Qlucore Omics Explorer 2.0
- Qlucore Omics Explorer (OE) allows very fast data analysis
- OE works in full real time with 3D presentation of all data
- OE supports the user to explore the data by changing filters and parameters with only one mouse click
- OE support hieararchical clustering and heatmap plots
- OE supports Dynamic Principal Component Analysis (PCA) and generates plots faster than any other tool
- OE only requires a normal PC to handle huge data sets (more than 100 million entries) in real time
System Requirements
Omics Explorer requires:
- Windows 2000, Windows XP or Windows Vista
- 512 MB of RAM memory
Omics Explorer takes full advantage of processors with multiple cores and computers with multiple processors.
Main functionality
- Analyze and explore data set by a combination of visualization and intuitive filters
- Generate results with false discovery rates(q-value), fold change and p-values
- Perform hierarchical clustering and generate dynamic heatmap plots
- Instantly create Principal Component Analysis PCA plots of large data sets on an ordinary PC
- Use any of several methods, Hierarchical clustering, PCA, ISOMAP and or graphs to better understand data
- Easily work with paired data
- Remove unwanted dependencies such as artefacts and outliers
- Drill down into you data set by selecting which part of data to analyze and combine data with annotations
- Change parameters at any time with a single mouse click, and automatically get an updated plot immediately (in <0.1s)
- A simple and intuitive user interface
- Keep track of your work with powerful global log and restore function
Output
- High quality 3-D graphics
- 4 plot types: Heatmap, sample PCA, variable PCA and scatter plot.
- Data table view.
- Plot both samples and variables
- Synchronized plots
- Variable lists with p-values, fold change and FDR (q-values) values
- Plot arbitrary principal components
- Color the samples and the variables through different methods
- Label the samples and the variables through different methods
Selection
- Work with subsets of samples and variables
- Select samples based on clinical variables and other annotations
- Select variables based on variance, F-test(ANOVA), t-test, rank correlation, correlation coefficients, annotation text searches and imported variable lists (such as pathways)
- Select variables based on linear or quibic regression
- Study part of data set based on imported variable lists (such as pathways)
Editing of data
- Interactive editing of sample annotations
- Interactive editing of variable lists
Verification
- Get direct feedback on p-values and q-values during variable selection
- Verify results by redoing the analysis with permuted sample annotations or with random numbers
- Verify results through remove-one-at-a-time cross validation or several at the time cross validation
Clusters and networks
- Visualize sample clusters by connecting each sample with its nearest neighbours
- Visualize variable clusters by connecting correlated variables
Import
- Data files (both standard .gedata and also transposed data files)
- Variable lists
- Annotation files
- Direct NetAffx import
- Affymetrix CHP and ARR files
Export
- Still images (plots)
- Data files
- Variable lists (with annoations and data if so preferred)
- Videos
Other
- Missing value reconstruction (two versions)
- Variable normalization
- Multi-dimensional rescaling
- Isomap
- Take logarithm of data