Effects of filtering by Present call on analysis of microarray experiments.

@article{citeulike:1268725, abstract = {BACKGROUND: Affymetrix GeneChips are widely used for expression profiling of tens of thousands of genes. The large number of comparisons can lead to false positives. Various methods have been used to reduce false positives, but they have rarely been compared or quantitatively evaluated. Here we describe and evaluate a simple method that uses the detection (Present/Absent) call generated by the Affymetrix microarray suite version 5 software (MAS5) to remove data that is not reliably detected before further analysis, and compare this with filtering by expression level. We explore the effects of various thresholds for removing data in experiments of different size (from 3 to 10 arrays per treatment), as well as their relative power to detect significant differences in expression. RESULTS: Our approach sets a threshold for the fraction of arrays called Present in at least one treatment group. This method removes a large percentage of probe sets called Absent before carrying out the comparisons, while retaining most of the probe sets called Present. It preferentially retains the more significant probe sets (p < or = 0.001) and those probe sets that are turned on or off, and improves the false discovery rate. Permutations to estimate false positives indicate that probe sets removed by the filter contribute a disproportionate number of false positives. Filtering by fraction Present is effective when applied to data generated either by the MAS5 algorithm or by other probe-level algorithms, for example RMA (robust multichip average). Experiment size greatly affects the ability to reproducibly detect significant differences, and also impacts the effect of filtering; smaller experiments (3-5 samples per treatment group) benefit from more restrictive filtering (> or =50\% Present). CONCLUSION: Use of a threshold fraction of Present detection calls (derived by MAS5) provided a simple method that effectively eliminated from analysis probe sets that are unlikely to be reliable while preserving the most significant probe sets and those turned on or off; it thereby increased the ratio of true positives to false positives.}, address = {Department of Medical and Molecular Genetics, Indiana University, Indianapolis, Indiana, USA. jnmcclin@iupui.edu}, author = {McClintick, J. N. and Edenberg, H. J. }, citeulike-article-id = {1268725}, doi = {http://dx.doi.org/10.1186/1471-2105-7-49}, issn = {1471-2105}, journal = {BMC Bioinformatics}, keywords = {affymetrix, analysis, filtering}, posted-at = {2008-01-04 00:35:46}, priority = {4}, title = {Effects of filtering by Present call on analysis of microarray experiments.}, url = {http://dx.doi.org/10.1186/1471-2105-7-49}, volume = {7}, year = {2006} }

TY - JOUR ID - citeulike:1268725 L3 - citeulike-article-id:1268725 TI - Effects of filtering by Present call on analysis of microarray experiments. JF - BMC Bioinformatics VL - 7 AD - Department of Medical and Molecular Genetics, Indiana University, Indianapolis, Indiana, USA. jnmcclin@iupui.edu SN - 1471-2105 N2 - BACKGROUND: Affymetrix GeneChips are widely used for expression profiling of tens of thousands of genes. The large number of comparisons can lead to false positives. Various methods have been used to reduce false positives, but they have rarely been compared or quantitatively evaluated. Here we describe and evaluate a simple method that uses the detection (Present/Absent) call generated by the Affymetrix microarray suite version 5 software (MAS5) to remove data that is not reliably detected before further analysis, and compare this with filtering by expression level. We explore the effects of various thresholds for removing data in experiments of different size (from 3 to 10 arrays per treatment), as well as their relative power to detect significant differences in expression. RESULTS: Our approach sets a threshold for the fraction of arrays called Present in at least one treatment group. This method removes a large percentage of probe sets called Absent before carrying out the comparisons, while retaining most of the probe sets called Present. It preferentially retains the more significant probe sets (p < or = 0.001) and those probe sets that are turned on or off, and improves the false discovery rate. Permutations to estimate false positives indicate that probe sets removed by the filter contribute a disproportionate number of false positives. Filtering by fraction Present is effective when applied to data generated either by the MAS5 algorithm or by other probe-level algorithms, for example RMA (robust multichip average). Experiment size greatly affects the ability to reproducibly detect significant differences, and also impacts the effect of filtering; smaller experiments (3-5 samples per treatment group) benefit from more restrictive filtering (> or =50% Present). CONCLUSION: Use of a threshold fraction of Present detection calls (derived by MAS5) provided a simple method that effectively eliminated from analysis probe sets that are unlikely to be reliable while preserving the most significant probe sets and those turned on or off; it thereby increased the ratio of true positives to false positives. KW - affymetrix KW - analysis KW - filtering AU - McClintick, JN AU - Edenberg, HJ PY - 2006/// UR - http://dx.doi.org/10.1186/1471-2105-7-49 ER -