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Performance evaluation of transcriptomics data normalization for survival risk prediction.

Ai Ni1, Li-Xuan Qin2

  • 1Ohio State University, New York, NY 10017 USA.

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|July 10, 2021
PubMed
Summary
This summary is machine-generated.

Handling effects in transcriptomics data significantly impact survival prediction. Median normalization and variance stabilizing normalization outperform quantile normalization for microRNA microarray survival outcome prediction.

Keywords:
data normalizationhandling effectsmicroRNA microarraypenalized Cox regressionsurvival predictiontranscriptomics data

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Area of Science:

  • Bioinformatics
  • Genomics
  • Biostatistics

Background:

  • Transcriptomics data analysis often encounters unwanted variations due to experimental handling effects.
  • Normalization methods are crucial for mitigating these effects in differential expression analysis.
  • Their performance in survival outcome prediction, a key biomedical research goal, remains under-evaluated.

Purpose of the Study:

  • To evaluate the performance of different normalization methods in survival outcome prediction using transcriptomics data.
  • To assess the impact of handling effects on survival prediction accuracy.
  • To develop and utilize a benchmarking tool for this evaluation.

Main Methods:

  • Utilized paired datasets with and without handling effects for microRNA microarrays.
  • Developed a benchmarking tool to evaluate normalization methods in survival prediction.
  • Assessed quantile, median, and variance stabilizing normalization using various modeling approaches.

Main Results:

  • Handling effects significantly impact survival prediction accuracy.
  • Quantile normalization, widely used, underperformed median and variance stabilizing normalization.
  • Median normalization demonstrated potential for improving survival predictor development.

Conclusions:

  • Normalization method performance is context-dependent on the downstream analysis, such as survival prediction.
  • Median normalization shows promise for enhancing the development of survival predictors from transcriptomics data.
  • A benchmarking tool is provided to facilitate further evaluation of normalization methods.