Qlucore is a software platform for the analysis of genomics, proteomics and related data. As with most statistical and genomics software, it generates an immediate graphic for most analyses. Its specific areas of use include:
- gene expression
- protein arrays
- DNA methylation
- pattern and structure identification in multivariate data
With the software, ease-of-use, as well as speed of computation allows rapid analysis of genetic data sets interactively in real time. Statistical tools include the F-test, False Discovery Rate, and Principal Components Analysis (PCA) for group separations analytically and graphically. No in-depth knowledge of math or statistics is expected (but it certainly helps!).
As it is vital to import data as a first step and align it properly with gene sequence on the chips, importation of a wide variety of data formats is essential.
Qlucore imports a number of data types. These fall under three broad categories:
1. Import and normalization of platform dependent “raw” data.
(e.g. Affymetrix gene expression arrays, Agilent mRNA and microRNA arrays and RNA-seq data from BAM files.)
2. Platform independent import of normalized data from a text based file (.txt, .csv and .tsv).
3. Direct download from the Gene Expression Omnibus (GEO).
As the tutorial tells us, data can be generated from many sources:
- gene expression: microarrays, real-time PCR, RNA-seq
- microRNA: microarrays,
- DNA methylation: microarrays
- protein expression: microarrays, antibody arrays, 2-D gels, LC-MS data
- image analysis data
Figure 1 displays the main window and the ‘Getting Started’ and ‘Statistics windows. In the middle, you find the Work Space where all plots will be displayed in Plot windows. There are also several Dock Windows. By default the Samples, Variables and Log dock windows are docked to the left of the main window and the Statistics and Getting Started windows are floating.
The first step suggested is to hit File/Restore Default settings. This will start the next analysis at the settings of the present analysis once you are finished and exit the program. Next, we import some data with an easy load; the getdata set is already in the program so next we hit Help> Example Files> Qlucore Test DataSet.gedata. The main screen now instantly displays the PCA separation of the 12 samples (two must be rather close together) of 50 measurements each. The individual measurements for each sample may be viewed by selecting Methods/Mode/Variable in the window next to the toolbox. The sample points have been pre-colored to enhance the visualization. Although the PCA is the default plot, you can quickly produce heatmaps, box charts, histograms and scatter plots. Data tables are also chosen here.
The samples are colored by Treatment however, in the View/Color tab they may be re-colored by age, gender or sample ID. By selecting the move button in the Toolbox, the user can left-click on a PCA graph and rotate it along any axis. This is particularly useful for 3-D graphics. Other buttons allow the graphics to be cleared of labels and other markings and also multiple sample selection.
To begin our statistical analysis with some visual aids, we will make a heatmap and cluster analysis with very few commands. Figure 3 displays the results of a simple test. Clusters may then be colored and labeled.
We will next do the statistics be using variable sliders to select p value cutoffs, q-values (false discovery rate), and fold changes for any label group.
There are many other tests and graphics that may be easily generated, and the interested reader should access the Qlucore website (www.qlucore.com) for further information.
More information is available through the written tutorials, as well as the Reference Manual under the Help section. On-line documentation, as well as a tech support, are also very useful. For those just getting into statistics, an ‘Introduction to Statistical Concepts’ is presented in the appendix of the tutorial. Qlucore also gives live webinars which the novice will find useful.
There are two major advantages of the way the logical flow of analyses are constructed that the genomicist will find extremely helpful:
- The software is very efficient at uncovering hidden structures and finding patterns in large data sets.
- Through functionality, such as the inbuilt Gene Ontology browser, the inbuilt Gene Set Enrichment analysis workbench, and the direct download of Gene Expression Omnibus data, you can assess and explore the data from a systems point of view. This is vital as to actually design a working drug, as the experimenter has to know a lot more than merely the genome data.
As will all software that I enjoy reviewing, Qlucore Omics Explorer has a very gentle learning curve. Download a free 15-day trial version and experiment for yourself!
- Yearly license, single seat commercial (includes free tech support)
- E-mail/call for pricing.
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New York, NY 10022
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John Wass is a statistician based in Chicago, IL. He may be reached at editor@ScientificComputing.com.