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Updated: Sep 24, 2025

Transcriptome Analysis of Single Cells

Transcriptome Analysis of Single Cells

Published on: April 25, 2011

scESI: evolutionary sparse imputation for single-cell transcriptomes from nearest neighbor cells.

Qiaoming Liu¹, Ximei Luo^2,3, Jie Li¹

¹School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China.

Briefings in Bioinformatics

|May 5, 2022

Summary

This summary is machine-generated.

The evolutionary sparse imputation (ESI) algorithm addresses noise in single-cell RNA sequencing data by learning cell relationships. ESI improves data quality, cell type classification, and biological discovery.

Keywords:

imputation multiobjective evolutionary algorithm single-cell RNA-seq sparse representation

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Published on: April 25, 2011

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Single-cell RNA Sequencing of Fluorescently Labeled Mouse Neurons Using Manual Sorting and Double In Vitro Transcription with Absolute Counts Sequencing DIVA-Seq

Single-cell RNA Sequencing of Fluorescently Labeled Mouse Neurons Using Manual Sorting and Double In Vitro Transcription with Absolute Counts Sequencing DIVA-Seq

Published on: October 26, 2018

Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Single-cell RNA sequencing (scRNA-seq) data is prone to noise from the ubiquitous dropout problem, impacting gene expression profiles.
This noise hinders accurate downstream analyses such as cell type classification and trajectory inference.

Purpose of the Study:

To develop a novel algorithm, evolutionary sparse imputation (ESI), to address the dropout problem in scRNA-seq data.
To improve the quality and reliability of scRNA-seq data by reducing noise and imputing missing gene expression values.

Main Methods:

Constructed a sparse representation model for single-cell transcriptomes utilizing gene regulation relationships.
Designed an optimization framework based on nondominated sorting genetics to solve the sparse model.
Incorporated topological cell relationships and gene expression variability into an iterative global optimization search.

Main Results:

The ESI algorithm learned a Pareto optimal cell-cell affinity matrix, effectively modeling sparse relationships.
scESI demonstrated superior performance over benchmark methods on simulated datasets across various metrics.
Applied to real scRNA-seq data, scESI enhanced cell type classification, visualization, trajectory reconstruction, and differential gene expression analysis.

Conclusions:

ESI effectively reduces noise and improves data quality in scRNA-seq datasets.
The algorithm facilitates more accurate biological insights, including marker gene expression trend recovery, new cell type discovery, and identification of regulatory pathways.