SCRABBLE: single-cell RNA-seq imputation constrained by bulk RNA-seq data
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 reduce...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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BMC
2019-05-01
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| Series: | Genome Biology |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s13059-019-1681-8 |
| _version_ | 1828389564788506624 |
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| author | Tao Peng Qin Zhu Penghang Yin Kai Tan |
| author_facet | Tao Peng Qin Zhu Penghang Yin Kai Tan |
| author_sort | Tao Peng |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-10T06:31:18Z |
| format | Article |
| id | doaj.art-2f48186683fe472db2bc3f340e7b68f9 |
| institution | Directory Open Access Journal |
| issn | 1474-760X |
| language | English |
| last_indexed | 2024-12-10T06:31:18Z |
| publishDate | 2019-05-01 |
| publisher | BMC |
| record_format | Article |
| series | Genome Biology |
| spelling | doaj.art-2f48186683fe472db2bc3f340e7b68f92022-12-22T01:59:04ZengBMCGenome Biology1474-760X2019-05-0120111210.1186/s13059-019-1681-8SCRABBLE: single-cell RNA-seq imputation constrained by bulk RNA-seq dataTao Peng0Qin Zhu1Penghang Yin2Kai Tan3Division of Oncology and Center for Childhood Cancer Research, Children’s Hospital of PhiladelphiaGraduate Group in Genomics and Computational Biology, University of PennsylvaniaDepartment of Mathematics, University of CaliforniaDivision of Oncology and Center for Childhood Cancer Research, Children’s Hospital of PhiladelphiaAbstract 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.http://link.springer.com/article/10.1186/s13059-019-1681-8Single-cell RNA-seqImputationMatrix regularizationOptimization |
| spellingShingle | Tao Peng Qin Zhu Penghang Yin Kai Tan SCRABBLE: single-cell RNA-seq imputation constrained by bulk RNA-seq data Genome Biology Single-cell RNA-seq Imputation Matrix regularization Optimization |
| title | SCRABBLE: single-cell RNA-seq imputation constrained by bulk RNA-seq data |
| title_full | SCRABBLE: single-cell RNA-seq imputation constrained by bulk RNA-seq data |
| title_fullStr | SCRABBLE: single-cell RNA-seq imputation constrained by bulk RNA-seq data |
| title_full_unstemmed | SCRABBLE: single-cell RNA-seq imputation constrained by bulk RNA-seq data |
| title_short | SCRABBLE: single-cell RNA-seq imputation constrained by bulk RNA-seq data |
| title_sort | scrabble single cell rna seq imputation constrained by bulk rna seq data |
| topic | Single-cell RNA-seq Imputation Matrix regularization Optimization |
| url | http://link.springer.com/article/10.1186/s13059-019-1681-8 |
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