Do you expect there to be potentially relevant clusters in your data? In the cluster analysis, we utilize supervised and unsupervised clustering methods to find the clusters in your data to identify potential sub-groups or sub-studies. Then we run the individual analysis with and without cluster-defined grouping/separation of your data. This allows you to address your data on several levels.
Most diseases are heterogeneous, which can be addressed to some level by clustering if sub-groups are affecting the measurement or quantifications at a high enough level. Response treatment has also been shown heterogeneous in several different drug-studies, and clustering can help to address this.
If you like, we can also implement data analysis with bioinformatic annotations of the tendencies found in your data. And you will, of course, get all features from our Basic Data Analysis in the Clustering Analysis.
3 - 4 weeks. Fast delivery available.
Machine learning is utilized to find outliers, visualize data, and bioinformatics. The latter is performed using the DAVID and Reactome database.