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Optimization of cDNA microarrays procedures using criteria that do not rely on external standards.

Bruland T, Anderssen E, Doseth B, Bergum H, Beisvag V, Laegreid A - BMC Genomics (2007)

Bottom Line: We performed a cDNA microarry experiment including RNA from samples with high expected differential gene expression termed "high contrasts" (rat cell lines AR42J and NRK52E) compared to self-self hybridization, and optimized a pipeline to maximize the number of genes found to be differentially expressed in the "high contrasts" RNA samples by estimating the false discovery rate (FDR) using a distribution obtained from the self-self experiment.Cross platform microarray (Illumina) analysis was used to validate that the increase in the number of differentially expressed genes found by HCSSM was real.The results show that HCSSM can be a useful and simple approach to optimize microarray procedures without including external standards.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Cancer Research and Molecular Medicine, Faculty of Medicine, Norwegian University of Science and Technology (NTNU), N-7489 Trondheim, Norway. torunn.bruland@ntnu.no

ABSTRACT

Background: The measurement of gene expression using microarray technology is a complicated process in which a large number of factors can be varied. Due to the lack of standard calibration samples such as are used in traditional chemical analysis it may be a problem to evaluate whether changes done to the microarray procedure actually improve the identification of truly differentially expressed genes. The purpose of the present work is to report the optimization of several steps in the microarray process both in laboratory practices and in data processing using criteria that do not rely on external standards.

Results: We performed a cDNA microarry experiment including RNA from samples with high expected differential gene expression termed "high contrasts" (rat cell lines AR42J and NRK52E) compared to self-self hybridization, and optimized a pipeline to maximize the number of genes found to be differentially expressed in the "high contrasts" RNA samples by estimating the false discovery rate (FDR) using a distribution obtained from the self-self experiment. The proposed high-contrast versus self-self method (HCSSM) requires only four microarrays per evaluation. The effects of blocking reagent dose, filtering, and background corrections methodologies were investigated. In our experiments a dose of 250 ng LNA (locked nucleic acid) dT blocker, no background correction and weight based filtering gave the largest number of differentially expressed genes. The choice of background correction method had a stronger impact on the estimated number of differentially expressed genes than the choice of filtering method. Cross platform microarray (Illumina) analysis was used to validate that the increase in the number of differentially expressed genes found by HCSSM was real.

Conclusion: The results show that HCSSM can be a useful and simple approach to optimize microarray procedures without including external standards. Our optimizing method is highly applicable to both long oligo-probe microarrays which have become commonly used for well characterized organisms such as man, mouse and rat, as well as to cDNA microarrays which are still of importance for organisms with incomplete genome sequence information such as many bacteria, plants and fish.

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Effects of background correction and level of filtration on the number of differentially expressed genes estimated. The figure shows data from hybridization with 250 ng LNA blocker added.
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Figure 4: Effects of background correction and level of filtration on the number of differentially expressed genes estimated. The figure shows data from hybridization with 250 ng LNA blocker added.

Mentions: The data from the blocking study were also analysed using combinations of filter methods and background correction (Figure 4 and Additional file 1). We tested the following five different filtering methods of increasing complexity: coarse, medium, fine, uncertain and weighting filter. Filters were based on spot foreground intensity, percentage of pixels saturated and ratio uncertainties as described in the Methods section. In addition we applied three different background corrections: none were only foreground signal is used to calculate ratios, Edwards which is a subtraction method that ensures positive values [21] and dampened Edwards were a small number is added to the corrected signal to avoid extreme ratios in those cases where the background corrected intensities would be very low. Weight based filtering; using the ratio uncertainty to reduce the impact of "bad" spots without removing them completely found the highest numbers of differentially expressed genes regardless of background correction method and blocking agent dose (Figure 4 and Addition file 1). The choice of background correction method had a higher impact on the number of differentially expressed genes than filtering. No background correction resulted in the highest amount of differentially expressed genes estimated, the numbers ranging from 515 to 2064 under the five different filtering methods and blocking with 250 ng LNA (Figure 4). Edwards and dampened Edwards gave 66–120 and 191–588 differentially expressed genes, respectively, under the same filtering conditions. Our results strongly indicate that omission of background correction consistently improves the results. The reason may be that background correction introduces a lot of variability to remove a small bias. As long as the red and green backgrounds are highly correlated they will dampen the ratios, but only significantly for the low intensity spots, for which the ratios are uncertain anyway. One strategy may be to use error propagation to evaluate background correction, and only background correct the spots where the bias is large, thus not increasing the variance of all spots, but still removing the bias for some of the worst affected spots.


Optimization of cDNA microarrays procedures using criteria that do not rely on external standards.

Bruland T, Anderssen E, Doseth B, Bergum H, Beisvag V, Laegreid A - BMC Genomics (2007)

Effects of background correction and level of filtration on the number of differentially expressed genes estimated. The figure shows data from hybridization with 250 ng LNA blocker added.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2147032&req=5

Figure 4: Effects of background correction and level of filtration on the number of differentially expressed genes estimated. The figure shows data from hybridization with 250 ng LNA blocker added.
Mentions: The data from the blocking study were also analysed using combinations of filter methods and background correction (Figure 4 and Additional file 1). We tested the following five different filtering methods of increasing complexity: coarse, medium, fine, uncertain and weighting filter. Filters were based on spot foreground intensity, percentage of pixels saturated and ratio uncertainties as described in the Methods section. In addition we applied three different background corrections: none were only foreground signal is used to calculate ratios, Edwards which is a subtraction method that ensures positive values [21] and dampened Edwards were a small number is added to the corrected signal to avoid extreme ratios in those cases where the background corrected intensities would be very low. Weight based filtering; using the ratio uncertainty to reduce the impact of "bad" spots without removing them completely found the highest numbers of differentially expressed genes regardless of background correction method and blocking agent dose (Figure 4 and Addition file 1). The choice of background correction method had a higher impact on the number of differentially expressed genes than filtering. No background correction resulted in the highest amount of differentially expressed genes estimated, the numbers ranging from 515 to 2064 under the five different filtering methods and blocking with 250 ng LNA (Figure 4). Edwards and dampened Edwards gave 66–120 and 191–588 differentially expressed genes, respectively, under the same filtering conditions. Our results strongly indicate that omission of background correction consistently improves the results. The reason may be that background correction introduces a lot of variability to remove a small bias. As long as the red and green backgrounds are highly correlated they will dampen the ratios, but only significantly for the low intensity spots, for which the ratios are uncertain anyway. One strategy may be to use error propagation to evaluate background correction, and only background correct the spots where the bias is large, thus not increasing the variance of all spots, but still removing the bias for some of the worst affected spots.

Bottom Line: We performed a cDNA microarry experiment including RNA from samples with high expected differential gene expression termed "high contrasts" (rat cell lines AR42J and NRK52E) compared to self-self hybridization, and optimized a pipeline to maximize the number of genes found to be differentially expressed in the "high contrasts" RNA samples by estimating the false discovery rate (FDR) using a distribution obtained from the self-self experiment.Cross platform microarray (Illumina) analysis was used to validate that the increase in the number of differentially expressed genes found by HCSSM was real.The results show that HCSSM can be a useful and simple approach to optimize microarray procedures without including external standards.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Cancer Research and Molecular Medicine, Faculty of Medicine, Norwegian University of Science and Technology (NTNU), N-7489 Trondheim, Norway. torunn.bruland@ntnu.no

ABSTRACT

Background: The measurement of gene expression using microarray technology is a complicated process in which a large number of factors can be varied. Due to the lack of standard calibration samples such as are used in traditional chemical analysis it may be a problem to evaluate whether changes done to the microarray procedure actually improve the identification of truly differentially expressed genes. The purpose of the present work is to report the optimization of several steps in the microarray process both in laboratory practices and in data processing using criteria that do not rely on external standards.

Results: We performed a cDNA microarry experiment including RNA from samples with high expected differential gene expression termed "high contrasts" (rat cell lines AR42J and NRK52E) compared to self-self hybridization, and optimized a pipeline to maximize the number of genes found to be differentially expressed in the "high contrasts" RNA samples by estimating the false discovery rate (FDR) using a distribution obtained from the self-self experiment. The proposed high-contrast versus self-self method (HCSSM) requires only four microarrays per evaluation. The effects of blocking reagent dose, filtering, and background corrections methodologies were investigated. In our experiments a dose of 250 ng LNA (locked nucleic acid) dT blocker, no background correction and weight based filtering gave the largest number of differentially expressed genes. The choice of background correction method had a stronger impact on the estimated number of differentially expressed genes than the choice of filtering method. Cross platform microarray (Illumina) analysis was used to validate that the increase in the number of differentially expressed genes found by HCSSM was real.

Conclusion: The results show that HCSSM can be a useful and simple approach to optimize microarray procedures without including external standards. Our optimizing method is highly applicable to both long oligo-probe microarrays which have become commonly used for well characterized organisms such as man, mouse and rat, as well as to cDNA microarrays which are still of importance for organisms with incomplete genome sequence information such as many bacteria, plants and fish.

Show MeSH
Related in: MedlinePlus