A multi-dimentional gene ranking tool

     Currently, we have collected and curated genetic studies for schizophrenia from six major categories listed as following. Genes in these datasets are initially scored by category-specific scoring methods, which are also described in the following link.

• association studies
• linkage analyses
• gene expression
• literature search
• Gene Ontology annotation
• gene networkn

     In our recent work, we developed a multi-dimensional evidence-based candidate gene prioritization approach for complex diseases. We extended the algorithm in our online gene ranking tool by integrating 6 data sources. In this algorithm, we searched an optimal weight matrix that weighs the score in each data category differently. Here, we provide two options for gene ranking. The first one is based on the weight scheme that we recommended in our paper, thus each weight is within certain limitation. The second option is custom weight scheme. You may choose any weight for each data category based on your prior knowledge or special interest. The combined scores are then calculated by:

    We provide two graphical presentations of the ranking results. The first one is to show the rank positions of the core genes among all the ranked candidate genes (Fig. 1A) . We use core genes (described here) for this purpose. Assuming that the core genes may have better evidence than other candidate genes, an efficient weight matrix is expected to rank the core genes, or their majority, on top of all candidate genes. The second one is a comparative distribution of the scores of the core genes and all genes (Fig. 1B). Genes are separated into different bins by their scores (e.g., 1-2, 2-3). An ideal distribution is that most of the core genes are ranked on the top while only few are ranked in the middle or at the bottom.