Gene Logic offers a wide range of statistical and bioinformatic analyses of microarray data from basic quality control and changing gene analysis to extended pathway and mechanistic studies. We start with a detailed review of your progam and goals and then assist with the most appropriate study design to meet your needs. We provide three classes of bioinformatics analysis: basic, extended and custom.

Basic Statistical Analysis is included with microarray processing and consists of the following:

1. RNA and Microarray Quality Control data.
2. MAS 5.0 output of the GCOS Server.
3. Functional Gene Annotations
4. Summary statistics for each treatment group
5. Multiple pairwise comparisons of principal interest using fold change and t-test derived p-values.

Advanced Statistical Analysis is optional on a per study basis. The analysis is performed such that an experienced statistician specializing in microarray data analysis actively interrogates the data and provides robust and valid measures of gene regulation, principal data trends, and outlier analysis.

Advanced Biological Analyses are optional on a per study basis and provide results from the following:

1. Pathway Prioritizer™ Tool: For each comparison of interest, an algorithm will yield pathways that are most coordinately regulated. These pathways are rank ordered and a level of significance is reported for each pathway's regulation provile
2. Similarity Analysis (i.e., clustering, etc.): Across study, gene set(s) will be identified that answer a biological question by virtue of their regulation and/or pattern of expression. A variety of unsupervised techniques, according to specific situation and statistician’s assessment, are applied.
3. Ontology Enrichment: For identified gene set(s), a prioritization of Gene Ontologies based on enrichment of set relative to random baseline is provided.

Custom Analyses are designed with the customer and are focused on analysis and interpretation of the biological implications of the studies in question. Custom analyses could include:

1. Biological Interpretation of Results
2. Predictive Modeling
3. Comparisons with Gene Logic's Reference Databases