Single-cell Transcriptome Study as Big Data
The rapid growth of single-cell RNA-seq studies (scRNA-seq) demands efficient data storage, processing, and analysis. Big-data technology provides a framework that facilitates the comprehensive discovery of biological signals from inter-institutional scRNA-seq datasets. The strategies to solve the s...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Elsevier
2016-02-01
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| Series: | Genomics, Proteomics & Bioinformatics |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1672022916000437 |
| _version_ | 1797329386562650112 |
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| author | Pingjian Yu Wei Lin |
| author_facet | Pingjian Yu Wei Lin |
| author_sort | Pingjian Yu |
| collection | DOAJ |
| description | The rapid growth of single-cell RNA-seq studies (scRNA-seq) demands efficient data storage, processing, and analysis. Big-data technology provides a framework that facilitates the comprehensive discovery of biological signals from inter-institutional scRNA-seq datasets. The strategies to solve the stochastic and heterogeneous single-cell transcriptome signal are discussed in this article. After extensively reviewing the available big-data applications of next-generation sequencing (NGS)-based studies, we propose a workflow that accounts for the unique characteristics of scRNA-seq data and primary objectives of single-cell studies. |
| first_indexed | 2024-03-08T07:03:54Z |
| format | Article |
| id | doaj.art-a418a9f53d2b46acae625cb059040d2a |
| institution | Directory Open Access Journal |
| issn | 1672-0229 |
| language | English |
| last_indexed | 2024-03-08T07:03:54Z |
| publishDate | 2016-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Genomics, Proteomics & Bioinformatics |
| spelling | doaj.art-a418a9f53d2b46acae625cb059040d2a2024-02-03T05:06:36ZengElsevierGenomics, Proteomics & Bioinformatics1672-02292016-02-01141213010.1016/j.gpb.2016.01.005Single-cell Transcriptome Study as Big DataPingjian YuWei LinThe rapid growth of single-cell RNA-seq studies (scRNA-seq) demands efficient data storage, processing, and analysis. Big-data technology provides a framework that facilitates the comprehensive discovery of biological signals from inter-institutional scRNA-seq datasets. The strategies to solve the stochastic and heterogeneous single-cell transcriptome signal are discussed in this article. After extensively reviewing the available big-data applications of next-generation sequencing (NGS)-based studies, we propose a workflow that accounts for the unique characteristics of scRNA-seq data and primary objectives of single-cell studies.http://www.sciencedirect.com/science/article/pii/S1672022916000437Single cellRNA-seqBig dataTranscriptional heterogeneitySignal normalization |
| spellingShingle | Pingjian Yu Wei Lin Single-cell Transcriptome Study as Big Data Genomics, Proteomics & Bioinformatics Single cell RNA-seq Big data Transcriptional heterogeneity Signal normalization |
| title | Single-cell Transcriptome Study as Big Data |
| title_full | Single-cell Transcriptome Study as Big Data |
| title_fullStr | Single-cell Transcriptome Study as Big Data |
| title_full_unstemmed | Single-cell Transcriptome Study as Big Data |
| title_short | Single-cell Transcriptome Study as Big Data |
| title_sort | single cell transcriptome study as big data |
| topic | Single cell RNA-seq Big data Transcriptional heterogeneity Signal normalization |
| url | http://www.sciencedirect.com/science/article/pii/S1672022916000437 |
| work_keys_str_mv | AT pingjianyu singlecelltranscriptomestudyasbigdata AT weilin singlecelltranscriptomestudyasbigdata |