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The Reproducibility Task: Cancer Biology seeks to address growing concerns about reproducibility in scientific research by conducting replications of selected experiments from a substantial number of high-profile papers in the field of cancer biology

The Reproducibility Task: Cancer Biology seeks to address growing concerns about reproducibility in scientific research by conducting replications of selected experiments from a substantial number of high-profile papers in the field of cancer biology. co-treatment of either HDIs or an IGF-1R inhibitor, in combination with TKIs (Figure 5A-B). Inhibition Rabbit Polyclonal to IL11RA of IGF-1R activation also led to decreased KDM5A expression and restoration of H3K4 methylation, suggesting a direct link between the IGF-1R signaling YW3-56 pathway and KDM5A function (Figure 7A, 7C, and 7I). The Reproducibility Project: Cancer Biology is a collaboration between the Center for Open Science and Science Exchange and the results of the replications will be published in gene has become an attractive target for small molecular inhibitors. Tyrosine kinase inhibitors (TKIs) that target test, difference between two independent means, alpha error = 0.05 Power Calculations Performed with G*Power software, version 3.1.7 (Faul et al., 2007). (based test, difference between two independent means, Bonferronis correction, alpha error = 0.01667 Power calculations Performed with G*Power software, version 3.1.7 (Faul et al., 2007). 2% variance: test, difference between two independent means, Bonferronis correction, alpha error = 0.01667 Power calculations Performed with G*Power software, version 3.1.7 (Faul et al., 2007). (the number of replicates) 10,000 simulations were run and Mantel-Haenszel chi-squared value was calculated for each simulated data set. The energy was determined by keeping track of the amount of instances check after that, difference between two 3rd party means, Bonferornis modification: alpha mistake = 0.0125 Power calculations Performed with G*Power software, version 3.1.7 (Faul et al., 2007). check: Means: Wilcoxon-Mann-Whitney, Bonferornis modification: alpha mistake = 0.025 Power calculations Performed with G*Power software, version 3.1.7. (Faul et al., 2007) check, difference between two 3rd party means, Bonferronis modification, alpha mistake = 0.025 Power calculations Performed with G*Power software, version 3.1.7 (Faul et al., 2007). 2% variance: check, difference between two 3rd party means, Bonferronis modification, alpha mistake = 0.025 Power calculations Performed with G*Power software, version 3.1.7 (Faul et al., 2007). 2% variance: check, difference between two 3rd party means,, alpha mistake = 0.05 Power calculations Performed with G*Power software, version 3.1.7 (Faul et al., 2007). 2% variance: check, difference between two 3rd party means, alpha mistake = 0.05 Power calculations Performed with G*Power software, version 3.1.7 (Faul et al., 2007). 2% variance: thead th valign=”best” rowspan=”1″ colspan=”1″ Group 1 /th th valign=”best” rowspan=”1″ colspan=”1″ Group 2 /th th valign=”best” rowspan=”1″ colspan=”1″ Impact size em d /em /th th valign=”best” rowspan=”1″ colspan=”1″ A priori power /th th valign=”best” rowspan=”1″ colspan=”1″ Group 1 br / test size /th th valign=”best” rowspan=”1″ colspan=”1″ Group 2 br / test size /th /thead Automobile treated br / Personal computer9 DTPsAEW541 treated br / Personal computer9 DTPs66.5139399.9%22 Open up in another window 15% variance: thead th valign=”top” rowspan=”1″ colspan=”1″ Group 1 /th th valign=”top” rowspan=”1″ colspan=”1″ Group 2 /th th valign=”top” rowspan=”1″ colspan=”1″ Effect size em d /em /th th valign=”top” rowspan=”1″ colspan=”1″ A priori power /th th valign=”top” rowspan=”1″ colspan=”1″ Group 1 br / test size /th th valign=”top” rowspan=”1″ colspan=”1″ Group 2 br / test size /th /thead Vehicle treated br / PC9 DTPsAEW541 treated br / PC9 DTPs8.8685297.9%22 Open up in another window 28% variance: thead th valign=”top” rowspan=”1″ colspan=”1″ Group 1 /th th valign=”top” rowspan=”1″ colspan=”1″ Group 2 /th th valign=”top” rowspan=”1″ colspan=”1″ Effect size em d /em /th th valign=”top” rowspan=”1″ colspan=”1″ A priori power /th th valign=”top” rowspan=”1″ colspan=”1″ Group 1 br / test size /th th valign=”top” rowspan=”1″ colspan=”1″ Group 2 br / test size /th /thead Vehicle treated br / PC9 DTPsAEW541 treated br / PC9 DTPs4.7509998.8%33 Open up in a separate window 40% variance: thead th valign=”top” rowspan=”1″ colspan=”1″ YW3-56 Group 1 /th th valign=”top” rowspan=”1″ colspan=”1″ Group 2 /th th valign=”top” rowspan=”1″ colspan=”1″ Effect size em d /em /th th valign=”top” rowspan=”1″ colspan=”1″ A priori power /th th valign=”top” rowspan=”1″ colspan=”1″ Group 1 sample size /th th valign=”top” rowspan=”1″ colspan=”1″ Group 2 sample YW3-56 size /th /thead Vehicle treated br / PC9 DTPsAEW541 treated br / PC9 DTPs3.3257085.5%33 Open in a separate window In order to produce quantitative replication data, we will run the experiment three times. Each time we will quantify band intensity. We will determine the standard deviation of band intensity across the biological replicates and combine this with the reported value from the original study to simulate the original effect size. We will use this simulated effect size to determine the number of replicates necessary to reach a power of at least 80%. We will then perform additional replicates, if required, to ensure.