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Published on: September 18, 2021
Probability fold change: a robust computational approach for identifying differentially expressed gene lists.
Xutao Deng1, Jun Xu, James Hui
1Transcriptional Genomics Core, Cedars-Sinai Medical Center, David Geffen School of Medicine at UCLA, Los Angeles, CA 90048, USA. dengx@ucla.edu
Computer Methods and Programs in Biomedicine
|October 10, 2008
Summary
A new Probabilistic Fold Change (PFC) algorithm improves gene ranking in microarray analysis. PFC offers superior reproducibility and accuracy compared to other methods, enhancing biological interpretation.
Area of Science:
- Bioinformatics
- Computational Biology
- Genomics
Background:
- Differential gene expression analysis is crucial for microarray studies.
- Existing gene ranking methods yield inconsistent results, impacting biological interpretation.
- Improved ranking algorithms are essential for accurate microarray data analysis.
Purpose of the Study:
- To introduce and evaluate a novel algorithm, Probabilistic Fold Change (PFC), for ranking differentially expressed genes.
- To compare the performance of PFC against six established ranking methods using benchmark datasets.
- To assess the relationship between statistical accuracy and biological reproducibility in gene ranking.
Main Methods:
- Developed the Probabilistic Fold Change (PFC) algorithm, ranking genes by fold change confidence intervals.
- Utilized MicroArray Quality Control (MAQC) datasets for extensive testing and validation.
- Corroborated findings with qRT-PCR and Latin square spike-in datasets.
- Compared PFC against Mean Fold Change (FC), SAM, t-statistic (T), Bayesian-t (BAYT), Intensity-Conditional Fold Change (CFC), and Rank Product (RP).
Main Results:
- PFC consistently demonstrated high accuracy and reproducibility among the tested algorithms.
- Other popular ranking algorithms exhibited weaknesses in certain scenarios.
- Statistical accuracy did not directly correlate with biological reproducibility.
- Both statistical and biological quality aspects require evaluation for robust gene ranking.
Conclusions:
- The Probabilistic Fold Change (PFC) algorithm offers a reliable and accurate method for gene ranking in microarray analysis.
- PFC outperforms several widely used methods in terms of reproducibility and accuracy.
- Evaluating both statistical accuracy and biological reproducibility is critical for reliable microarray data interpretation.
