Relative power and sample size analysis on gene expression profiling data
<p>Abstract</p> <p>Background</p> <p>With the increasing number of expression profiling technologies, researchers today are confronted with choosing the technology that has sufficient power with minimal sample size, in order to reduce cost and time. These depend on data...
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2009-09-01
|
| Series: | BMC Genomics |
| Online Access: | http://www.biomedcentral.com/1471-2164/10/439 |
| _version_ | 1818472673988050944 |
|---|---|
| author | den Dunnen JT Hooiveld GJEJ Pedotti P 't Hoen PAC van Iterson M van Ommen GJB Boer JM Menezes RX |
| author_facet | den Dunnen JT Hooiveld GJEJ Pedotti P 't Hoen PAC van Iterson M van Ommen GJB Boer JM Menezes RX |
| author_sort | den Dunnen JT |
| collection | DOAJ |
| description | <p>Abstract</p> <p>Background</p> <p>With the increasing number of expression profiling technologies, researchers today are confronted with choosing the technology that has sufficient power with minimal sample size, in order to reduce cost and time. These depend on data variability, partly determined by sample type, preparation and processing. Objective measures that help experimental design, given own pilot data, are thus fundamental.</p> <p>Results</p> <p>Relative power and sample size analysis were performed on two distinct data sets. The first set consisted of Affymetrix array data derived from a nutrigenomics experiment in which weak, intermediate and strong PPAR<it>α </it>agonists were administered to wild-type and PPAR<it>α</it>-null mice. Our analysis confirms the hierarchy of PPAR<it>α</it>-activating compounds previously reported and the general idea that larger effect sizes positively contribute to the average power of the experiment. A simulation experiment was performed that mimicked the effect sizes seen in the first data set. The relative power was predicted but the estimates were slightly conservative. The second, more challenging, data set describes a microarray platform comparison study using hippocampal <it>δ</it>C-doublecortin-like kinase transgenic mice that were compared to wild-type mice, which was combined with results from Solexa/Illumina deep sequencing runs. As expected, the choice of technology greatly influences the performance of the experiment. Solexa/Illumina deep sequencing has the highest overall power followed by the microarray platforms Agilent and Affymetrix. Interestingly, Solexa/Illumina deep sequencing displays comparable power across all intensity ranges, in contrast with microarray platforms that have decreased power in the low intensity range due to background noise. This means that deep sequencing technology is especially more powerful in detecting differences in the low intensity range, compared to microarray platforms.</p> <p>Conclusion</p> <p>Power and sample size analysis based on pilot data give valuable information on the performance of the experiment and can thereby guide further decisions on experimental design. Solexa/Illumina deep sequencing is the technology of choice if interest lies in genes expressed in the low-intensity range. Researchers can get guidance on experimental design using our approach on their own pilot data implemented as a BioConductor package, SSPA <url>http://bioconductor.org/packages/release/bioc/html/SSPA.html</url>.</p> |
| first_indexed | 2024-04-14T04:10:45Z |
| format | Article |
| id | doaj.art-7d9de50073d54c9c8538095e7b7f4a72 |
| institution | Directory Open Access Journal |
| issn | 1471-2164 |
| language | English |
| last_indexed | 2024-04-14T04:10:45Z |
| publishDate | 2009-09-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Genomics |
| spelling | doaj.art-7d9de50073d54c9c8538095e7b7f4a722022-12-22T02:13:10ZengBMCBMC Genomics1471-21642009-09-0110143910.1186/1471-2164-10-439Relative power and sample size analysis on gene expression profiling dataden Dunnen JTHooiveld GJEJPedotti P't Hoen PACvan Iterson Mvan Ommen GJBBoer JMMenezes RX<p>Abstract</p> <p>Background</p> <p>With the increasing number of expression profiling technologies, researchers today are confronted with choosing the technology that has sufficient power with minimal sample size, in order to reduce cost and time. These depend on data variability, partly determined by sample type, preparation and processing. Objective measures that help experimental design, given own pilot data, are thus fundamental.</p> <p>Results</p> <p>Relative power and sample size analysis were performed on two distinct data sets. The first set consisted of Affymetrix array data derived from a nutrigenomics experiment in which weak, intermediate and strong PPAR<it>α </it>agonists were administered to wild-type and PPAR<it>α</it>-null mice. Our analysis confirms the hierarchy of PPAR<it>α</it>-activating compounds previously reported and the general idea that larger effect sizes positively contribute to the average power of the experiment. A simulation experiment was performed that mimicked the effect sizes seen in the first data set. The relative power was predicted but the estimates were slightly conservative. The second, more challenging, data set describes a microarray platform comparison study using hippocampal <it>δ</it>C-doublecortin-like kinase transgenic mice that were compared to wild-type mice, which was combined with results from Solexa/Illumina deep sequencing runs. As expected, the choice of technology greatly influences the performance of the experiment. Solexa/Illumina deep sequencing has the highest overall power followed by the microarray platforms Agilent and Affymetrix. Interestingly, Solexa/Illumina deep sequencing displays comparable power across all intensity ranges, in contrast with microarray platforms that have decreased power in the low intensity range due to background noise. This means that deep sequencing technology is especially more powerful in detecting differences in the low intensity range, compared to microarray platforms.</p> <p>Conclusion</p> <p>Power and sample size analysis based on pilot data give valuable information on the performance of the experiment and can thereby guide further decisions on experimental design. Solexa/Illumina deep sequencing is the technology of choice if interest lies in genes expressed in the low-intensity range. Researchers can get guidance on experimental design using our approach on their own pilot data implemented as a BioConductor package, SSPA <url>http://bioconductor.org/packages/release/bioc/html/SSPA.html</url>.</p>http://www.biomedcentral.com/1471-2164/10/439 |
| spellingShingle | den Dunnen JT Hooiveld GJEJ Pedotti P 't Hoen PAC van Iterson M van Ommen GJB Boer JM Menezes RX Relative power and sample size analysis on gene expression profiling data BMC Genomics |
| title | Relative power and sample size analysis on gene expression profiling data |
| title_full | Relative power and sample size analysis on gene expression profiling data |
| title_fullStr | Relative power and sample size analysis on gene expression profiling data |
| title_full_unstemmed | Relative power and sample size analysis on gene expression profiling data |
| title_short | Relative power and sample size analysis on gene expression profiling data |
| title_sort | relative power and sample size analysis on gene expression profiling data |
| url | http://www.biomedcentral.com/1471-2164/10/439 |
| work_keys_str_mv | AT dendunnenjt relativepowerandsamplesizeanalysisongeneexpressionprofilingdata AT hooiveldgjej relativepowerandsamplesizeanalysisongeneexpressionprofilingdata AT pedottip relativepowerandsamplesizeanalysisongeneexpressionprofilingdata AT thoenpac relativepowerandsamplesizeanalysisongeneexpressionprofilingdata AT vanitersonm relativepowerandsamplesizeanalysisongeneexpressionprofilingdata AT vanommengjb relativepowerandsamplesizeanalysisongeneexpressionprofilingdata AT boerjm relativepowerandsamplesizeanalysisongeneexpressionprofilingdata AT menezesrx relativepowerandsamplesizeanalysisongeneexpressionprofilingdata |