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Genome Biol. 2019 May 6;20(1):88. doi: 10.1186/s13059-019-1681-8.

SCRABBLE: single-cell RNA-seq imputation constrained by bulk RNA-seq data.

Peng T1, Zhu Q2, Yin P3, Tan K4,5,6,7,8,9.

Author information

1
Division of Oncology and Center for Childhood Cancer Research, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA.
2
Graduate Group in Genomics and Computational Biology, University of Pennsylvania, Philadelphia, PA, 19104, USA.
3
Department of Mathematics, University of California, Los Angeles, CA, 90095, USA.
4
Division of Oncology and Center for Childhood Cancer Research, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA. tank1@email.chop.edu.
5
Graduate Group in Genomics and Computational Biology, University of Pennsylvania, Philadelphia, PA, 19104, USA. tank1@email.chop.edu.
6
Department of Biomedical and Health Informatics, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA. tank1@email.chop.edu.
7
Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA. tank1@email.chop.edu.
8
Department of Cell and Developmental Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA. tank1@email.chop.edu.
9
Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA. tank1@email.chop.edu.

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.

KEYWORDS:

Imputation; Matrix regularization; Optimization; Single-cell RNA-seq

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