Empirical validation of the S-Score algorithm in the analysis of gene expression data

doi:10.1186/1471-2105-7-154

BMC Bioinformatics

Volume 7

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Methodology article

Empirical validation of the S-Score algorithm in the analysis of gene expression data

Richard E Kennedy¹ , Kellie J Archer¹^,4 and Michael F Miles²^,3^,4

¹Department of Biostatistics, Virginia Commonwealth University, Richmond, VA 23298, USA

²Department of Pharmacology and Toxicology, Virginia Commonwealth University, Richmond, VA 23298, USA

³Department of Neurology, Virginia Commonwealth University, Richmond, VA 23298, USA

⁴Center for the Study of Biological Complexity, Virginia Commonwealth University, Richmond, VA 23298, USA

author email corresponding author email

BMC Bioinformatics 2006, 7:154doi:10.1186/1471-2105-7-154


Published:	17 March 2006

Additional 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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