How to decide? Different methods of calculating gene expression from short oligonucleotide array data will give different results-4

<p><b>Copyright information:</b></p><p>Taken from "How to decide? Different methods of calculating gene expression from short oligonucleotide array data will give different results"</p><p>BMC Bioinformatics 2006;7():137-137.</p><p>Published online 15 Mar 2006</p><p>PMCID:PMC1431565.</p><p>Copyright © 2006 Millenaar et al; licensee BioMed Central Ltd.</p> GC-RMA and PDNN. Reproducibility is calculated as the standard deviation divided by the average signal, which is the coefficient of variation (CV). The CV values are sorted from low to high. The PM, RMA and PDNN algorithms are giving the best reproducible results and MAS 5.0 the worst. Reproducibility of the two other replicated treatments ethylene and low-light gave similar results (data not shown)

How to decide? Different methods of calculating gene expression from short oligonucleotide array data will give different results-5

<p><b>Copyright information:</b></p><p>Taken from "How to decide? Different methods of calculating gene expression from short oligonucleotide array data will give different results"</p><p>BMC Bioinformatics 2006;7():137-137.</p><p>Published online 15 Mar 2006</p><p>PMCID:PMC1431565.</p><p>Copyright © 2006 Millenaar et al; licensee BioMed Central Ltd.</p>ignal intensity is smaller than the PM signal. In panel A and B there is no correlation between the PM and MM signals as can been seen by the low slope and Pearson correlation coefficient. This in contrast to results in panel C and D were the slope and Pearson correlation coefficient are large. These signals are obtained from the microarray scanner and are the input for the six calculation methods

How to decide? Different methods of calculating gene expression from short oligonucleotide array data will give different results-3

<p><b>Copyright information:</b></p><p>Taken from "How to decide? Different methods of calculating gene expression from short oligonucleotide array data will give different results"</p><p>BMC Bioinformatics 2006;7():137-137.</p><p>Published online 15 Mar 2006</p><p>PMCID:PMC1431565.</p><p>Copyright © 2006 Millenaar et al; licensee BioMed Central Ltd.</p>eriment. The observed concentrations are adjusted so that all lines have the same intercept at a ln concentration of 2.8 (16 pmol). The solid line without symbols represents the ideal slope-1 line. () The accuracy of picking up the spiked-in genes. The significance between two successive spike-in concentrations (0–0.125; 0.125–0.25; etc.) was calculated for each gene. The number of genes where calculated per spike-in concentration that significantly where up regulated, and presented on the y-axis as percentage. This means that at "1" all 42 genes where significant at a given concentration

How to decide? Different methods of calculating gene expression from short oligonucleotide array data will give different results-0

<p><b>Copyright information:</b></p><p>Taken from "How to decide? Different methods of calculating gene expression from short oligonucleotide array data will give different results"</p><p>BMC Bioinformatics 2006;7():137-137.</p><p>Published online 15 Mar 2006</p><p>PMCID:PMC1431565.</p><p>Copyright © 2006 Millenaar et al; licensee BioMed Central Ltd.</p>ethod used to calculate gene expression. This diagram shows exactly the differences and similarities between all the methods. PDNN, MAS 5.0 (MAS, or GCOS), dChip PMMM (PMMM), dChip PM only (PM), RMA and GC-RMA were used. Only 790 genes were in common for all four algorithms. Comparable results were obtained from the low-light treatment. Areas with one letter shows genes which are unique for one method, areas with two letters shows genes which are only in common between these two methods, and so on

How to decide? Different methods of calculating gene expression from short oligonucleotide array data will give different results-2

<p><b>Copyright information:</b></p><p>Taken from "How to decide? Different methods of calculating gene expression from short oligonucleotide array data will give different results"</p><p>BMC Bioinformatics 2006;7():137-137.</p><p>Published online 15 Mar 2006</p><p>PMCID:PMC1431565.</p><p>Copyright © 2006 Millenaar et al; licensee BioMed Central Ltd.</p>lation (r= 0.9913), see also table 1. However, variation increased closer to the unity. For example a signal of 4 in MAS 5.0 results in a signal between 4 to 5.5 in RMA on a ln scale

How to decide? Different methods of calculating gene expression from short oligonucleotide array data will give different results-1

<p><b>Copyright information:</b></p><p>Taken from "How to decide? Different methods of calculating gene expression from short oligonucleotide array data will give different results"</p><p>BMC Bioinformatics 2006;7():137-137.</p><p>Published online 15 Mar 2006</p><p>PMCID:PMC1431565.</p><p>Copyright © 2006 Millenaar et al; licensee BioMed Central Ltd.</p>p PM only (PM), RMA, GC-RMA and PDNN. The signal intensity is sorted from low to high. Similar results where observed with expression data from treated plants

How to decide? Different methods of calculating gene expression from short oligonucleotide array data will give different results-6

<p><b>Copyright information:</b></p><p>Taken from "How to decide? Different methods of calculating gene expression from short oligonucleotide array data will give different results"</p><p>BMC Bioinformatics 2006;7():137-137.</p><p>Published online 15 Mar 2006</p><p>PMCID:PMC1431565.</p><p>Copyright © 2006 Millenaar et al; licensee BioMed Central Ltd.</p>ets are used which represents one gene. Both slope and correlation are sorted from low to high. See figure 6 for further explanation and individual examples