Tempora: Cell trajectory inference using time-series single-cell RNA sequencing data.
Single-cell RNA sequencing (scRNA-seq) can map cell types, states and transitions during dynamic biological processes such as tissue development and regeneration. Many trajectory inference methods have been developed to order cells by their progression through a dynamic process. However, when time s...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2020-09-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://doi.org/10.1371/journal.pcbi.1008205 |
| _version_ | 1818735048416821248 |
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| author | Thinh N Tran Gary D Bader |
| author_facet | Thinh N Tran Gary D Bader |
| author_sort | Thinh N Tran |
| collection | DOAJ |
| description | Single-cell RNA sequencing (scRNA-seq) can map cell types, states and transitions during dynamic biological processes such as tissue development and regeneration. Many trajectory inference methods have been developed to order cells by their progression through a dynamic process. However, when time series data is available, most of these methods do not consider the available time information when ordering cells and are instead designed to work only on a single scRNA-seq data snapshot. We present Tempora, a novel cell trajectory inference method that orders cells using time information from time-series scRNA-seq data. In performance comparison tests, Tempora inferred known developmental lineages from three diverse tissue development time series data sets, beating state of the art methods in accuracy and speed. Tempora works at the level of cell clusters (types) and uses biological pathway information to help identify cell type relationships. This approach increases gene expression signal from single cells, processing speed, and interpretability of the inferred trajectory. Our results demonstrate the utility of a combination of time and pathway information to supervise trajectory inference for scRNA-seq based analysis. |
| first_indexed | 2024-12-18T00:15:04Z |
| format | Article |
| id | doaj.art-5c0833a72c774c9ca90d5865afee0a99 |
| institution | Directory Open Access Journal |
| issn | 1553-734X 1553-7358 |
| language | English |
| last_indexed | 2024-12-18T00:15:04Z |
| publishDate | 2020-09-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj.art-5c0833a72c774c9ca90d5865afee0a992022-12-21T21:27:33ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582020-09-01169e100820510.1371/journal.pcbi.1008205Tempora: Cell trajectory inference using time-series single-cell RNA sequencing data.Thinh N TranGary D BaderSingle-cell RNA sequencing (scRNA-seq) can map cell types, states and transitions during dynamic biological processes such as tissue development and regeneration. Many trajectory inference methods have been developed to order cells by their progression through a dynamic process. However, when time series data is available, most of these methods do not consider the available time information when ordering cells and are instead designed to work only on a single scRNA-seq data snapshot. We present Tempora, a novel cell trajectory inference method that orders cells using time information from time-series scRNA-seq data. In performance comparison tests, Tempora inferred known developmental lineages from three diverse tissue development time series data sets, beating state of the art methods in accuracy and speed. Tempora works at the level of cell clusters (types) and uses biological pathway information to help identify cell type relationships. This approach increases gene expression signal from single cells, processing speed, and interpretability of the inferred trajectory. Our results demonstrate the utility of a combination of time and pathway information to supervise trajectory inference for scRNA-seq based analysis.https://doi.org/10.1371/journal.pcbi.1008205 |
| spellingShingle | Thinh N Tran Gary D Bader Tempora: Cell trajectory inference using time-series single-cell RNA sequencing data. PLoS Computational Biology |
| title | Tempora: Cell trajectory inference using time-series single-cell RNA sequencing data. |
| title_full | Tempora: Cell trajectory inference using time-series single-cell RNA sequencing data. |
| title_fullStr | Tempora: Cell trajectory inference using time-series single-cell RNA sequencing data. |
| title_full_unstemmed | Tempora: Cell trajectory inference using time-series single-cell RNA sequencing data. |
| title_short | Tempora: Cell trajectory inference using time-series single-cell RNA sequencing data. |
| title_sort | tempora cell trajectory inference using time series single cell rna sequencing data |
| url | https://doi.org/10.1371/journal.pcbi.1008205 |
| work_keys_str_mv | AT thinhntran temporacelltrajectoryinferenceusingtimeseriessinglecellrnasequencingdata AT garydbader temporacelltrajectoryinferenceusingtimeseriessinglecellrnasequencingdata |