
#### With this CrossICC's Shiny APP:  

* Visulize clustering output in vary plot funtions.
* Download matrix and plot directly by a simple clik.
* Explore analysis parameters and performance applied in the clustering.
* Do further investigation in extra test dataset.
* Perfrom correlation analysis, ssGSEA, survival analysis with subtypes. 

#### Introduction  

Cancer subtyping and prognosis have been studied extensively by various molecular profiling, such as genes, DNA methylation markers, proteins. With the data, unsupervised clustering are proven to be a well-established methodology for this purpose. However, most of clustering method requires users to define the optimal number of clusters and usually cancer subtypes resulting from a single dataset can not be replicated in external dataset. To address this issue, we present CrossICC, that implemented a previous published iterater-based clustering algorithm and also add a new strategy to improve the performance of the method. Particularly, [CrossICC](https://github.com/bioinformatist/CrossICC) utilizes an iterative strategy to derive the optimal gene set and cluster number from consensus similarity matrix generated by consensus clustering. [CrossICC](https://github.com/bioinformatist/CrossICC) is able to deal with multiple cross platform datasets so that requires no between-dataset normalizations. This package also provides abundant functions to help users visualize the identified subtypes and evaluate the subtyping performance. Specially, many cancer-related analysis methods are embedded to facilitate the clinical translation of the identified cancer subtypes.

#### Three analysis modules 
This shiny-based APP was implemented as a web application to help inteprate the object by `CrossICC` main functions from the package, and it also provided interactive ways to perform downstream analysis of cluster result. Briefly, the reporter consists of three main modules below,including CrossICC result viewer, new sample allocator and cancer related analysis module. 


