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EinImpute: a local and gene-based approach to imputation of dropout events in ScRNA-seq data

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Abstract

Single cell RNA sequencing (scRNA-seq) technology is recognized as a new technology that enables the analysis of the structure of every cell in different tissues with high resolution. Typically, the results of the scRNA-seq experiment consist of a large volume of randomly-generated data with biological and technical noise. Dropout events are an example of these noises where no RNA is measured for a gene that is truly expressed in the cell. This causes non-zero entries to appear as zero in the gene expression matrix. This paper presents a novel method to imputation of dropout events that improves the final results of visualizing and identifying cell populations in scRNA-seq data. The proposed method, which we called EinImpute, is a local method that first clusters the cells. Next, independently in each cluster, a KNN graph of genes is generated that discovers potential trajectories between genes. Finally, using these gene networks and linear regression models, data reconstruction is performed and the dropout events are estimated. The overall improvement rate obtained by EinImpute based on different clustering methods was 16.57% on average. Among other imputation methods, DrImpute ranked the second with an improvement rate of 4.04%. Also, the visualization results using tSNE show that the EinImpute compared to other well-known imputation method can represent cells with a far better quality through visualization techniques.

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Authors and Affiliations

  1. Department of Computer Engineering, Dezful Branch, Islamic Azad University, Dezful, Iran

    Amin Einipour & Mohammad Mosleh

  2. Department of Computer, Andimeshk Branch, Islamic Azad University, Andimeshk, Iran

    Amin Einipour

  3. Department of Electrical Engineering, Faculty of Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran

    Karim Ansari-Asl

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  1. Amin Einipour
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  2. Mohammad Mosleh
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  3. Karim Ansari-Asl
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Correspondence to Mohammad Mosleh.

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Einipour, A., Mosleh, M. & Ansari-Asl, K. EinImpute: a local and gene-based approach to imputation of dropout events in ScRNA-seq data. J Ambient Intell Human Comput 14, 3225–3237 (2023). https://doi.org/10.1007/s12652-021-03463-8

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  • DOI: https://doi.org/10.1007/s12652-021-03463-8

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