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A first principles approach to differential expression in microarray data analysis.

Rubin RA - BMC Bioinformatics (2009)

Bottom Line: Here we take the approach of making the fewest assumptions about the structure of the microarray data.We applied the technique to the HGU-133A, HG-U95A, and "Golden Spike" spike-in data sets.The resulting receiver operating characteristic (ROC) curves compared favorably with other published results.

View Article: PubMed Central - HTML - PubMed

Affiliation: Mathematics Department, Whittier College, 13406 E. Philadelphia St., Whittier, CA 90608, USA. brubin698@earthlink.net

ABSTRACT

Background: The disparate results from the methods commonly used to determine differential expression in Affymetrix microarray experiments may well result from the wide variety of probe set and probe level models employed. Here we take the approach of making the fewest assumptions about the structure of the microarray data. Specifically, we only require that, under the hypothesis that a gene is not differentially expressed for specified conditions, for any probe position in the gene's probe set: a) the probe amplitudes are independent and identically distributed over the conditions, and b) the distributions of the replicated probe amplitudes are amenable to classical analysis of variance (ANOVA). Log-amplitudes that have been standardized within-chip meet these conditions well enough for our approach, which is to perform ANOVA across conditions for each probe position, and then take the median of the resulting (1 - p) values as a gene-level measure of differential expression.

Results: We applied the technique to the HGU-133A, HG-U95A, and "Golden Spike" spike-in data sets. The resulting receiver operating characteristic (ROC) curves compared favorably with other published results. This procedure is quite sensitive, so much so that it has revealed the presence of probe sets that might properly be called "unanticipated positives" rather than "false positives", because plots of these probe sets strongly suggest that they are differentially expressed.

Conclusion: The median ANOVA (1-p) approach presented here is a very simple methodology that does not depend on any specific probe level or probe models, and does not require any pre-processing other than within-chip standardization of probe level log amplitudes. Its performance is comparable to other published methods on the standard spike-in data sets, and has revealed the presence of new categories of probe sets that might properly be referred to as "unanticipated positives" and "unanticipated negatives" that need to be taken into account when using spiked-in data sets at "truthed" test beds.

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Related in: MedlinePlus

Median ANOVA (1-p), Median Signed ANOVA (1-p), RMA, PLM ROC curves for all HG-U95A d = 1 conditions. This chart shows the HG-U95A ROC curves for all comparisons in which experimental conditions increase by one (factor of 2) concentration step (plus those in the Latin square whose concentrations drop from 1024 to 0 pM). ROC curves for each of the median of the probe level ANOVA (1-p)'s (red), the median of the probe level signed ANOVA (1-p)'s (black) and affylmGUI's RMA (green) and PLM (cyan) were obtained after pooling the results of the analyses of all d = 1 conditions.
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Figure 9: Median ANOVA (1-p), Median Signed ANOVA (1-p), RMA, PLM ROC curves for all HG-U95A d = 1 conditions. This chart shows the HG-U95A ROC curves for all comparisons in which experimental conditions increase by one (factor of 2) concentration step (plus those in the Latin square whose concentrations drop from 1024 to 0 pM). ROC curves for each of the median of the probe level ANOVA (1-p)'s (red), the median of the probe level signed ANOVA (1-p)'s (black) and affylmGUI's RMA (green) and PLM (cyan) were obtained after pooling the results of the analyses of all d = 1 conditions.

Mentions: The ROC curves in Figure 9 compare the performance of median ANOVA (1-p), median signed ANOVA (1-p), RMA and PLM over the full range of false positive rates for all d = 1 conditions, obtained in the same manner as for the HG-U133A experiment. Figure 10 shows the initial portion of the ROC curves up to 450 false positives (roughly an average of 40 false positives per d = 1 comparison), the region of highest practical importance.


A first principles approach to differential expression in microarray data analysis.

Rubin RA - BMC Bioinformatics (2009)

Median ANOVA (1-p), Median Signed ANOVA (1-p), RMA, PLM ROC curves for all HG-U95A d = 1 conditions. This chart shows the HG-U95A ROC curves for all comparisons in which experimental conditions increase by one (factor of 2) concentration step (plus those in the Latin square whose concentrations drop from 1024 to 0 pM). ROC curves for each of the median of the probe level ANOVA (1-p)'s (red), the median of the probe level signed ANOVA (1-p)'s (black) and affylmGUI's RMA (green) and PLM (cyan) were obtained after pooling the results of the analyses of all d = 1 conditions.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2749840&req=5

Figure 9: Median ANOVA (1-p), Median Signed ANOVA (1-p), RMA, PLM ROC curves for all HG-U95A d = 1 conditions. This chart shows the HG-U95A ROC curves for all comparisons in which experimental conditions increase by one (factor of 2) concentration step (plus those in the Latin square whose concentrations drop from 1024 to 0 pM). ROC curves for each of the median of the probe level ANOVA (1-p)'s (red), the median of the probe level signed ANOVA (1-p)'s (black) and affylmGUI's RMA (green) and PLM (cyan) were obtained after pooling the results of the analyses of all d = 1 conditions.
Mentions: The ROC curves in Figure 9 compare the performance of median ANOVA (1-p), median signed ANOVA (1-p), RMA and PLM over the full range of false positive rates for all d = 1 conditions, obtained in the same manner as for the HG-U133A experiment. Figure 10 shows the initial portion of the ROC curves up to 450 false positives (roughly an average of 40 false positives per d = 1 comparison), the region of highest practical importance.

Bottom Line: Here we take the approach of making the fewest assumptions about the structure of the microarray data.We applied the technique to the HGU-133A, HG-U95A, and "Golden Spike" spike-in data sets.The resulting receiver operating characteristic (ROC) curves compared favorably with other published results.

View Article: PubMed Central - HTML - PubMed

Affiliation: Mathematics Department, Whittier College, 13406 E. Philadelphia St., Whittier, CA 90608, USA. brubin698@earthlink.net

ABSTRACT

Background: The disparate results from the methods commonly used to determine differential expression in Affymetrix microarray experiments may well result from the wide variety of probe set and probe level models employed. Here we take the approach of making the fewest assumptions about the structure of the microarray data. Specifically, we only require that, under the hypothesis that a gene is not differentially expressed for specified conditions, for any probe position in the gene's probe set: a) the probe amplitudes are independent and identically distributed over the conditions, and b) the distributions of the replicated probe amplitudes are amenable to classical analysis of variance (ANOVA). Log-amplitudes that have been standardized within-chip meet these conditions well enough for our approach, which is to perform ANOVA across conditions for each probe position, and then take the median of the resulting (1 - p) values as a gene-level measure of differential expression.

Results: We applied the technique to the HGU-133A, HG-U95A, and "Golden Spike" spike-in data sets. The resulting receiver operating characteristic (ROC) curves compared favorably with other published results. This procedure is quite sensitive, so much so that it has revealed the presence of probe sets that might properly be called "unanticipated positives" rather than "false positives", because plots of these probe sets strongly suggest that they are differentially expressed.

Conclusion: The median ANOVA (1-p) approach presented here is a very simple methodology that does not depend on any specific probe level or probe models, and does not require any pre-processing other than within-chip standardization of probe level log amplitudes. Its performance is comparable to other published methods on the standard spike-in data sets, and has revealed the presence of new categories of probe sets that might properly be referred to as "unanticipated positives" and "unanticipated negatives" that need to be taken into account when using spiked-in data sets at "truthed" test beds.

Show MeSH
Related in: MedlinePlus