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Updated: Oct 4, 2025

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

Network inference with Granger causality ensembles on single-cell transcriptomics.

Atul Deshpande¹, Li-Fang Chu², Ron Stewart²

¹Department of Electrical and Computer Engineering, University of Wisconsin - Madison, Madison, WI 53706, USA; Morgridge Institute for Research, Madison, WI 53715, USA.

|February 9, 2022

Summary

This summary is machine-generated.

We developed SINGE, a new algorithm for inferring gene regulatory networks from single-cell gene expression data. SINGE effectively analyzes cell differentiation dynamics, outperforming existing methods in identifying regulator-gene interactions.

Keywords:

mouse embryonic stem cells network evaluation pseudotime time series analysis transcriptional regulation

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Transcriptome Analysis of Single Cells

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Last Updated: Oct 4, 2025

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Transcriptome Analysis of Single Cells

Transcriptome Analysis of Single Cells

Published on: April 25, 2011

Area of Science:

Computational Biology
Systems Biology
Genomics

Background:

Cellular gene expression dynamically changes during biological processes like differentiation.
Pseudotimes order cells by differentiation stage using gene expression, revealing regulator-gene interactions.
Non-uniform pseudotimes and missing data challenge standard analysis methods.

Purpose of the Study:

To present a novel algorithm, single-cell inference of networks using Granger ensembles (SINGE), for gene regulatory network inference.
To address challenges in analyzing ordered single-cell gene expression data with irregular pseudotimes and missing values.

Main Methods:

SINGE employs kernel-based Granger causality regression to handle irregular pseudotimes and impute missing expression values.
It aggregates predictions from an ensemble of regression analyses.
The method compiles a ranked list of candidate interactions between transcriptional regulators and target genes.

Main Results:

SINGE demonstrates superior performance compared to contemporary algorithms on two mouse embryonic stem cell differentiation datasets.
The algorithm successfully infers gene regulatory networks from ordered single-cell gene expression data.

Conclusions:

SINGE is an effective tool for gene regulatory network inference from single-cell data, particularly in dynamic processes.
Further examination revealed limitations concerning individual regulator performance and pseudotime informativeness, suggesting areas for future improvement.