This tool estimates the False Discovery Rate (FDR) for 2X2 Contingency Tables, based on Fischer statistics. When testing a large number of hypotheses, it can be helpful to estimate or control the false discovery rate (FDR), the expected proportion of tests called significant that are truly null. We use Fisher's exact test to calculate the exact null distribution over a set of 2X2 contingency tables. Using these statistics we compute pooled pvalues, estimate the ration of true nulls, and compute qvalues. The currently installed version is V2 RC2. For detailed description of False Discovery Rate program and for literature references please consult the False Discovery Rate web page.

Calculations will be carried out on the BioHPC compute cluster at CBSU. You will receive e-mail notifications when the job is submitted, when it starts, and when it is finished. Output will be available via links embedded in the notification e-mails. For more information about this program and BioHPC interface in general, please visit our Frequently Asked Questions page.

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E-mail:   (only guests need to use this field, registered users should log in)

Enter job name:                                         

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INPUT DATA (Required):    paste upload copy

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Data properties (optional)

First row contains column headers 

Table counts start on column: 

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Operation properties (optional)

Filter irrelevant tables     Pi evaluation method:   Pi=1 sum (observed p-values) / (null p-values)

Compute positive FDR (pFDR) 

Use Sampling Sample Size (tables): 

                             Automated Sampling    d: 

Huge dataset mode 

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Output properties (optional)

Report progress while running 

Output all the computed statistics 

Output only tables with q-value less than: 

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Cluster: Autocbsum1cbsusrv05   ( Show timeout info )