We describe a system for meta-analysis where a wiki stores numerical data in a simple format and a web service performs the numerical computation. We initially apply the system on multiple metaanalyses of structural neuroimaging data results. The described system allows for mass meta-analysis, e.g., meta-analysis across multiple brain regions and multiple mental disorders.
We have previously explored a simple online meta-analysis system|a \ elded
wiki"|in connection with personality genetics [
Following Ward Cunningham's quote \What's the simplest thing that could possibly work?" we present a simple system that allows for mass meta-analysis of numerical data presented as comma-separated values (CSV) in a standard MediaWiki-based wiki, | the Brede Wiki: http://neuro.imm.dtu.dk/wiki/. 2
We use the MediaWiki-based Brede Wiki to represent the data [
Separate wiki pages store| rather than uploaded les| the CSV data, so the MediaWiki template functionality can transclude the CSV data on other wiki pages. By convention pages with CSV information have the \.csv" extension as part of the title so external scripts can recognize them as special pages and the wiki pages have no wiki markup. Fig. 1. Screenshot from the wiki showing CSV
MediaWiki templates may data transcluded on a page. generate links for download, editing and meta-analysis of the data. Presently, no controlled vocabulary beyond the template elds describes the columns in the CSV. To generate an appropriate content-type (text/csv) a bridging web script functions as a proxy, so a download of the CSV page can spawn a client-side spreadsheet program.
A few MediaWiki extensions can format CSV information: SimpleTable and TableData. Figure 1 shows the transclusion of CSV data with a modi ed version of the SimpleTable extension. The Brede Wiki uses the standard template system for recording structured bibliographic data about the publication and to annotate the CSV information, see Figure 2.
The bulk of the data cur- {{{{MMeettaaaannaallyyssiiss ccssvv begin}}
rently presented in the wiki || ttoiptilce1==MPajiotruitDaerpryessive Disorder Neuroimaging Database - Pituitary, total
cmoemtae-sanfarolymsisthoef lvaorgluemmetarsics {}{}||Metttooappaiincca32ly==siMMsaaNjcDosrvdepressive disorder
studies on major depressive || ttoiptilce1==OPbsietsusiitvaer-ycompulsive disorder Neuroimaging Database - Pituitary
sdeispoarrdaetre mreetpao-ratninaglyseosveforr i5n0- }{}{||Metttooappaiincca32ly==siOOsbbNscDessvsievned-}c}ompulsive disorder
dividual brain regions [
Further data comes from Fig. 2. Template to annotate the CSV data and
mass meta-analyses across de ne the links to the meta-analysis.
multiple brain regions on
bipolar disorder [
Apart from neuroimaging studies the Brede Wiki also records data from
meta-analyses from a few other studies outside neuroimaging [
The web script for meta-analysis reads the CSV information, identi es the required columns for meta-analysis, performs the statistical computations and makes meta-analytic plots| the so-called forest and funnel plots|in the SVG format, see Figure 3. From either the title information or a PubMed identi er the script generates back-links from the generated page to pages on the wiki. The script may also export the computed results as JSON or CSV. Furthermore, it may generate a small R script that sets up the data in variables and use the meta library for meta-analysis.
The web script attempts to guess the separator used on the CSV page and also tries to match the elements of the column header, e.g., the strings \control n", \controls number", \number of controls", etc. match for number of control subjects. With no matches the user needs to explicitly specify the relevant columns via URL parameters, which in turn a wiki template can setup.
Standard meta-analysis computes an e ect size from each result in a paper
and computes a combined meta-analytic e ect size and its con dence
interval. Although the methodological development continues, there exist established
statistical analysis approaches for ordinary meta-analysis [
By using MediaWiki in our present system we exploit the template facility to capture structured information, and free-form wikitext for annotation and comment on the individual scienti c papers, | as in semantic academic annotation wikis AcaWiki and WikiPapers. It is also possible to use the pages of the wiki as a simple means to keep track of the status of the papers considered for the meta-analysis: potentially eligible, eligible, partially entered and fully entered.
Why not Semantic MediaWiki? Semantic MediaWiki (SMW) may query text
and numerical data, though has not had the ability to make complex
computations. The Semantic Result Formats extension includes average, sum, product
and count result formats enabling simple computations of a series of numerical
values, but insu cient for the kind of computations we require. The data for
meta-analysis form a n-ary data record (mean, standard deviation, number of
subjects, labels) so either individual SMW pages should store each data record
or we should invoke the n-ary functionality in Semantic Internal Objects SMW
extension, SMW record or the recently-introduced subobject SMW
functionality. We have not investigated whether these tools provide convenient means for
representing our data. The Brede Wiki can export its ontologies de ned in
MediaWiki template to SKOS. Our future research can consider RDFication of the
CSV information through the SCOVO format [
We wrote the web service in Python, where Numpy makes vector computation available and Scipy provides statistical methods, necessary for the computation. In a future PHP implementation the script could more closely integrate with the wiki as either a MediaWiki or a SMW extension.
A wiki built from standard components Fig. 4. Results from mass meta-analyses shown in a provides a inexpensive L'Abbe-like plot and constructed by calling the web solution with means to script multiple times. Each dot corresponds to a metamanage meta-analytic analysis. Uncertainty as a function of e ect size with data in a collaborative size of each dot determined by the number of subjects. environment. The gen- The line indicates 0.05-signi cance. eral framework allows not only the meta-analysis of neuroimaging-derived data but has the potential for managing and analyzing data from many other domains.