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 DeCyder Extended Data Analysis (EDA)
Analysis workflow in DeCyder EDA.
- Offers advanced statistical analysis in a simple-to-use format·
- Employs multivariate analysis and sophisticated clustering methods to uncover patterns in expression data and relationships.
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- Understand regulatory pathwayso
- Identify proteins that discriminate between disease stages, tumor types or other sample subtypes
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DeCyder Extended Data Analysis (EDA) software extends the statistical options offered in DeCyder 2-D Differential Analysis Software.
DeCyder™ Extended Data Analysis (DeCyder EDA) is high-performance informatics software for the analysis of large and combined proteomics data sets. This software enables the seamless integration of DeCyder EDA statistical results with DeCyder 2D visualization data (Fig 1), thereby placing the results into biological context by linking to internal and external databases.
DeCyder EDA offers advanced statistical analysis in a simple-to-use format, uncovering patterns in expression data and relationships using multivariate analysis and sophisticated clustering methods. DeCyder EDA can be used to answer questions remaining from DeCyder 2-D Biological Variation Analysis (BVA), such as:
- How many groups or classes exist in a given data set?
- Are there proteins or spots that behave similarly to a given protein or spot (i.e. co-regulation)?
- Are there proteins that might be used for the development of noninvasive tests (i.e. diagnostic markers)?
- Are there proteins or protein patterns that might be characteristic of a biological state (e.g. tumor versus normal tissue)?
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The software helps you understand regulatory pathways, find protein spots with similar expression profiles, and group your samples according to common expression patterns. It identifies proteins that discriminate between disease stages, tumor types, or other sample subtypes-giving your Ettan™ DIGE results clarity and biological relevance.
The DeCyder EDA analysis workflow consists of the following steps (Fig 1):
- Setup-creates a base set after importing data from BVA workspaces. Multiple workspaces can be compared with each other. This is the starting point for further analysis.
- Calculations- setting up and performing calculations for a selected set of data
- Results-analyzing the results of the calculations.
- Interpretation-biological information and context from in-house or public databases are integrated for the proteins of interest found in the results of the calculations.
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Setup
DeCyder EDA uses a set of data for analysis. A set is a group of spot maps with matched spots, i.e. a group of spot maps and proteins. A set of data can be displayed in several ways depending on the context, for example as a heat map where each row represents a protein and each column represents a spot map.
Differential Expression Analysis
 Differential expression analysis, such as Student's t-test and Analysis Of Variance (ANOVA), can be used to find significantly expressed proteins and to reduce the data set, for example limiting it to proteins that show changes in expression level (Fig 2.)
Fig 2: Results from Differential Expression Analysis calculations.
Principal Components Analysis
 Principal Components Analysis (PCA) reduces the dimensionality of a data set by defining principal components that describe a percentage of the total variance of the data. The first principal component will describe the greatest amount of variance of the data, the second principal component the second greatest, and so on.
Fig 3: Results from Principal Components Analysis calculations.
Pattern Analysis
 This process finds patterns in the expression profiles in the DeCyder EDA data without any prior information about the variables (Fig 4). The algorithms in DeCyder EDA can help in finding patterns in proteins, spot maps, and expression groups.
Fig 4: Results from Pattern Analysis calculations, with Hierarchical Cluster Analysis tab shown.
In pattern analysis, four types of unsupervised clustering can be applied:
- Hierarchical clustering, K-means clustering, Self-organizing and Gene shaving.
Discriminant Analysis
 Discriminant analysis identifies markers and creates classifiers for unknowns. This analysis also helps find proteins that might be useful for the development of noninvasive diagnostic tests (Fig 5).
Fig 5: Results from Discriminant Analysis calculations, with Classifier Creation tab shown.
Interpretation
Fig 6: Interpretation screen with a Gene Ontology query shown.
| Interpretation is a very powerful tool used to get biological information from different public or local databases regarding the proteins of interest. The results from the queries are displayed in a user-friendly way. Four different queries exist in DeCyder EDA:
- Gene Ontology
- UniProt features
- Pathways
- PubMed
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Technical Specifications
DeCyder Extended Data Analysis (EDA) Software (PDF) |
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