A correlation based matrix completion approach to gene expression prediction

Finding candidate genes that could cause specific diseases has been the subject of many studies. This is an important research task, however in the biological experimentation domain it can be very expensive and time consuming. So an alternative way is to find gene expression values from partial measurements and try to predict the rest. By using computational methods, we can statistically estimate these relationships faster and in a more efficient way, providing domain experts suggestions on what exploration of likely relationships they should be focusing. One common computational approach is to model the gene expression data as a matrix (where each row represents a gene and each column a subject); the entries of the matrix can then be mRNA measurements that show the extent of gene expressions. Since entries of the dataset are based on partial measurements, the dataset has missing values, and the problem is then to estimate the missing values and thus to recover the global matrix based on the known values. In this paper, we present a correlation based approach to the matrix completion task (CMC) and discuss its functionality. The CMC based algorithm is then compared to a state-of-the-art nuclear-norm minimization iterative algorithm. Our results show that the CMC based algorithm significantly outperforms the iterative algorithm and even shows a better tendency when the amount of missing values grow. We argue that the CMC based algorithm can provide good estimates for missing values, possibly guiding time consuming biological gene expression profiling as to which values should be confirmed. Although our primary focus is on gene expression prediction, the strategy discussed is applicable to any highly correlated dataset where missing values need to be estimated/recovered.