics data. This approach effectively handles missing values and large dimensions, enabling accurate biological insights.

Keywords:

feature extraction multiomics data single-cell tensor decomposition

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Area of Science:

Computational Biology
Bioinformatics
Genomics

Background:

Single-cell multiomics data analysis presents challenges due to high dimensionality and missing values.
Integrating diverse omics data (gene expression, DNA methylation, accessibility) is crucial for comprehensive biological understanding.

Purpose of the Study:

To implement and evaluate a tensor decomposition (TD)-based unsupervised feature extraction (FE) technique for single-cell multiomics data integration.
To address the challenges of high dimensionality and missing data in multiomics datasets.

Main Methods:

Utilized a recently proposed tensor decomposition (TD)-based unsupervised feature extraction (FE) technique.
Integrated gene expression, DNA methylation, and accessibility data from single cells.
Employed UMAP for two-dimensional embedding of the integrated data.

Main Results:

The TD-based unsupervised FE technique successfully integrated three omics datasets without imputation of missing values.
Achieved two-dimensional embeddings that were coincident with classification, demonstrating effective data integration.
Identified genes associated with biological roles based on the feature extraction results.

Conclusions:

Tensor decomposition-based unsupervised feature extraction is a viable method for integrating challenging single-cell multiomics datasets.
This approach can handle large dimensions and missing data, facilitating robust biological discovery.
The method provides biologically relevant insights through feature selection and data visualization.