Methodology article
Empirical validation of the S-Score algorithm in the analysis of gene expression data
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* Corresponding author: Richard E Kennedy rkennedy@vcu.edu
1 Department of Biostatistics, Virginia Commonwealth University, Richmond, VA 23298, USA
2 Department of Pharmacology and Toxicology, Virginia Commonwealth University, Richmond, VA 23298, USA
3 Department of Neurology, Virginia Commonwealth University, Richmond, VA 23298, USA
4 Center for the Study of Biological Complexity, Virginia Commonwealth University, Richmond, VA 23298, USA
BMC Bioinformatics 2006, 7:154 doi:10.1186/1471-2105-7-154
Published: 17 March 2006Additional files
Additional File 4:
Quantile-quantile plots of intensity data for the Dilution dataset.
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Additional File 5:
Linearity plots for the Latin Square dataset.
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Additional File 6:
Supplementary Tables 1-20.
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Additional File 1:
Comparison of S-Score and RMA. Plot of absolute value of S-Score vs absolute value of difference in RMA expression summaries, comparing the specified concentration to the baseline chip. X- and Y-axis projections are added to show separation of spike-in probes more clearly.
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Additional File 2:
Comparison of S-Score and dChip. Plot of absolute value of S-Score vs absolute value of difference in base 2 logarithm of dChip model-based expression index, comparing the specified concentration to the baseline chip. X- and Y-axis projections are added to show separation of spike-in probes more clearly.
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Additional File 3:
Comparison of S-Score and MAS5. Plot of absolute value of S-Score vs MAS5 p-values, comparing the specified concentration to the baseline chip. MAS5 p-values were transformed so that significantly up- and down-regulated genes will have p-values approaching 0. X- and Y-axis projections are added to show separation of spike-in probes more clearly.
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