A novel f-divergence based generative adversarial imputation method for scRNA-seq data analysis.

A novel f-divergence based generative adversarial imputation method for scRNA-seq data analysis.

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Comprehensive analysis of single-cell RNA sequencing (scRNA-seq) data can enhance our understanding of cellular diversity and aid in the development of personalized therapies for individuals.The abundance of missing values, known as dropouts, makes the analysis of scRNA-seq data a challenging task.Most traditional methods made assumptions about specific distributions for missing values, which limit their capability to capture the intricacy of high-dimensional scRNA-seq data.

Moreover, the imputation performance of traditional methods decreases with higher missing rates.We propose a novel f-divergence based generative adversarial imputation method, called sc-fGAIN, for the scRNA-seq data imputation.Our studies identify four f-divergence functions, namely cross-entropy, Kullback-Leibler (KL), reverse ugg ultra mini grade school KL, and Jensen-Shannon, that can be effectively integrated with the generative adversarial imputation network to generate imputed values without any assumptions, and mathematically prove that the distribution of imputed data using sc-fGAIN algorithm is same as the distribution of original data.

Real quick sling heater mount scRNA-seq data analysis has shown that, compared to many traditional methods, the imputed values generated by sc-fGAIN algorithm have a smaller root-mean-square error, and it is robust to varying missing rates, moreover, it can reduce imputation variability.The flexibility offered by the f-divergence allows the sc-fGAIN method to accommodate various types of data, making it a more universal approach for imputing missing values of scRNA-seq data.