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Related Experiment Video

Updated: Jun 15, 2025

Isolation and Profiling of Human Primary Mesenteric Arterial Endothelial Cells at the Transcriptome Level

Isolation and Profiling of Human Primary Mesenteric Arterial Endothelial Cells at the Transcriptome Level

Published on: March 14, 2022

Denoiseit: denoising gene expression data using rank based isolation trees.

Jaemin Jeon¹, Youjeong Suk², Sang Cheol Kim³

¹Interdisciplinary Program in Bioinformatics, Seoul National University, Gwanak-gu, Seoul, 08826, Republic of Korea.

BMC Bioinformatics

|August 21, 2024

Summary

This summary is machine-generated.

DenoiseIt, a novel backward gene selection method, effectively removes outlier genes to reduce noise in gene expression data. This approach enhances biomarker discovery and improves downstream analysis quality for RNA sequencing studies.

Keywords:

Filtering Gene Matrix factorization Noise

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Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Gene selection is crucial for accurate gene expression analysis and biomarker identification.
Uninformative genes and noise can significantly impact downstream analysis results.

Purpose of the Study:

To introduce DenoiseIt, a backward gene selection method for noise reduction.
To enhance the robustness of gene sets for improved biomarker discovery and comparative gene expression analysis.

Main Methods:

DenoiseIt employs a backward search strategy, contrasting with conventional forward methods.
It utilizes non-negative matrix factorization and isolation forests to identify and remove outlier genes.

Main Results:

DenoiseIt successfully identified and removed genes with sample-specific expression anomalies.
The method reduced technical noise while retaining biologically relevant genes in bulk and single-cell RNA-seq data.
Performance was validated on TCGA and COVID-19 cohorts.

Conclusions:

DenoiseIt provides a robust approach to gene set curation for gene expression studies.
The software is publicly available, facilitating its application in research.