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

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