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Related Experiment Video

Updated: Sep 10, 2025

Metabolic Labeling of Newly Transcribed RNA for High Resolution Gene Expression Profiling of RNA Synthesis, Processing and Decay in Cell Culture

Metabolic Labeling of Newly Transcribed RNA for High Resolution Gene Expression Profiling of RNA Synthesis, Processing and Decay in Cell Culture

Published on: August 8, 2013

Exposure-inducible genes may contribute to missingness in RNAseq-based gene expression analyses.

Olga Y Gorlova¹, Ivan P Gorlov², R Taylor Ripley³

¹Section of Epidemiology and Population Science, Department of Medicine, Baylor College of Medicine, Houston, TX, 77030, USA.

Scientific Reports

|August 22, 2025

Summary

Missing gene expression values in RNAseq data are common. This study reveals that borderline expression and true biological missingness (TBM) contribute to missing data, cautioning against indiscriminate imputation.

Keywords:

Environmental exposure Gene expression Missing values RNAseq

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Last Updated: Sep 10, 2025

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Published on: January 22, 2018

Area of Science:

Genomics
Bioinformatics
Cancer Research

Background:

Missing gene expression values are a frequent challenge in RNA sequencing (RNAseq) analyses.
Factors influencing data missingness in RNAseq, particularly in lung adenocarcinoma, remain underexplored.

Purpose of the Study:

To identify genetic and environmental factors contributing to missingness in RNAseq data.
To investigate the complex relationship between gene expression levels and data missingness.

Main Methods:

Analysis of RNAseq data from 66 lung adenocarcinoma tumors and adjacent normal tissues.
Comparative analysis of missingness in smokers versus nonsmokers.
Examination of tobacco smoke-sensitive genes and gene enrichment analysis.

Main Results:

A strong negative association between gene expression level and missingness was observed, indicating borderline expression as a key contributor.
A complex relationship emerged, with high mean expression observed in genes with very high missingness rates.
Evidence supports the hypothesis of true biological missingness (TBM) due to inter-individual gene expression variation, particularly in smokers.

Conclusions:

True biological missingness (TBM) is a significant factor in RNAseq data, alongside borderline expression.
Identifying TBM is crucial to prevent imputation bias, as imputing values for non-expressed genes can skew results.
Caution is advised against indiscriminate imputation of missing RNAseq values, advocating for separate analysis of TBM genes.