DCI: learning causal differences between gene regulatory networks
<jats:title>Abstract</jats:title> <jats:sec> <jats:title>Summary</jats:title> <jats:p>Designing interventions to control gene regulation necessitates modeling a gene regulatory network by a causal graph. Curren...
| Main Authors: | , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
Oxford University Press (OUP)
2022
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| Online Access: | https://hdl.handle.net/1721.1/143916 |
| _version_ | 1826201704160296960 |
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| author | Belyaeva, Anastasiya Squires, Chandler Uhler, Caroline |
| author2 | Massachusetts Institute of Technology. Laboratory for Information and Decision Systems |
| author_facet | Massachusetts Institute of Technology. Laboratory for Information and Decision Systems Belyaeva, Anastasiya Squires, Chandler Uhler, Caroline |
| author_sort | Belyaeva, Anastasiya |
| collection | MIT |
| description | <jats:title>Abstract</jats:title>
<jats:sec>
<jats:title>Summary</jats:title>
<jats:p>Designing interventions to control gene regulation necessitates modeling a gene regulatory network by a causal graph. Currently, large-scale gene expression datasets from different conditions, cell types, disease states, and developmental time points are being collected. However, application of classical causal inference algorithms to infer gene regulatory networks based on such data is still challenging, requiring high sample sizes and computational resources. Here, we describe an algorithm that efficiently learns the differences in gene regulatory mechanisms between different conditions. Our difference causal inference (DCI) algorithm infers changes (i.e. edges that appeared, disappeared, or changed weight) between two causal graphs given gene expression data from the two conditions. This algorithm is efficient in its use of samples and computation since it infers the differences between causal graphs directly without estimating each possibly large causal graph separately. We provide a user-friendly Python implementation of DCI and also enable the user to learn the most robust difference causal graph across different tuning parameters via stability selection. Finally, we show how to apply DCI to single-cell RNA-seq data from different conditions and cell states, and we also validate our algorithm by predicting the effects of interventions.</jats:p>
</jats:sec>
<jats:sec>
<jats:title>Availability and implementation</jats:title>
<jats:p>Python package freely available at http://uhlerlab.github.io/causaldag/dci.</jats:p>
</jats:sec>
<jats:sec>
<jats:title>Supplementary information</jats:title>
<jats:p>Supplementary data are available at Bioinformatics online.</jats:p>
</jats:sec> |
| first_indexed | 2024-09-23T11:55:45Z |
| format | Article |
| id | mit-1721.1/143916 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T11:55:45Z |
| publishDate | 2022 |
| publisher | Oxford University Press (OUP) |
| record_format | dspace |
| spelling | mit-1721.1/1439162023-02-06T20:34:25Z DCI: learning causal differences between gene regulatory networks Belyaeva, Anastasiya Squires, Chandler Uhler, Caroline Massachusetts Institute of Technology. Laboratory for Information and Decision Systems Massachusetts Institute of Technology. Institute for Data, Systems, and Society <jats:title>Abstract</jats:title> <jats:sec> <jats:title>Summary</jats:title> <jats:p>Designing interventions to control gene regulation necessitates modeling a gene regulatory network by a causal graph. Currently, large-scale gene expression datasets from different conditions, cell types, disease states, and developmental time points are being collected. However, application of classical causal inference algorithms to infer gene regulatory networks based on such data is still challenging, requiring high sample sizes and computational resources. Here, we describe an algorithm that efficiently learns the differences in gene regulatory mechanisms between different conditions. Our difference causal inference (DCI) algorithm infers changes (i.e. edges that appeared, disappeared, or changed weight) between two causal graphs given gene expression data from the two conditions. This algorithm is efficient in its use of samples and computation since it infers the differences between causal graphs directly without estimating each possibly large causal graph separately. We provide a user-friendly Python implementation of DCI and also enable the user to learn the most robust difference causal graph across different tuning parameters via stability selection. Finally, we show how to apply DCI to single-cell RNA-seq data from different conditions and cell states, and we also validate our algorithm by predicting the effects of interventions.</jats:p> </jats:sec> <jats:sec> <jats:title>Availability and implementation</jats:title> <jats:p>Python package freely available at http://uhlerlab.github.io/causaldag/dci.</jats:p> </jats:sec> <jats:sec> <jats:title>Supplementary information</jats:title> <jats:p>Supplementary data are available at Bioinformatics online.</jats:p> </jats:sec> 2022-07-21T13:43:50Z 2022-07-21T13:43:50Z 2021 2022-07-21T13:35:36Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/143916 Belyaeva, Anastasiya, Squires, Chandler and Uhler, Caroline. 2021. "DCI: learning causal differences between gene regulatory networks." Bioinformatics, 37 (18). en 10.1093/BIOINFORMATICS/BTAB167 Bioinformatics Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Oxford University Press (OUP) bioRxiv |
| spellingShingle | Belyaeva, Anastasiya Squires, Chandler Uhler, Caroline DCI: learning causal differences between gene regulatory networks |
| title | DCI: learning causal differences between gene regulatory networks |
| title_full | DCI: learning causal differences between gene regulatory networks |
| title_fullStr | DCI: learning causal differences between gene regulatory networks |
| title_full_unstemmed | DCI: learning causal differences between gene regulatory networks |
| title_short | DCI: learning causal differences between gene regulatory networks |
| title_sort | dci learning causal differences between gene regulatory networks |
| url | https://hdl.handle.net/1721.1/143916 |
| work_keys_str_mv | AT belyaevaanastasiya dcilearningcausaldifferencesbetweengeneregulatorynetworks AT squireschandler dcilearningcausaldifferencesbetweengeneregulatorynetworks AT uhlercaroline dcilearningcausaldifferencesbetweengeneregulatorynetworks |