Abstract
BACKGROUND: Single-cell RNA-sequencing (scRNA-seq) is becoming indispensable in the study of cell-specific transcriptomes. However, in scRNA-seq techniques, only a small fraction of the genes are captured due to "dropout" events. These dropout events require intensive treatment when analyzing scRNA-seq data. For example, imputation tools have been proposed to estimate dropout events and de-noise data. The performance of these imputation tools are often evaluated, or fine-tuned, using various clustering criteria based on ground-truth cell subgroup labels. This limits their effectiveness in the cases where we lack cell subgroup knowledge. We consider an alternative strategy which requires the imputation to follow a "self-consistency" principle; that is, the imputation process is to refine its results until there is no internal inconsistency or dropouts from the data. RESULTS: We propose the use of "self-consistency" as a main criteria in performing imputation. To demonstrate this principle we devised I-Impute, a "self-consistent" method, to impute scRNA-seq data. I-Impute optimizes continuous similarities and dropout probabilities, in iterative refinements until a self-consistent imputation is reached. On the in silico data sets, I-Impute exhibited the highest Pearson correlations for different dropout rates consistently compared with the state-of-art methods SAVER and scImpute. Furthermore, we collected three wetlab datasets, mouse bladder cells dataset, embryonic stem cells dataset, and aortic leukocyte cells dataset, to evaluate the tools. I-Impute exhibited feasible cell subpopulation discovery efficacy on all the three datasets. It achieves the highest clustering accuracy compared with SAVER and scImpute. CONCLUSIONS: A strategy based on "self-consistency", captured through our method, I-Impute, gave imputation results better than the state-of-the-art tools. Source code of I-Impute can be accessed at https://github.com/xikanfeng2/I-Impute .