Limits...
Considerations when using the significance analysis of microarrays (SAM) algorithm.

Larsson O, Wahlestedt C, Timmons JA - BMC Bioinformatics (2005)

Bottom Line: We have examined the effect of discrete data selection criteria (qualification criteria for inclusion) and response thresholds (out-put filtering) on the number of significant genes reported by SAM.This effect can be so large that it changes subsequent post hoc analysis interpretation, such as ontology overrepresentation analysis.Our results argue for caution when using SAM.

View Article: PubMed Central - HTML - PubMed

Affiliation: Center for Genomics and Bioinformatics, Karolinska Institutet, Berzelius Väg, 35, 171 77 Stockholm, Sweden. ola.larsson@cgb.ki.se

ABSTRACT

Background: Users of microarray technology typically strive to use universally acceptable data analysis strategies to determine significant expression changes in their experiments. One of the most frequently utilised methods for gene expression data analysis is SAM (significance analysis of microarrays). The impact of selection thresholds, on the output from SAM, may critically alter the conclusion of a study, yet this consideration has not been systematically evaluated in any publication.

Results: We have examined the effect of discrete data selection criteria (qualification criteria for inclusion) and response thresholds (out-put filtering) on the number of significant genes reported by SAM. The use of a reduced data set by applying arbitrary restrictions vis-à-vis abundance calls (e.g. from D-chip) or application of the fold change (FC) option within SAM (named the FC hurdle hereafter), can substantially alter the significant gene list when running SAM in Microsoft Excel. We determined that for a given final FC criteria (e.g. 1.5 fold change) the FC hurdle applied within Microsoft Excel SAM alters the number of reported genes above the final FC criteria. The reason is that the FC hurdle changes the composition of the control data set, such that a different significance level (q-value) is obtained for any given gene. This effect can be so large that it changes subsequent post hoc analysis interpretation, such as ontology overrepresentation analysis.

Conclusion: Our results argue for caution when using SAM. All data sets analysed with SAM could be reanalysed taking into account the potential impact of the use of arbitrary thresholds to trim data sets before significance testing.

Show MeSH

Related in: MedlinePlus

FC effects on the senescence data set: SAM analysis was used at various fold changes using the senescence data set while scoring genes with a q-value of <0.01 and FC>1.5. A comparison between non-senescent cells and senescent cells was used (two replicates of the senescent cells and four replicates of the non-senescent cells).
© Copyright Policy
Related In: Results  -  Collection


getmorefigures.php?uid=PMC1173086&req=5

Figure 2: FC effects on the senescence data set: SAM analysis was used at various fold changes using the senescence data set while scoring genes with a q-value of <0.01 and FC>1.5. A comparison between non-senescent cells and senescent cells was used (two replicates of the senescent cells and four replicates of the non-senescent cells).

Mentions: The second data set was derived from an in-vitro mouse senescence study performed on U74Av2 chips [7] and normalized using RMA [4-6]. When comparing two of the time points during the induction of senescence we were unable to observe any effect of FC selection within SAM on the yield of significant genes (Figure 2). We believe this reflected the very low q-values obtained when originally using SAM, which in turn most likely reflects the low experimental variation that one can achieve using in-vitro models.


Considerations when using the significance analysis of microarrays (SAM) algorithm.

Larsson O, Wahlestedt C, Timmons JA - BMC Bioinformatics (2005)

FC effects on the senescence data set: SAM analysis was used at various fold changes using the senescence data set while scoring genes with a q-value of <0.01 and FC>1.5. A comparison between non-senescent cells and senescent cells was used (two replicates of the senescent cells and four replicates of the non-senescent cells).
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC1173086&req=5

Figure 2: FC effects on the senescence data set: SAM analysis was used at various fold changes using the senescence data set while scoring genes with a q-value of <0.01 and FC>1.5. A comparison between non-senescent cells and senescent cells was used (two replicates of the senescent cells and four replicates of the non-senescent cells).
Mentions: The second data set was derived from an in-vitro mouse senescence study performed on U74Av2 chips [7] and normalized using RMA [4-6]. When comparing two of the time points during the induction of senescence we were unable to observe any effect of FC selection within SAM on the yield of significant genes (Figure 2). We believe this reflected the very low q-values obtained when originally using SAM, which in turn most likely reflects the low experimental variation that one can achieve using in-vitro models.

Bottom Line: We have examined the effect of discrete data selection criteria (qualification criteria for inclusion) and response thresholds (out-put filtering) on the number of significant genes reported by SAM.This effect can be so large that it changes subsequent post hoc analysis interpretation, such as ontology overrepresentation analysis.Our results argue for caution when using SAM.

View Article: PubMed Central - HTML - PubMed

Affiliation: Center for Genomics and Bioinformatics, Karolinska Institutet, Berzelius Väg, 35, 171 77 Stockholm, Sweden. ola.larsson@cgb.ki.se

ABSTRACT

Background: Users of microarray technology typically strive to use universally acceptable data analysis strategies to determine significant expression changes in their experiments. One of the most frequently utilised methods for gene expression data analysis is SAM (significance analysis of microarrays). The impact of selection thresholds, on the output from SAM, may critically alter the conclusion of a study, yet this consideration has not been systematically evaluated in any publication.

Results: We have examined the effect of discrete data selection criteria (qualification criteria for inclusion) and response thresholds (out-put filtering) on the number of significant genes reported by SAM. The use of a reduced data set by applying arbitrary restrictions vis-à-vis abundance calls (e.g. from D-chip) or application of the fold change (FC) option within SAM (named the FC hurdle hereafter), can substantially alter the significant gene list when running SAM in Microsoft Excel. We determined that for a given final FC criteria (e.g. 1.5 fold change) the FC hurdle applied within Microsoft Excel SAM alters the number of reported genes above the final FC criteria. The reason is that the FC hurdle changes the composition of the control data set, such that a different significance level (q-value) is obtained for any given gene. This effect can be so large that it changes subsequent post hoc analysis interpretation, such as ontology overrepresentation analysis.

Conclusion: Our results argue for caution when using SAM. All data sets analysed with SAM could be reanalysed taking into account the potential impact of the use of arbitrary thresholds to trim data sets before significance testing.

Show MeSH
Related in: MedlinePlus