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Truncation in Survival Analysis

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Updated: Jun 9, 2026

Using Microarrays to Interrogate Microenvironmental Impact on Cellular Phenotypes in Cancer
08:20

Using Microarrays to Interrogate Microenvironmental Impact on Cellular Phenotypes in Cancer

Published on: May 21, 2019

Incorporating Nonlinear Relationships in Microarray Missing Value Imputation.

Tianwei Yu1, Hesen Peng, Wei Sun

  • 1Department of Biostatistics and Bioinformatics, 1518 Clifton Rd., N.E., 3rd Floor, Rollins School of Public Health, Emory University, Atlanta, GA 30322, USA. tyu8@emory.edu

IEEE/ACM Transactions on Computational Biology and Bioinformatics
|August 25, 2010
PubMed
Summary
This summary is machine-generated.

Accurate imputation of missing gene expression data is crucial. This study introduces a novel method leveraging nonlinear gene dependencies, improving imputation accuracy and preserving statistical significance in microarray analysis.

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Last Updated: Jun 9, 2026

Using Microarrays to Interrogate Microenvironmental Impact on Cellular Phenotypes in Cancer
08:20

Using Microarrays to Interrogate Microenvironmental Impact on Cellular Phenotypes in Cancer

Published on: May 21, 2019

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Microarray gene expression data frequently have missing values, hindering downstream analyses.
  • Existing imputation methods often overlook nonlinear relationships between genes.
  • Accurate missing value estimation is vital for reliable biological interpretation.

Purpose of the Study:

  • To develop and evaluate an imputation method that utilizes nonlinear dependencies between genes.
  • To assess the impact of nonlinear imputation on the accuracy of missing value estimation.
  • To investigate the influence of data normalization on imputation performance.

Main Methods:

  • Proposed an imputation scheme exploiting nonlinear gene expression dependencies.
  • Conducted simulations using real microarray data to validate the method.
  • Evaluated imputation accuracy using normalized root-mean-squared error and preservation of significant genes.
  • Analyzed the effect of gene-wise mean removal normalization on simulation outcomes.

Main Results:

  • The proposed nonlinear imputation method significantly improved accuracy compared to methods relying solely on linear correlations.
  • Incorporating nonlinear relationships enhanced the preservation of significant genes in statistical testing.
  • Data normalization techniques, particularly gene-wise mean removal, can inflate the performance of correlation-based imputation methods.
  • The study identified potential biases in simulation results due to normalization-induced artificial dependencies.

Conclusions:

  • Leveraging nonlinear gene dependencies offers a more accurate approach for imputing missing values in microarray data.
  • Standard normalization procedures can mask the true performance of imputation methods, necessitating careful evaluation.
  • The developed imputation strategy provides a robust alternative for handling missing data in high-throughput gene expression studies.