# On the Role of Clustering and Visualization Techniques in Gene Microarray Data
## 4 Conclusions  
Among machine learning methods, clustering is one of the most important instruments to study gene microarray data. However, a plethora of clustering techniques also exists specifically tailored for gene microarray data, thus making it difficult for a data scientist to effectively choose the best one for the particular problem at hand. Another effective tool for studying gene microarray data consists of data visualization techniques, which permit to extract useful information from large quantities of raw data. Biologists need a visual environment that facilitates exploring high-dimensional data dependent on many parameters. Indeed, the human eye and brain together constitute an amazing model recognition tool, but, for them to function, the data must be presented in a low-dimensional space, commonly two or three dimensions. Even a very simple association may appear very vague when the information is displayed in tabular type, but visual testing often makes it very easy to see. Nonetheless, as it emerges from early discussions, molecular data are complex, and it calls for tackling several sub-problems in order to develop effective tools for data analysis, e.g., effective preprocessing  
Figure 1: Latent variable responsibilities onto the spherical latent space. Note how the red areas correspond to higher density locations. Figure 2: Visualization of resulting clusters from a hierarchical approach. techniques for filtering out noise from gene expression data, feature selection methods to find important features and reduce the very high dimensionality of molecular data. Furthermore, there is a huge effort by researchers to develop different clustering strategies to make effective their approaches in a variety of heterogeneous data sets rather than for an ad-hoc case study. Unfortunately, we are still far from discovering \"The Master Algorithm\" [72] able to effectively address whatever problem at hand and to solve it, and that's why the literature on the topic is endless. However, some interesting lines could be drawn. Firstly, from an analytical point of view, one of the main difficulties in analyzing gene expression data are how to tackle the very high dimension of data sets, which leads to both the curse of dimensionality issue in addition to making it complex to properly assess the relative weight of each feature in determining the evolution of clusters. Visualization techniques suffer very high dimensions because the loss of information when compressing data to two or three dimensions. While feature selection could aid in reducing the size of parameters, it could be more beneficial to analyse in a systematical and formal way the dimensionality of data sets by means of techniques to study the intrinsic dimension of data [73], consisting of dimensionality reduction methods that aim at projecting the original data set of dimensionality \(N\), without information loss, onto a lower M-dimensional submanifold. Since the value of \(M\) is unknown, techniques that allow knowing in advance the value of M, called intrinsic dimension estimation, might help. Secondly, complete framework for data analysis, encompassing preprocessing, feature selection, clustering and visualization methods might be of a certain amount of help, thus an increasing number of such works, as described in [74; 75] have been proposed in recent years. In more detail, in [74], a computational method that uses a spatial reference map for inferring the spatial location of cells, in the context of complex and heterogeneous tissues, is proposed, whereas, in [75], has been developed a scalable toolkit for analyzing single-cell gene expression data, comprising techniques for preprocessing, visualization, clustering, trajectory inference, differential expression testing, and simulation of gene regulatory networks.