Abstract
Calculations for the number of per gene replicate spots in microarray experiments are presented for the purpose of obtaining estimates of the sampling variability present in microarray data, and for determining the minimum number of replicate spots required to achieve a high probability of detecting a significant fold change in gene expression. Our approach is based on data from control microarrays, and employs standard statistical estimation techniques. We have demonstrated the usefulness of our framework by analyzing two experimental data sets containing control array data. The minimum number of replicate spots required on a treatment array were calculated to achieve detection of a 3-fold increase in expression with 90%, 95% or 99% confidence. The inclusion of replicate spots on microarrays not only allows more accurate estimation of the variability present in an experiment, but more importantly increases the probability of detecting genes undergoing significant fold changes in expression, while substantially decreasing the probability of observing fold changes due to chance rather than true differential expression.
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Recommended Citation
Black, Michael A. and Doerge, R. W.
(2001).
"CALCULATION OF THE MINIMUM NUMBER OF REPLICATE SPOTS REQUIRED FOR DETECTION OF SIGNIFICANT GENE EXPRESSION FOLD CHANGE IN
MICROARRA Y EXPERIMENTS,"
Conference on Applied Statistics in Agriculture.
https://doi.org/10.4148/2475-7772.1222
CALCULATION OF THE MINIMUM NUMBER OF REPLICATE SPOTS REQUIRED FOR DETECTION OF SIGNIFICANT GENE EXPRESSION FOLD CHANGE IN MICROARRA Y EXPERIMENTS
Calculations for the number of per gene replicate spots in microarray experiments are presented for the purpose of obtaining estimates of the sampling variability present in microarray data, and for determining the minimum number of replicate spots required to achieve a high probability of detecting a significant fold change in gene expression. Our approach is based on data from control microarrays, and employs standard statistical estimation techniques. We have demonstrated the usefulness of our framework by analyzing two experimental data sets containing control array data. The minimum number of replicate spots required on a treatment array were calculated to achieve detection of a 3-fold increase in expression with 90%, 95% or 99% confidence. The inclusion of replicate spots on microarrays not only allows more accurate estimation of the variability present in an experiment, but more importantly increases the probability of detecting genes undergoing significant fold changes in expression, while substantially decreasing the probability of observing fold changes due to chance rather than true differential expression.
