Principal Component Analysis

Principal component analysis (PCA) is a multivariate approach for reducing the dimensionality of data. It consists of finding those orthogonal linear combinations of the multiple variables which could explain a good amount of variation in the data so that we can consider these principal components only, instead of redundant variables. PCA is a standard tool in modern data analysis and is used in diverse fields, from neuroscience to computer graphics, because it is a simple, non-parametric method for extracting relevant information from confusing data sets. At minimal cost, PCA provides a roadmap for reducing a complex data set to a lower dimension helping reveal the sometimes hidden, often simple underlying structures.

Services Offered

Jaivik Data offers exhaustive PCA of high-throughput data using latest techniques to help identify how different variables work together to create the dynamics of the system. We use PCA to reduce dimensionality, decrease redundancy and filter noise in the data, perform correction for population stratification in genome-wide association studies and to choose covariates to adjust in further statistical analysis.

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Cluster Analysis

The expression or methylation levels of thousands of genes can be measured simultaneously by DNA microarrays. Cluster analysis provides easy interpretation and inference of genome-wide data for biologists by using statistical algorithms that arrange genes according to pattern of gene expression or methylation. Then, cluster analysis partitions a given data set into groups based on similarity in data.

Services Offered

At Jaivik data, we offer cluster analysis of genome-wide expression or methylation microarray data using several different statistical algorithms such as k-means, hierarchical, PAM, Agnes, Diana and visualization of clustering results using graphics in a user friendly and intuitive way.

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Gene-Set Enrichment & Pathway Analysis

Gene-set enrichment analysis is a useful technique to help functionally characterize large gene lists, such as the results of gene expression experiments. This technique finds functionally coherent gene-sets, such as pathways, that are statistically over-represented in a given gene list.

Services Offered

Our services include identification of pathways, molecular functions and biological processes that are statistically over represented in a given gene lists, finding disease associations to the gene-sets with statistical significance, performing network enrichment analysis and prediction of canonical pathways and top biological functions associated with the gene-sets.

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