Abstract
Single-cell RNA-seq data contain a large proportion of zeros for expressed genes. Such dropout events present a fundamental challenge for various types of data analyses. Here, we describe the SCRABBLE algorithm to address this problem. SCRABBLE leverages bulk data as a constraint and reduces unwanted bias towards expressed genes during imputation. Using both simulation and several types of experimental data, we demonstrate that SCRABBLE outperforms the existing methods in recovering dropout events, capturing true distribution of gene expression across cells, and preserving gene-gene relationship and cell-cell relationship in the data.
Highlights
Single-cell RNA sequencing has revolutionized cell biology, enabling studies of heterogeneity and transcriptome dynamics of complex tissues at single-cell resolution
SCRABBLE is based on the mathematical framework of matrix regularization [8]
We developed a convex optimization algorithm to minimize the objective function
Summary
Single-cell RNA sequencing (scRNA-seq) has revolutionized cell biology, enabling studies of heterogeneity and transcriptome dynamics of complex tissues at single-cell resolution. A major limitation of scRNA-seq data is the low capturing and sequencing efficiency affecting each cell, resulting in a large proportion of expressed genes with zeros or low read counts, which is known as the “dropout” phenomenon. Such dropout events lead to bias in downstream analysis, such as clustering, classification, differential expression analysis, and pseudo-time analysis. The second approach is direct imputation of scRNA-seq data Among these methods, MAGIC imputes dropout events by data diffusion based on a Markov transition matrix that defines a kernel distance measure among
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