Detect influential points of feature rankings

Background Deriving feature rankings is essential in bioinformatics studies since the ordered features are important in guiding subsequent research. Feature rankings may be distorted by influential points (IP), but such effects are rarely mentioned in previous studies. This study aimed to investigate the impact of IPs on feature rankings and propose a new method to detect IPs. Method The present study utilized a case-deletion (i.e., leave-one-out) approach to assess the impact of cases. The influence of a case was measured by comparing the rank changes before and after the deletion of that case. We proposed a rank comparison method using adaptive top-prioritized weights that highlighted the rank changes of the top-ranked features. The weights were adjustable to the distribution of rank changes. Results Potential IPs could be observed in several datasets. The presence of IPs could significantly alter the results of the following analysis (e.g., enriched pathways), suggesting the necessity of IPs detection when deriving feature rankings. Compared with existing methods, the novel rank comparison method could identify rank changes of important (top-ranked) features because of employing the adaptive weights adjusted to the distribution of rank changes. Conclusions IPs detection should be routinely performed when deriving feature rankings. The new method for IPs detection exhibited favorable features compared with existing methods.