Qlucore on journal front covers

Plots and figures originating from research performed using Qlucore Omics Explorer have recently featured on the front covers of two journals; Cancer Discovery and the American Journal of Transplantation.

At Qlucore we are not surprised by this as visualization is extremely powerful in communicating results, and PCA is a method that aids researchers to get a better understanding of their data.

What is PCA?

The very high dimensional nature of many data sets makes direct visualization impossible since we humans can comprehend only three dimensions. The solution is to work with data dimension reduction techniques.

When reducing the dimensions of your data, it’s important not to lose more information than is necessary. The variation in a data set can be seen as representing the information that we would like to keep. Principal Component Analysis (PCA) is a well-established mathematical technique for reducing the dimensionality of data , whilst keeping as much variation as possible. 

PCA achieves dimension reduction by creating new, artificial variables called principal components. Each principal component is a linear combination of the observed variables.

PCA is an unsupervised method, meaning that no information about groups is used in the dimension reduction. This means that PCA will show a visual representation of the dominating patterns in the data set.

'Based on a principal component analysis supplemented with straightforward statistical evaluation methods, Qlucore is an excellent choice for clustering and comparative studies of large-scale transcript profiling data.” says- Csaba Konz, PhD, Max-Planck Institute, Germany.

By calculating, for example, the first three principal components, and visualizing the samples in this three-dimensional space, we have created a visualization containing more of the variance in the original data than any other trio of linear combinations, so in this sense PCA provides the optimal three-dimensional sample representation.

One of the keys behind the success of PCA is that in addition to the low-dimensional sample representation, it also provides a synchronized low-dimensional representation of the variables. The synchronized sample and variable representations provide a way to visually find variables that are characteristic of a group of samples.

Cancer Discovery

http://cancerdiscovery.aacrjournals.org/content/3/6.cover-expansion

the American Journal of Transplantation 

http://onlinelibrary.wiley.com/doi/10.1111/ajt.v13.5/issuetoc