Integrative Analysis of Integrative Analysis of Biological DataBiological Data

Sai MoturuSai Moturu

MAGICMAGIC

Multisource Association of Genes by Integration of Clusters

Goal: Integrate heterogeneous types of high-throughput data for accurate gene function prediction

Bayesian reasoning Incorporates expert knowledge Yeast Data

Integrative analysis ! Why ??Integrative analysis ! Why ??

High throughput methods sacrifice High throughput methods sacrifice specificity for scalespecificity for scale

Microarray data alone is good for Microarray data alone is good for hypothesis generation but lacks hypothesis generation but lacks specificity for accurate gene function specificity for accurate gene function predictionprediction

By using heterogeneous functional By using heterogeneous functional data, the prediction accuracy is data, the prediction accuracy is improvedimproved

Need for MAGICNeed for MAGIC

Studies have combined different Studies have combined different types of data in a heuristic fashion on types of data in a heuristic fashion on a case by case basisa case by case basis

No general scheme or probabilistic No general scheme or probabilistic representation is appliedrepresentation is applied

Methods for combination of specific Methods for combination of specific datadata

MAGIC – general method to integrate MAGIC – general method to integrate disparate data sourcesdisparate data sources

Input to MAGICInput to MAGIC

Input: Gene-Gene relation matrices for Input: Gene-Gene relation matrices for each data sourceeach data source

The elements of the matrix are scores The elements of the matrix are scores that indicate whether there could be that indicate whether there could be relationship between two genesrelationship between two genes

The score can be binary, discrete or The score can be binary, discrete or continuouscontinuous

Input format is flexible and allows Input format is flexible and allows genes to be in more than one group or genes to be in more than one group or clustercluster

Thus does not exclude biclustering or Thus does not exclude biclustering or fuzzy clustering methodsfuzzy clustering methods

Structure of the MAGIC Structure of the MAGIC Bayesian networkBayesian network

Prior probabilities assessed by Prior probabilities assessed by expertsexperts

EvaluationEvaluation

No gold standard for gene groupings No gold standard for gene groupings existsexists

GO is the best available reflection of GO is the best available reflection of current biological knowledgecurrent biological knowledge

Use a cutoff of 3 levels in the Use a cutoff of 3 levels in the hierarchical structure to say that to hierarchical structure to say that to genes are functionally relatedgenes are functionally related

ResultsResults

ResultsResults

AVIDAVID

Annotation Via Integration of Data

Integrates data to build high-Integrates data to build high-confidence networks in which proteins confidence networks in which proteins are connected if they are likely to are connected if they are likely to share a common annotationshare a common annotation

AVID predictions functional AVID predictions functional annotation in all three GO categoriesannotation in all three GO categories

AVID stagesAVID stages

AVID resultsAVID results

AVID resultsAVID results