Basic Data Analysis

Our basic data analysis services involve:

• Normalization
• Gene Expression
• Gene Table

Normalization:

Gene Expression Profile Before normalization After normalization

We use various normalization techniques to take care of technical and biological variations and set up the platform where genes can be easily compared. Lowess normalization (per spot per chip): It is used for two-channel microarrays. Variations in the labeling intensities lead to dye bias that may produce spurious results. On scatter plot, the data is curved and is away from 1 fold (central line). Lowess normalization merges and smooths two-channel data to reduce dye bias. Robust multi array average (RMA): it is used for comparison of gene expression on common platform in response to genetic and environmental differences across all the samples in a given set.

Gene expression:

To view changes in gene expression across samples from different experimental conditions such as infection type, treatment types, tissue types or time series. It permits quantization of  gene expression levels  along with a wide range of visualizations. Total values are shown under comparison.

Gene Table:

To view the detailed information (e.g. intensity values, fold change values, p values, gene ontology, gene names, annotations) for every gene in tabular form allowing convenient access to visualize complete data in the project.