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Cell Specific Gene Expression

Cell Specific Gene Expression

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Updated: Jan 5, 2026

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 Applied to Single-Cell Gene Expression Analysis.

Y-H Taguchi¹, Turki Turki²

¹Department of Physics, Chuo University, Tokyo, Japan.

Frontiers in Genetics

|October 15, 2019

Summary

This summary is machine-generated.

Tensor decomposition (TD)-based unsupervised feature extraction effectively integrates single-cell RNA sequencing data from human and mouse midbrain development, identifying key genes and transcription factors.

Keywords:

enrichment analysis inter-species analysis midbrain development single-cell RNA-sequencing tensor decomposition

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Published on: October 25, 2018

Area of Science:

Computational Biology
Genomics
Developmental Biology

Background:

Single-cell RNA sequencing (scRNA-seq) provides valuable gene expression data but requires robust methods for cell interpretation.
Unsupervised clustering techniques like t-SNE and UMAP are used for dimensionality reduction but are sensitive to gene selection.
Effective unsupervised gene selection is crucial for accurate analysis of scRNA-seq data.

Purpose of the Study:

To apply tensor decomposition (TD)-based unsupervised feature extraction (FE) for integrating human and mouse midbrain development scRNA-seq data.
To evaluate the performance of TD-based FE in selecting coincident and biologically reliable genes compared to other methods.
To identify potential regulatory transcription factors involved in midbrain development.

Main Methods:

Tensor decomposition (TD)-based unsupervised feature extraction (FE) was employed.
The method was applied to integrate scRNA-seq expression profiles from human and mouse midbrain development.
Performance was compared against principal component analysis (PCA)-based FE and other gene selection methods (highly variable genes, bimodal genes, dpFeature).

Main Results:

TD-based unsupervised FE successfully integrated cross-species scRNA-seq data, selecting coincident and biologically reliable genes.
The TD-based method demonstrated superior performance compared to PCA-based FE and other popular unsupervised gene selection techniques.
Ten transcription factors (TFs) potentially regulating midbrain development were identified, including BHLHE40, EGR1, and STAT3.

Conclusions:

TD-based unsupervised FE is a promising and effective method for integrating and analyzing scRNA-seq data across different species.
This approach enhances the identification of biologically relevant genes and potential regulatory networks in developmental processes.
The identified TFs offer insights into the molecular mechanisms governing midbrain development.