A critical step for gene selection algorithms using rough set theory is the establishment of a gene evaluation function to assess the classification ability of candidate gene subsets. The concept of dependency in a classic neighborhood rough set model plays the role of this evaluation function. This criterion only notes the information provided by the lower approximation and omits the upper approximation, which may result in the loss of some important information. This paper proposes gene selection algorithms within a single-cell gene decision space by employing self-information, taking into account both lower and upper approximations. Initially, the distance between gene expression values within each subspace is defined to establish the tolerance relation on the cell set. Subsequently, self-information is introduced through the lens of tolerance classes. The relationship between these measures and their respective properties is then examined in detail. For gene expression data, the proposed self-information metric demonstrates superiority over other measures by accounting for both lower and upper approximations, thereby facilitating the selection of optimal gene subsets. Finally, gene selection algorithms within a single-cell gene decision space are developed based on the proposed self-information metric, and experiments conducted on 10 publicly available single-cell datasets indicate that the classification performance of the proposed algorithms can be enhanced through the selection of genes pertinent to classification. The results demonstrate that $Fi-SI$ achieves an average classification accuracy of 93.7% (KNN) while selecting 48.3% fewer genes than Fisher’s score.