RankGOstatby Tim Beißbarth
beissbarth@wehi.edu.au
To submit Genes in Experiment and Ranks either upload a text file or paste into the text area:
File:
Format: Each gene id is followed by a number indicating differential expression, a p-value, rank, or a number that can be converted into a rank. Genes are separated by LINEBREAK or SEMICOLON, gene and rank are separtated by WHITESPACE or COMMA.
Text:
Available GO gene-association databases 
Details


Type of statistics:


Minimal length of considered GO paths:
e.g. "biological_process"=1, "biological_process%behavior"=2


Subset of GO hierarchy:
Limit search to subset of GO hierarchy that contains a keyword, e.g. "biological_process", "molecular_function", "cellular_component"


Maximal p-value in GO output list:


Maximum number of GOs/groups to display:


Show GOs with constistant LOW/HIGH ranks:


Cluster GOs:
-1 => do not cluster.
Merge GOs if indicating gene lists are inclusions or differ by less than # genes


Display Format:


Correct for multiple testing:

Find significant GOs in ranked gene list.

The input is a list of genes of a complete microarray or experiment with an accompanying number. The number specifies a rank or a p-value and indicates, for example, differential expression. If the number is not a rank, it is converted into one. The output is similar to GOstat. The Total represents the mean rank or mean input p-value. A P-Value for each GO is calculated

There is three different options for test statistics:
  1. (recommended) performs a Wilcoxon Signed Rank test. This test will determine whether the ranks for a particular GO are significantly higher or lower than usual.
  2. uses a Kolmogorov-Smirnov test to test whether the ranks of all genes associated with a GO follow uniform distribution, i.e. are randomly distributed within the complete list of genes or are biased towards low or high ranks/p-values. This approach is similar to the "Gene set enrichment analysis" described by Mootha et al (Nature Genetics, 2003). The main difference to 1 is that KS will test for any deviations from uniformity (i.e. also those GOs with ranks in the middle) while the Wilcoxon test only considders high or low ranks.
  3. you may do a Kolmogorov-Smirnov test based on p-values, in this case the input has to be p-values and is left as it is. In this case it is assumed that p-values are usually uniformly distributed between 0 and 1 and Kolmogorov-Smirnov tests are performed to find GOs where the distribution of p-values differs from this. However, care should be taken with this option as results of significance tests on microarray data do not often fulfill the requirement of producing uniformly distributed p-values, but may be biased towards 0 or 1.